Models that contain the Model Type : Neuron or other electrically excitable cell

(An electrically excitable cell such as a Neuron, Heart, Muscle, Sensory, or Endocrine cell.)
Re-display model names without descriptions
    Models   Description
1.  Burst and tonic firing behaviour in subfornical organ (SFO) neurons (Medlock et al 2018)
"Subfornical organ (SFO) neurons exhibit heterogeneity in current expression and spiking behavior, where the two major spiking phenotypes appear as tonic and burst firing. Insight into the mechanisms behind this heterogeneity is critical for understanding how the SFO, a sensory circumventricular organ, integrates and selectively influences physiological function. To integrate efficient methods for studying this heterogeneity, we built a single-compartment, Hodgkin-Huxley-type model of an SFO neuron that is parameterized by SFO-specific in vitro patch-clamp data. The model accounts for the membrane potential distribution and spike train variability of both tonic and burst firing SFO neurons. Analysis of model dynamics confirms that a persistent Na+ and Ca2+ currents are required for burst initiation and maintenance and suggests that a slow-activating K+ current may be responsible for burst termination in SFO neurons. Additionally, the model suggests that heterogeneity in current expression and subsequent influence on spike afterpotential underlie the behavioral differences between tonic and burst firing SFO neurons. Future use of this model in coordination with single neuron patch-clamp electrophysiology provides a platform for explaining and predicting the response of SFO neurons to various combinations of circulating signals, thus elucidating the mechanisms underlying physiological signal integration within the SFO."
2.  2 Distinct Classes of L2 and L3 Pyramidal Neurons in Human Temporal Cortex (Deitcher et al 2017)
"There have been few quantitative characterizations of the morphological, biophysical, and cable properties of neurons in the human neocortex. We employed feature-based statistical methods on a rare data set of 60 3D reconstructed pyramidal neurons from L2 and L3 in the human temporal cortex (HL2/L3 PCs) removed after brain surgery. Of these cells, 25 neurons were also characterized physiologically. Thirty-two morphological features were analyzed (e.g., dendritic surface area, 36 333 ± 18 157 µm2; number of basal trees, 5.55 ± 1.47; dendritic diameter, 0.76 ± 0.28 µm). ... A novel descriptor for apical dendritic topology yielded 2 distinct classes, termed hereby as “slim-tufted” and “profuse-tufted” HL2/L3 PCs; the latter class tends to fire at higher rates. Thus, our morpho-electrotonic analysis shows 2 distinct classes of HL2/L3 PCs."
3.  5-neuron-model of neocortex for producing realistic extracellular AP shapes (Van Dijck et al. 2012)
This is a 5-neuron model of neocortex, containing one tufted layer-5 pyramidal cell, two non-tufted pyramidal cells, and two inhibitory interneurons. It was used to reproduce extracellular spike shapes in a study comparing algorithms for spike sorting and electrode selection. The neuron models are adapted from Dyhrfjeld-Johnsen et al. (2005).
4.  A 1000 cell network model for Lateral Amygdala (Kim et al. 2013)
1000 Cell Lateral Amygdala model for investigation of plasticity and memory storage during Pavlovian Conditioning.
5.  A cardiac cell simulator (Puglisi and Bers 2001), applied to the QT interval (Busjahn et al 2004)
"LabHEART is an easy to use program that simulates the cardiac action potential, calcium transient and ionic currents. Key parameters such as ionic concentration, stimulus waveform and channel conductance can easily be changed by a click on an icon or dragging a slider. It is a powerfull tool for teaching and researching cardiac electrophysiology."
6.  A computational model of a small DRG neuron to explore pain (Verma et al. 2019, 2020)
This is a Hodgkin-Huxley type model for a small DRG neuron consisting of four voltage-gated ion channels: sodium channels 1.7 and 1.8, delayed rectifier potassium, and A-type transient potassium channels. This model was used to explore the dynamics of this neuron using bifurcation theory, with the motive to investigate pain since small DRG neuron is a pain-sensing neuron.
7.  A CORF computational model of a simple cell that relies on LGN input (Azzopardi & Petkov 2012)
"... We propose a computational model that uses as afferent inputs the responses of model LGN cells with center-surround receptive fields (RFs) and we refer to it as a Combination of Receptive Fields (CORF) model. We use shifted gratings as test stimuli and simulated reverse correlation to explore the nature of the proposed model. We study its behavior regarding the effect of contrast on its response and orientation bandwidth as well as the effect of an orthogonal mask on the response to an optimally oriented stimulus. We also evaluate and compare the performances of the CORF and GF (Gabor Filter) models regarding contour detection, using two public data sets of images of natural scenes with associated contour ground truths. ... The proposed CORF model is more realistic than the GF model and is more effective in contour detection, which is assumed to be the primary biological role of simple cells."
8.  A dendritic disinhibitory circuit mechanism for pathway-specific gating (Yang et al. 2016)
"While reading a book in a noisy café, how does your brain ‘gate in’ visual information while filtering out auditory stimuli? Here we propose a mechanism for such flexible routing of information flow in a complex brain network (pathway-specific gating), tested using a network model of pyramidal neurons and three classes of interneurons with connection probabilities constrained by data. We find that if inputs from different pathways cluster on a pyramidal neuron dendrite, a pathway can be gated-on by a disinhibitory circuit motif. ..."
9.  A detailed Purkinje cell model (Masoli et al 2015)
The Purkinje cell is one of the most complex type of neuron in the central nervous system and is well known for its massive dendritic tree. The initiation of the action potential was theorized to be due to the high calcium channels presence in the dendritic tree but, in the last years, this idea was revised. In fact, the Axon Initial Segment, the first section of the axon was seen to be critical for the spontaneous generation of action potentials. The model reproduces the behaviours linked to the presence of this fundamental sections and the interplay with the other parts of the neuron.
10.  A dynamic model of the canine ventricular myocyte (Hund, Rudy 2004)
The Hund-Rudy dynamic (HRd) model is based on data from the canine epicardial ventricular myocyte. Rate-dependent phenomena associated with ion channel kinetics, action potential properties and Ca2+ handling are simulated by the model. See paper for more and details.
11.  A Fast Rhythmic Bursting Cell: in vivo cell modeling (Lee 2007)
One of the cellular mechanisms underlying the generation of gamma oscillations is a type of cortical pyramidal neuron named fast rhythmic bursting (FRB) cells. After cells from cats' primary visual cortices were filled with Neurobiotin, the brains were cut, and the cells were photographed. One FRB cell was chosen to be confocaled, reconstructed with Neurolucida software, and generated a detailed multi-compartmental model in the NEURON program. We explore firing properties of FRB cells and the role of enhanced Na+ conductance.
12.  A finite volume method for stochastic integrate-and-fire models (Marpeau et al. 2009)
"The stochastic integrate and fire neuron is one of the most commonly used stochastic models in neuroscience. Although some cases are analytically tractable, a full analysis typically calls for numerical simulations. We present a fast and accurate finite volume method to approximate the solution of the associated Fokker-Planck equation. ..."
13.  A four compartmental model for ABPD complex in crustacean pyloric network (Maran et al. 2011)
"Central pattern generators (CPGs) frequently include bursting neurons that serve as pacemakers for rhythm generation. Phase resetting curves (PRCs) can provide insight into mechanisms underlying phase locking in such circuits. PRCs were constructed for a pacemaker bursting complex in the pyloric circuit in the stomatogastric ganglion of the lobster and crab. ..."
14.  A gap junction network of Amacrine Cells controls Nitric Oxide release (Jacoby et al 2018)
"... The effects of the neuromodulator nitric oxide (NO) have been studied in many circuits, including in the vertebrate retina, where it regulates synaptic release, gap junction coupling, and blood vessel dilation, but little is known about the cells that release NO. We show that a single type of amacrine cell (AC) controls NO release in the inner retina, and we report its light responses, electrical properties, and calcium dynamics. We discover that this AC forms a dense gap junction network and that the strength of electrical coupling in the network is regulated by light through NO. A model of the network offers insights into the biophysical specializations leading to auto-regulation of NO release within the network."
15.  A model for how correlation depends on the neuronal excitability type (Hong et al. 2012)
“ … Using simulations and experiments in rat hippocampal neurons, we show here that pairs of neurons receiving correlated input also exhibit correlations arising from precise spike-time synchronization. Contrary to rate comodulation, spike-time synchronization is unaffected by firing rate, thus enabling synchrony- and rate-based coding to operate independently. The type of output correlation depends on whether intrinsic neuron properties promote integration or coincidence detection: “ideal” integrators (with spike generation sensitive to stimulus mean) exhibit rate comodulation, whereas ideal coincidence detectors (with spike generation sensitive to stimulus variance) exhibit precise spike-time synchronization. … Our results explain how different types of correlations arise based on how individual neurons generate spikes, and why spike-time synchronization and rate comodulation can encode different stimulus properties. Our results also highlight the importance of neuronal properties for population-level coding insofar as neural networks can employ different coding schemes depending on the dominant operating mode of their constituent neurons. “
16.  A model for interaural time difference sensitivity in the medial superior olive (Zhou et al 2005)
This model simulates responses of neurons to interaural time difference (ITD) in the medial superior olive (MSO) of the mammalian brainstem. The model has a bipolar cell structure and incorporates two anatomic observations in the MSO: (1) the axon arises from the dendrite that receives ipsilateral inputs and (2) inhibitory synapses are located primarily on the soma in adult animals. Fine adjustment of the best ITD is achieved by the interplay of somatic sodium currents and synaptic inhibitory currents. The model suggests a mechanism for dynamically "fine-tuning" the ITD sensitivity of MSO cells by the opponency between depolarizing sodium currents and hyperpolarizing inhibitory currents.
17.  A model for pituitary GH(3) lactotroph (Wu and Chang 2005)
The ATP-sensitive K(+) (K(ATP)) channels are composed of sulfonylurea receptor and inwardly rectifying K(+) channel (Kir6.2) subunit. These channels are regulated by intracellular ADP/ATP ratio and play a role in cellular metabolism. ... The objective of this study was to determine whether Diethyl pyrocarbonate (DEPC) modifies K(ATP)-channel activity in pituitary GH(3) cells. ... Simulation studies also demonstrated that the increased conductance of K(ATP)-channels used to mimic DEPC actions reduced the frequency of spontaneous action potentials and fluctuation of intracellular Ca(2+). The results indicate that chemical modification with DEPC enhances K(ATP)-channel activity and influences functional activities of pituitary GH(3) cells. See paper for more and details.
18.  A model for recurrent spreading depolarizations (Conte et al. 2017)
A detailed biophysical model for a neuron/astrocyte network is developed in order to explore mechanisms responsible for cortical spreading depolarizations. This includes a model for the Na+-glutamate transporter, which allows for a detailed description of reverse glutamate uptake. In particular, we consider the specific roles of elevated extracellular glutamate and K+ in the initiation, propagation and recurrence of spreading depolarizations.
19.  A model of local field potentials generated by medial superior olive neurons (Goldwyn et al 2014)
A computational model of local field potentials generated by medial superior olive neurons. These field potentials are known as the "auditory neurophonic". MSO neuron is modeled as a soma and two dendrites (following Mathews et al, Nature Neurosci, 2010). Intracellular and a 1D extracellular domain are dynamically coupled and solved to simulate spatial-temporal patterns of membrane voltage and extracellular voltage in response to trains of synaptic inputs (monolateral or bilateral, excitation and/or inhibition). The model produces spatio-temporal patterns similar to neurophonic responses recorded in vivo, as discussed in the accompanying manuscript.
20.  A Model of Multiple Spike Initiation Zones in the Leech C-interneuron (Crisp 2009)
The leech C-interneuron and its electrical synapse with the S-interneuron exhibit unusual properties: an asymmetric delay when impulses travel from one soma to the other, and graded C-interneuron impulse amplitudes under elevated divalent cation concentrations. These properties have been simulated using a SNNAP model in which the C-interneuron has multiple, independent spike initiation zones associated with individual electrical junctions with the C-interneuron.
21.  A model of optimal learning with redundant synaptic connections (Hiratani & Fukai 2018)
This is a detailed neuron model of non-parametric near-optimal latent model acquisition using multisynaptic connections between pre- and postsynaptic neurons.
22.  A model of slow motor unit (Kim, 2017)
Cav1.3 channels in motoneuron dendrites are actively involved during normal motor activities. To investigate the effects of the activation of motoneuron Cav1.3 channels on force production, a model motor unit was built based on best-available data. The simulation results suggest that force potentiation induced by Cav1.3 channel activation is strongly modulated not only by firing history of the motoneuron but also by length variation of the muscle as well as neuromodulation inputs from the brainstem.
23.  A model of the femur-tibia control system in stick insects (Stein et al. 2008)
We studied the femur-tibia joint control system of the insect leg, and its switch between resistance reflex in posture control and "active reaction" in walking. The "active reaction" is basically a reversal of the resistance reflex. Both responses are elicited by the same sensory input and the same neuronal network (the femur-tibia network). The femur-tibia network was modeled by fitting the responses of model neurons to those obtained in animals. Each implemented neuron has a physiological counterpart. The strengths of 16 interneuronal pathways that integrate sensory input were then assigned three different values and varied independently, generating a database of more than 43 million network variants. The uploaded version contains the model that best represented the resistance reflex. Please see the README for more help. We demonstrate that the combinatorial code of interneuronal pathways determines motor output. A switch between different behaviors such as standing to walking can thus be achieved by altering the strengths of selected sensory integration pathways.
24.  A multi-compartment model for interneurons in the dLGN (Halnes et al. 2011)
This model for dLGN interneurons is presented in two parameterizations (P1 & P2), which were fitted to current-clamp data from two different interneurons (IN1 & IN2). The model qualitatively reproduces the responses in IN1 & IN2 under 8 different experimental condition, and quantitatively reproduces the I/O-relations (#spikes elicited as a function of injected current).
25.  A multiphysics neuron model for cellular volume dynamics (Lee et al. 2011)
This paper introduces a novel neuron model, where the cell volume is a time-varying variable and multiple physical principles are combined to build governing equations. Using this model, we analyzed neuronal volume responses during excitation, which elucidated the waveforms of fast intrinsic optical signals observed experimentally across the literature. In addition, we analyzed volume responses on a longer time scale with repetitive stimulation to study the characteristics of slow cell swelling.
26.  A multiscale approach to analyze circadian rhythms (Vasalou & Henson, 2010) (CellML)
" ... We developed a firing rate code model to incorporate known electrophysiological properties of SCN (suprachiasmatic nucleus) pacemaker cells, including circadian dependent changes in membrane voltage and ion conductances. Calcium dynamics were included in the model as the putative link between electrical firing and gene expression. Individual ion currents exhibited oscillatory patterns matching experimental data both in current levels and phase relationships. VIP and GABA neurotransmitters, which encode synaptic signals across the SCN, were found to play critical roles in daily oscillations of membrane excitability and gene expression. Blocking various mechanisms of intracellular calcium accumulation by simulated pharmacological agents (nimodipine, IP3- and ryanodine-blockers) reproduced experimentally observed trends in firing rate dynamics and core-clock gene transcription. The intracellular calcium concentration was shown to regulate diverse circadian processes such as firing frequency, gene expression and system periodicity. The model predicted a direct relationship between firing frequency and gene expression amplitudes, demonstrated the importance of intracellular pathways for single cell behavior and provided a novel multiscale framework which captured characteristics of the SCN at both the electrophysiological and gene regulatory levels."
27.  A multiscale approach to analyze circadian rhythms (Vasalou & Henson, 2010) (SBML)
" ... We developed a firing rate code model to incorporate known electrophysiological properties of SCN (suprachiasmatic nucleus) pacemaker cells, including circadian dependent changes in membrane voltage and ion conductances. Calcium dynamics were included in the model as the putative link between electrical firing and gene expression. Individual ion currents exhibited oscillatory patterns matching experimental data both in current levels and phase relationships. VIP and GABA neurotransmitters, which encode synaptic signals across the SCN, were found to play critical roles in daily oscillations of membrane excitability and gene expression. Blocking various mechanisms of intracellular calcium accumulation by simulated pharmacological agents (nimodipine, IP3- and ryanodine-blockers) reproduced experimentally observed trends in firing rate dynamics and core-clock gene transcription. The intracellular calcium concentration was shown to regulate diverse circadian processes such as firing frequency, gene expression and system periodicity. The model predicted a direct relationship between firing frequency and gene expression amplitudes, demonstrated the importance of intracellular pathways for single cell behavior and provided a novel multiscale framework which captured characteristics of the SCN at both the electrophysiological and gene regulatory levels."
28.  A neuronal circuit simulator for non Monte Carlo analysis of neuronal noise (Kilinc & Demir 2018)
cirsiumNeuron is a neuronal circuit simulator that can directly and efficiently compute characterizations of stochastic behavior, i.e., noise, for multi-neuron circuits. In cirsiumNeuron, we utilize a general modeling framework for biological neuronal circuits which systematically captures the nonstationary stochastic behavior of the ion channels and the synaptic processes. In this framework, we employ fine-grained, discrete-state, continuous-time Markov Chain (MC) models of both ion channels and synaptic processes in a unified manner. Our modeling framework can automatically generate the corresponding coarse-grained, continuous-state, continuous-time Stochastic Differential Equation (SDE) models. In addition, for the stochastic characterization of neuronal variability and noise, we have implemented semi-analytical, non Monte Carlo analysis techniques that work both in time and frequency domains, which were previously developed for analog electronic circuits. In these semi-analytical noise evaluation schemes, (differential) equations that directly govern probabilistic characterizations in the form of correlation functions (time domain) or spectral densities (frequency domain) are first derived analytically, and then solved numerically. These semi-analytical noise analysis techniques correctly and accurately capture the second order statistics (mean, variance, autocorrelation, and power spectral density) of the underlying neuronal processes as compared with Monte Carlo simulations.
29.  A set of reduced models of layer 5 pyramidal neurons (Bahl et al. 2012)
These are the NEURON files for 10 different models of a reduced L5 pyramidal neuron. The parameters were obtained by automatically fitting the models to experimental data using a multi objective evolutionary search strategy. Details on the algorithm can be found at and in Bahl et al. (2012).
30.  A simple integrative electrophysiological model of bursting GnRH neurons (Csercsik et al. 2011)
In this paper a modular model of the GnRH neuron is presented. For the aim of simplicity, the currents corresponding to fast time scales and action potential generation are described by an impulsive system, while the slower currents and calcium dynamics are described by usual ordinary differential equations (ODEs). The model is able to reproduce the depolarizing afterpotentials, afterhyperpolarization, periodic bursting behavior and the corresponding calcium transients observed in the case of GnRH neurons.
31.  A simplified cerebellar Purkinje neuron (the PPR model) (Brown et al. 2011)
These models were implemented in NEURON by Sherry-Ann Brown in the laboratory of Leslie M. Loew. The files reproduce Figures 2c-f from Brown et al, 2011 "Virtual NEURON: a Strategy For Merged Biochemical and Electrophysiological Modeling".
32.  A simplified model of NMDA oscillations in lamprey locomotor neurons (Huss et al. 2008)
Using experiments in conjunction with this simplified model, we sought to understand the basic mechanisms behind NMDA-induced oscillations in lamprey locomotor neurons, specifically (a) how the oscillation frequency depends on NMDA concentration and why, and (b) what the minimal number of components for generating NMDA oscillations is (in vitro and in the model).
33.  A spiking model of cortical broadcast and competition (Shanahan 2008)
"This paper presents a computer model of cortical broadcast and competition based on spiking neurons and inspired by the hypothesis of a global neuronal workspace underlying conscious information processing in the human brain. In the model, the hypothesised workspace is realised by a collection of recurrently interconnected regions capable of sustaining and disseminating a reverberating spatial pattern of activation. ..."
34.  A threshold equation for action potential initiation (Platkiewicz & Brette 2010)
"We examined in models the influence of Na channel activation, inactivation, slow voltage-gated channels and synaptic conductances on spike threshold. We propose a threshold equation which quantifies the contribution of all these mechanisms. It provides an instantaneous time-varying value of the threshold, which applies to neurons with fluctuating inputs. ... We find that spike threshold depends logarithmically on Na channel density, and that Na channel inactivation and K channels can dynamically modulate it in an adaptive way: the threshold increases with membrane potential and after every action potential. " See paper for more.
35.  A two-stage model of dendritic integration in CA1 pyramidal neurons (Katz et al. 2009)
"... In a two-stage integration model, inputs contribute directly to dendritic spikes, and outputs from multiple branches sum in the axon. ... We used serial-section electron microscopy to reconstruct individual apical oblique dendritic branches of CA1 pyramidal neurons and observe a synapse distribution consistent with the two-stage integration model. Computational modeling suggests that the observed synapse distribution enhances the contribution of each dendritic branch to neuronal output."
36.  Accurate and fast simulation of channel noise in conductance-based model neurons (Linaro et al 2011)
We introduce and operatively present a general method to simulate channel noise in conductance-based model neurons, with modest computational overheads. Our approach may be considered as an accurate generalization of previous proposal methods, to the case of voltage-, ion-, and ligand-gated channels with arbitrary complexity. We focus on the discrete Markov process descriptions, routinely employed in experimental identification of voltage-gated channels and synaptic receptors.
37.  Action Potential initiation and backpropagation in Neocortical L5 Pyramidal Neuron (Hu et al. 2009)
"...Previous computational studies have yielded conflicting conclusions about the role of Na+ channel density and biophysical properties in action potential initiation as a result of inconsistent estimates of channel density. Our modeling studies integrated the immunostaining and electrophysiological results and showed that the lowest threshold for action potential initiation at the distal AIS was largely determined by the density of low-threshold Nav1.6 channels ... Distinct from the function of Nav1.6 channel, the Nav1.2 channel may control action potential backpropagation because of its high density at the proximal AIS and high threshold. ... In conclusion, distal AIS accumulation of Nav1.6 channels determines the low threshold for action potential initiation; whereas proximal AIS accumulation of Nav1.2 channels sets the threshold for the generation of somatodendritic potentials and ensures action potential backpropagation to the soma and dendrites. Thus, Nav1.6 and Nav1.2 channels serve distinct functions in action potential initiation and backpropagation."
38.  Action potential initiation in the olfactory mitral cell (Shen et al 1999)
Mitral cell model with standard parameters for the paper: Shen, G.Y., Chen, W. R., Midtgaard, J., Shepherd, G.M., and Hines, M.L. (1999) Computational Analysis of Action Potential Initiation in Mitral Cell Soma and Dendrites Based on Dual Patch Recordings. Journal of Neurophysiology 82:3006. Contact if you have any questions about the implementation of the model.
39.  Action potential of adult rat ventricle (Wang et al. 2008)
"Aconitine (ACO), a highly toxic diterpenoid alkaloid, is recognized to have effects on cardiac voltage-gated Na(+) channels. However, it remains unknown whether it has any effects on K(+) currents. The effects of ACO on ion currents in differentiated clonal cardiac (H9c2) cells and in cultured neonatal rat ventricular myocytes were investigated in this study. ..." The rat action potential in this simulation was played back into the cell for experiments reported in this paper.
40.  Action potential of mouse urinary bladder smooth muscle (Mahapatra et al 2018)
Urinary incontinence is associated with enhanced spontaneous phasic contractions of the detrusor smooth muscle (DSM). Although a complete understanding of the etiology of these spontaneous contractions is not yet established, it is suggested that the spontaneously evoked action potentials (sAPs) in DSM cells initiate and modulate the contractions. In order to further our understanding of the ionic mechanisms underlying sAP generation, we present here a biophysically detailed computational model of a single DSM cell. First, we constructed mathematical models for nine ion channels found in DSM cells based on published experimental data: two voltage-gated Ca2+ ion channels, an hyperpolarization-activated ion channel, two voltage-gated K+ ion channels, three Ca2+-activated K+ ion channels and a non-specific background leak ion channel. Incorporating these channels, our DSM model is capable of reproducing experimentally recorded spike-type sAPs of varying configurations, ranging from sAPs displaying after-hyperpolarizations to sAPs displaying after-depolarizations. Our model, constrained heavily by physiological data, provides a powerful tool to investigate the ionic mechanisms underlying the genesis of DSM electrical activity, which can further shed light on certain aspects of urinary bladder function and dysfunction.
41.  Action potential of striated muscle fiber (Adrian et al 1970)
1. Membrane currents during step depolarizations were determined by a method in which three electrodes were inserted near the end of a fibre in the frog's sartorius muscle. The theoretical basis and limitations of the method are discussed. 2. Measurements of the membrane capacity (CM) and resting resistance (RM) derived from the current during a step change in membrane potential are consistent with values found by other methods. 3. In fibres made mechanically inactive with hypertonic solutions (Ringer solution plus 350 mM sucrose) step depolarizations produced ionic currents which resembled those of nerve in showing (a) an early transient inward current, abolished by tetrodotoxin, which reversed when the depolarization was carried beyond an internal potential of about +20 mV, (b) a delayed outward current, with a linear instantaneous current¡Xvoltage relation, and a mean equilibrium potential with a normal potassium concentration (2¡P5 mM) of -85 mV. 4. The reversal potential for the early current appears to be consistent with the sodium equilibrium potential expected in hypertonic solutions. 5. The variation of the equilibrium potential for the delayed current (V¡¬K) with external potassium concentration suggests that the channel for delayed current has a ratio of potassium to sodium permeability of 30:1; this is less than the resting membrane where the ratio appears to be 100:1. V¡¬K corresponds well with the membrane potential at the beginning of the negative after-potential observed under similar conditions. 6. The variation of V¡¬K with the amount of current which has passed through the delayed channel suggests that potassium ions accumulate in a space of between 1/3 and 1/6 of the fibre volume. If potassium accumulates in the transverse tubular system (T system) much greater variation in V¡¬K would be expected. 7. The delayed current is not maintained but is inactivated like the early current. The inactivation is approximately exponential with a time constant of 0¡P5 to 1 sec at 20¢X C. The steady-state inactivation of the potassium current is similar to that for the sodium current, but its voltage dependence is less steep and the potential for half inactivation is 20 mV rate more positive. 8. Reconstructions of ionic currents were made in terms of the parameters (m, n, h) of the Hodgkin¡XHuxley model for the squid axon, using constants which showed a similar dependence on voltage. 9. Propagated action potentials and conduction velocities were computed for various conditions on the assumption that the T system behaves as if it were a series resistance and capacity in parallel with surface capacity and the channels for sodium, potassium and leak current. There was reasonable agreement with observed values, the main difference being that the calculated velocities and rates of rise were somewhat less than those observed experimentally.
42.  Action potential reconstitution from measured current waveforms (Alle et al. 2009)
This NEURON code reconstitutes action potentials in a model of a hippocampal mossy fiber from experimentally measured sodium, potassium and calcium current waveforms as described in Alle et al. (2009).
43.  Action potential-evoked Na+ influx are similar in axon and soma (Fleidervish et al. 2010)
"In cortical pyramidal neurons, the axon initial segment (AIS) is pivotal in synaptic integration. It has been asserted that this is because there is a high density of Na+ channels in the AIS. However, we found that action potential-associated Na+ flux, as measured by high-speed fluorescence Na+ imaging, was about threefold larger in the rat AIS than in the soma. Spike-evoked Na+ flux in the AIS and the first node of Ranvier was similar and was eightfold lower in basal dendrites. ... In computer simulations, these data were consistent with the known features of action potential generation in these neurons."
44.  Action potential-evoked Na+ influx similar in axon and soma (Fleidervish et al. 2010) (Python)
"In cortical pyramidal neurons, the axon initial segment (AIS) is pivotal in synaptic integration. It has been asserted that this is because there is a high density of Na+ channels in the AIS. However, we found that action potential-associated Na+ flux, as measured by high-speed fluorescence Na+ imaging, was about threefold larger in the rat AIS than in the soma. Spike-evoked Na+ flux in the AIS and the first node of Ranvier was similar and was eightfold lower in basal dendrites. ... In computer simulations, these data were consistent with the known features of action potential generation in these neurons."
45.  Actions of Rotenone on ionic currents and MEPPs in Mouse Hippocampal Neurons (Huang et al 2018)
" ... With the aid of patch-clamp technology and simulation modeling, the effects of (Rotenone) Rot on membrane ion currents present in mHippoE-14 cells were investigated. Results: Addition of Rot produced an inhibitory action on the peak amplitude of INa ...; however, neither activation nor inactivation kinetics of INa was changed during cell exposure to this compound. Addition of Rot produced little or no modifications in the steady-state inactivation curve of INa. Rot increased the amplitude of Ca2+-activated Cl- current in response to membrane depolarization ... . Moreover, when these cells were exposed to 10 µM Rot, a specific population of ATP-sensitive K+ channels ... was measured, despite its inability to alter single-channel conductance. Under current clamp condition, the frequency of miniature end-plate potentials in mHippoE-14 cells was significantly raised in the presence of Rot (10 µM) with no changes in their amplitude and time course of rise and decay. In simulated model of hippocampal neurons incorporated with chemical autaptic connection, increased autaptic strength to mimic the action of Rot was noted to change the bursting pattern with emergence of subthreshold potentials. Conclusions: The Rot effects presented herein might exert a significant action on functional activities of hippocampal neurons occurring in vivo. "
46.  Active dendrites and spike propagation in a hippocampal interneuron (Saraga et al 2003)
We create multi-compartment models of an Oriens-Lacunosum/Moleculare (O-LM) hippocampal interneuron using passive properties, channel kinetics, densities and distributions specific to this cell type, and explore its signaling characteristics. We find that spike initiation depends on both location and amount of input, as well as the intrinsic properties of the interneuron. Distal synaptic input always produces strong back-propagating spikes whereas proximal input could produce both forward and back-propagating spikes depending on the input strength. Please see paper for more details.
47.  Active dendrites shape signaling microdomains in hippocampal neurons (Basak & Narayanan 2018)
The spatiotemporal spread of biochemical signals in neurons and other cells regulate signaling specificity, tuning of signal propagation, along with specificity and clustering of adaptive plasticity. Theoretical and experimental studies have demonstrated a critical role for cellular morphology and the topology of signaling networks in regulating this spread. In this study, we add a significantly complex dimension to this narrative by demonstrating that voltage-gated ion channels (A-type Potassium channels and T-type Calcium channels) on the plasma membrane could actively amplify or suppress the strength and spread of downstream signaling components. We employed a multiscale, multicompartmental, morphologically realistic, conductance-based model that accounted for the biophysics of electrical signaling and the biochemistry of calcium handling and downstream enzymatic signaling in a hippocampal pyramidal neuron. We chose the calcium – calmodulin – calcium/calmodulin-dependent protein kinase II (CaMKII) – protein phosphatase 1 (PP1) signaling pathway owing to its critical importance to several forms of neuronal plasticity, and employed physiologically relevant theta-burst stimulation (TBS) or theta-burst pairing (TBP) protocol to initiate a calcium microdomain through NMDAR activation at a synapse.
48.  Active dendritic action potential propagation (Casale & McCormick 2011)
This model explores the dendritic sodium and potassium conductances needed to recapitulate voltage-sensitive dye optical recordings of thalamic interneuron dendrites in the dorsal lateral geniculate nucleus. Model ion channels were selected based on pharmacological data.
49.  Activity constraints on stable neuronal or network parameters (Olypher and Calabrese 2007)
"In this study, we developed a general description of parameter combinations for which specified characteristics of neuronal or network activity are constant. Our approach is based on the implicit function theorem and is applicable to activity characteristics that smoothly depend on parameters. Such smoothness is often intrinsic to neuronal systems when they are in stable functional states. The conclusions about how parameters compensate each other, developed in this study, can thus be used even without regard to the specific mathematical model describing a particular neuron or neuronal network. ..."
50.  Activity dependent changes in motoneurones (Dai Y et al 2002, Gardiner et al 2002)
These two papers review various experimental papers and examine the effects of activity on motoneurons in a similar 5 compartment model with 10 active conductances. Included are slow (S) and fast (F) type and fast fatigue resistant (FR) and fast fatigable (FF) models corresponding to the types of motoneurons. See papers for more and details.
51.  Activity dependent conductances in a neuron model (Liu et al. 1998)
"... We present a model of a stomatogastric ganglion (STG) neuron in which several Ca2+-dependent pathways are used to regulate the maximal conductances of membrane currents in an activity-dependent manner. Unlike previous models of this type, the regulation and modification of maximal conductances by electrical activity is unconstrained. The model has seven voltage-dependent membrane currents and uses three Ca2+ sensors acting on different time scales. ... The model suggests that neurons may regulate their conductances to maintain fixed patterns of electrical activity, rather than fixed maximal conductances, and that the regulation process requires feedback systems capable of reacting to changes of electrical activity on a number of different time scales."
52.  Activity dependent regulation of pacemaker channels by cAMP (Wang et al 2002)
Demonstration of the physiological consequences of the cyclic allosteric gating scheme for Ih mediated by HCN2 in thalamocortical relay cells.
53.  Adaptive exponential integrate-and-fire model (Brette & Gerstner 2005)
"We introduce a two-dimensional integrate-and-fire model that combines an exponential spike mechanism with an adaptation equation, based on recent theoretical findings. ... The model is especially reliable in high-conductance states, typical of cortical activity in vivo, in which intrinsic conductances were found to have a reduced role in shaping spike trains. These results are promising because this simple model has enough expressive power to reproduce qualitatively several electrophysiological classes described in vitro."
54.  Afferent Integration in the NAcb MSP Cell (Wolf et al. 2005)
"We describe a computational model of the principal cell in the nucleus accumbens (NAcb), the medium spiny projection (MSP) neuron. The model neuron, constructed in NEURON, includes all of the known ionic currents in these cells and receives synaptic input from simulated spike trains via NMDA, AMPA, and GABAA receptors. ... results suggest that afferent information integration by the NAcb MSP cell may be compromised by pathology in which the NMDA current is altered or modulated, as has been proposed in both schizophrenia and addiction."
55.  Alcohol action in a detailed Purkinje neuron model and an efficient simplified model (Forrest 2015)
" ... we employ a novel reduction algorithm to produce a 2 compartment model of the cerebellar Purkinje neuron from a previously published, 1089 compartment model. It runs more than 400 times faster and retains the electrical behavior of the full model. So, it is more suitable for inclusion in large network models, where computational power is a limiting issue. We show the utility of this reduced model by demonstrating that it can replicate the full model’s response to alcohol, which can in turn reproduce experimental recordings from Purkinje neurons following alcohol application. ..."
56.  Alcohol excites Cerebellar Golgi Cells by inhibiting the Na+/K+ ATPase (Botta et al.2010)
Patch-clamp in cerebellar slices and computer modeling show that ethanol excites Golgi cells by inhibiting the Na+/K+ ATPase. In particular, voltage-clamp recordings of Na+/K+ ATPase currents indicated that ethanol partially inhibits this pump and this effect could be mimicked by low concentrations of the Na+/K+ ATPase blocker ouabain. The partial inhibition of Na+/K+ ATPase in a computer model of the Golgi cell reproduced these experimental findings that established a novel mechanism of action of ethanol on neural excitability.
57.  Allen Institute: Gad2-IRES-Cre VISp layer 5 472447460
This is an Allen Cell Types Database model of a Gad2-IRES-Cre neuron from layer 5 of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
58.  Allen Institute: Gad2-IRES-Cre VISp layer 5 473561729
This is an Allen Cell Types Database model of a Gad2-IRES-Cre neuron from layer 5 of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
59.  Allen Institute: Htr3a-Cre VISp layer 2/3 472352327
This is an Allen Cell Types Database model of a Htr3a-Cre neuron from layer 2/3 of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
60.  Allen Institute: Htr3a-Cre VISp layer 2/3 472421285
This is an Allen Cell Types Database model of a Htr3a-Cre neuron from layer 2/3 of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
61.  Allen Institute: Nr5a1-Cre VISp layer 2/3 473862496
This is an Allen Cell Types Database model of a Nr5a1-Cre neuron from layer 2/3 of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
62.  Allen Institute: Nr5a1-Cre VISp layer 4 329322394
This is an Allen Cell Types Database model of a Nr5a1-Cre neuron from layer 4 of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
63.  Allen Institute: Nr5a1-Cre VISp layer 4 472306544
This is an Allen Cell Types Database model of a Nr5a1-Cre neuron from layer 4 of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
64.  Allen Institute: Nr5a1-Cre VISp layer 4 472442377
This is an Allen Cell Types Database model of a Nr5a1-Cre neuron from layer 4 of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
65.  Allen Institute: Nr5a1-Cre VISp layer 4 472451419
This is an Allen Cell Types Database model of a Nr5a1-Cre neuron from layer 4 of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
66.  Allen Institute: Nr5a1-Cre VISp layer 4 472915634
This is an Allen Cell Types Database model of a Nr5a1-Cre neuron from layer 4 of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
67.  Allen Institute: Nr5a1-Cre VISp layer 4 473834758
This is an Allen Cell Types Database model of a Nr5a1-Cre neuron from layer 4 of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
68.  Allen Institute: Nr5a1-Cre VISp layer 4 473863035
This is an Allen Cell Types Database model of a Nr5a1-Cre neuron from layer 4 of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
69.  Allen Institute: Nr5a1-Cre VISp layer 4 473871429
This is an Allen Cell Types Database model of a Nr5a1-Cre neuron from layer 4 of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
70.  Allen Institute: Ntsr1-Cre VISp layer 4 472430904
This is an Allen Cell Types Database model of a Ntsr1-Cre neuron from layer 6a of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
71.  Allen Institute: Pvalb-IRES-Cre VISp layer 2/3 472306616
This is an Allen Cell Types Database model of a Pvalb-IRES-Cre neuron from layer 2/3 of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
72.  Allen Institute: Pvalb-IRES-Cre VISp layer 5 471085845
This is an Allen Cell Types Database model of a Pvalb-IRES-Cre neuron from layer 5 of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
73.  Allen Institute: Pvalb-IRES-Cre VISp layer 5 472349114
This is an Allen Cell Types Database model of a Pvalb-IRES-Cre neuron from layer 5 of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
74.  Allen Institute: Pvalb-IRES-Cre VISp layer 5 472912177
This is an Allen Cell Types Database model of a Pvalb-IRES-Cre neuron from layer 5 of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
75.  Allen Institute: Pvalb-IRES-Cre VISp layer 5 473465774
This is an Allen Cell Types Database model of a Pvalb-IRES-Cre neuron from layer 5 of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
76.  Allen Institute: Pvalb-IRES-Cre VISp layer 5 473862421
This is an Allen Cell Types Database model of a Pvalb-IRES-Cre neuron from layer 5 of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
77.  Allen Institute: Pvalb-IRES-Cre VISp layer 6a 471081668
This is an Allen Cell Types Database model of a Pvalb-IRES-Cre neuron from layer 6a of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
78.  Allen Institute: Pvalb-IRES-Cre VISp layer 6a 472301074
This is an Allen Cell Types Database model of a Pvalb-IRES-Cre neuron from layer 6a of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
79.  Allen Institute: Pvalb-IRES-Cre VISp layer 6a 473860269
This is an Allen Cell Types Database model of a Pvalb-IRES-Cre neuron from layer 6a of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
80.  Allen Institute: Rbp4-Cre VISp layer 5 472424854
This is an Allen Cell Types Database model of a Rbp4-Cre neuron from layer 5 of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
81.  Allen Institute: Rbp4-Cre VISp layer 6a 473871592
This is an Allen Cell Types Database model of a Rbp4-Cre neuron from layer 6a of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
82.  Allen Institute: Rorb-IRES2-Cre-D VISp layer 2/3 472299294
This is an Allen Cell Types Database model of a Rorb-IRES2-Cre-D neuron from layer 2/3 of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
83.  Allen Institute: Rorb-IRES2-Cre-D VISp layer 2/3 472434498
This is an Allen Cell Types Database model of a Rorb-IRES2-Cre-D neuron from layer 2/3 of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
84.  Allen Institute: Rorb-IRES2-Cre-D VISp layer 4 473863510
This is an Allen Cell Types Database model of a Rorb-IRES2-Cre-D neuron from layer 4 of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
85.  Allen Institute: Rorb-IRES2-Cre-D VISp layer 5 471087975
This is an Allen Cell Types Database model of a Rorb-IRES2-Cre-D neuron from layer 5 of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
86.  Allen Institute: Rorb-IRES2-Cre-D VISp layer 5 473561660
This is an Allen Cell Types Database model of a Rorb-IRES2-Cre-D neuron from layer 5 of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
87.  Allen Institute: Scnn1a-Tg2-Cre VISp layer 4 472300877
This is an Allen Cell Types Database model of a Scnn1a-Tg2-Cre neuron from layer 4 of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
88.  Allen Institute: Scnn1a-Tg2-Cre VISp layer 4 472427533
This is an Allen Cell Types Database model of a Scnn1a-Tg2-Cre neuron from layer 4 of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
89.  Allen Institute: Scnn1a-Tg2-Cre VISp layer 4 472912107
This is an Allen Cell Types Database model of a Scnn1a-Tg2-Cre neuron from layer 4 of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
90.  Allen Institute: Scnn1a-Tg2-Cre VISp layer 4 473465456
This is an Allen Cell Types Database model of a Scnn1a-Tg2-Cre neuron from layer 4 of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
91.  Allen Institute: Scnn1a-Tg2-Cre VISp layer 5 472306460
This is an Allen Cell Types Database model of a Scnn1a-Tg2-Cre neuron from layer 5 of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
92.  Allen Institute: Scnn1a-Tg3-Cre VISp layer 4 329321704
This is an Allen Cell Types Database model of a Scnn1a-Tg3-Cre neuron from layer 4 of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
93.  Allen Institute: Scnn1a-Tg3-Cre VISp layer 4 472363762
This is an Allen Cell Types Database model of a Scnn1a-Tg3-Cre neuron from layer 4 of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
94.  Allen Institute: Scnn1a-Tg3-Cre VISp layer 4 473862845
This is an Allen Cell Types Database model of a Scnn1a-Tg3-Cre neuron from layer 4 of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
95.  Allen Institute: Scnn1a-Tg3-Cre VISp layer 4 473872986
This is an Allen Cell Types Database model of a Scnn1a-Tg3-Cre neuron from layer 4 of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
96.  Allen Institute: Scnn1a-Tg3-Cre VISp layer 5 472455509
This is an Allen Cell Types Database model of a Scnn1a-Tg3-Cre neuron from layer 5 of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
97.  Allen Institute: Scnn1a-Tg3-Cre VISp layer 5 473863578
This is an Allen Cell Types Database model of a Scnn1a-Tg3-Cre neuron from layer 4 of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
98.  Allen Institute: Scnn1a-Tg3-Cre VISp layer 5 473871773
This is an Allen Cell Types Database model of a Scnn1a-Tg3-Cre neuron from layer 5 of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
99.  Allen Institute: Sst-IRES-Cre VISp layer 2/3 471086533
This is an Allen Cell Types Database model of a Sst-IRES-Cre neuron from layer 2/3 of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
100.  Allen Institute: Sst-IRES-Cre VISp layer 2/3 472304676
This is an Allen Cell Types Database model of a Sst-IRES-Cre neuron from layer 2/3 of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
101.  Allen Institute: Sst-IRES-Cre VISp layer 4 472304539
This is an Allen Cell Types Database model of a Sst-IRES-Cre neuron from layer 4 of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
102.  Allen Institute: Sst-IRES-Cre VISp layer 5 472299363
This is an Allen Cell Types Database model of a Sst-IRES-Cre neuron from layer 5 of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
103.  Allen Institute: Sst-IRES-Cre VISp layer 5 472450023
This is an Allen Cell Types Database model of a Sst-IRES-Cre neuron from layer 5 of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
104.  Allen Institute: Sst-IRES-Cre VISp layer 5 473835796
This is an Allen Cell Types Database model of a Sst-IRES-Cre neuron from layer 5 of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
105.  Allen Institute: Sst-IRES-Cre VISp layer 6a 472440759
This is an Allen Cell Types Database model of a Sst-IRES-Cre neuron from layer 6a of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
106.  Altered complexity in layer 2/3 pyramidal neurons (Luuk van der Velden et al. 2012)
" ... Our experimental results show that hypercomplexity of the apical dendritic tuft of layer 2/3 pyramidal neurons affects neuronal excitability by reducing the amount of spike frequency adaptation. This difference in firing pattern, related to a higher dendritic complexity, was accompanied by an altered development of the afterhyperpolarization slope with successive action potentials. Our abstract and realistic neuronal models, which allowed manipulation of the dendritic complexity, showed similar effects on neuronal excitability and confirmed the impact of apical dendritic complexity. Alterations of dendritic complexity, as observed in several pathological conditions such as neurodegenerative diseases or neurodevelopmental disorders, may thus not only affect the input to layer 2/3 pyramidal neurons but also shape their firing pattern and consequently alter the information processing in the cortex."
107.  Ambiguous Encoding and Distorted Perception (Carlson and Kawasaki 2006)
"... In the weakly electric fish Eigenmannia, P- and T-type primary afferent fibers are specialized for encoding the amplitude and phase, respectively, of electrosensory stimuli. We used a stimulus estimation technique to quantify the ability of P- and T-units to encode random modulations in amplitude and phase. As expected, P-units exhibited a clear preference for encoding amplitude modulations, whereas T-units exhibited a clear preference for encoding phase modulations. Surprisingly, both types of afferents also encoded their nonpreferred stimulus attribute when it was presented in isolation or when the preferred stimulus attribute was sufficiently weak. Because afferent activity can be affected by modulations in either amplitude or phase, it is not possible to unambiguously distinguish between these two stimulus attributes by observing the activity of a single afferent fiber. Simple model neurons with a preference for encoding either amplitude or phase also encoded their nonpreferred stimulus attribute when it was presented in isolation, suggesting that such ambiguity is unavoidable. ... " See paper for more and details.
108.  Amyloid beta (IA block) effects on a model CA1 pyramidal cell (Morse et al. 2010)
The model simulations provide evidence oblique dendrites in CA1 pyramidal neurons are susceptible to hyper-excitability by amyloid beta block of the transient K+ channel, IA. See paper for details.
109.  Amyloid-beta effects on release probability and integration at CA3-CA1 synapses (Romani et al. 2013)
The role of amyloid beta (Aß) in brain function and in the pathogenesis of Alzheimer’s disease remains elusive. Recent publications reported that an increase in Aß concentration perturbs presynaptic release in hippocampal neurons, in particular by increasing release probability of CA3-CA1 synapses. The model predics how this alteration can affect synaptic plasticity and signal integration. The results suggest that the perturbation of release probability induced by increased Aß can significantly alter the spike probability of CA1 pyramidal neurons and thus contribute to abnormal hippocampal function during Alzheimer’s disease.
110.  An integrative dynamic model of brain energy metabolism (Coultier et al 2009)
An integrative, systems approach to the modelling of brain energy metabolism is presented. Mechanisms such as glutamate cycling between neurons and astrocytes and glycogen storage in astrocytes have been implemented. A unique feature of the model is its calibration using in vivo data of brain glucose and lactate from freely moving rats under various stimuli. The model has been used to perform simulated perturbation experiments that show that glycogen breakdown in astrocytes is significantly activated during sensory (tail pinch) stimulation. This mechanism provides an additional input of energy substrate during high consumption phases. By way of validation, data from the perfusion of 50?µM propranolol in the rat brain was compared with the model outputs. Propranolol affects the glucose dynamics during stimulation, and this was accurately reproduced in the model by a reduction in the glycogen breakdown in astrocytes. The model’s predictive capacity was verified by using data from a sensory stimulation (restraint) that was not used for model calibration. Finally, a sensitivity analysis was conducted on the model parameters, this showed that the control of energy metabolism and transport processes are critical in the metabolic behaviour of cerebral tissue.
111.  An ion-based model for swelling of neurons and astrocytes (Hubel & Ullah 2016)
The programs describe ion dynamics and osmosis-driven cellular swelling. “code_fig3.ode” shows a scenario of permanent cessation of energy supply / Na/K-pump activity, and the induced transition from normal conditions to the Donnan equilibrium for an isolated neuron and its extracellular space. “code_Fig7.ode” shows spreading depolarization induced by an interruption of energy supply in a model consisting of a neuron, a glia cell and the extracellular space. The simulations show the evolution of ion concentrations, Nernst potentials, the membrane potential, gating variables and cellular volumes.
112.  Anoxic depolarization, recovery: effect of brain regions and extracellular space (Hubel et al. 2016)
The extent of anoxic depolarization (AD), the initial electrophysiological event during ischemia, determines the degree of brain region-specific neuronal damage. Neurons in higher brain regions have stronger ADs and are more easily injured than neurons in lower brain region. The mechanism leading to such differences is not clear. We use a computational model based on a Hodgkin-Huxley framework which includes neural spiking dynamics, processes of ion accumulation, and homeostatic mechanisms like vascular coupling and Na/K-exchange pumps. We show that a large extracellular space (ECS) explains the recovery failure in high brain regions. A phase-space analysis shows that with a large ECS recovery from AD through potassium regulation is impossible. The code 'time_series.ode' can be used to simulate AD for a large and a small ECS and show the different behaviors. The code ‘continuations.ode’ can be used to show the fixed point structure. Depending on our choice of large or small ECS the fixed point curve implies the presence/absence of a recovery threshold that defines the potassium clearance demand.
113.  AOB mitral cell: persistent activity without feedback (Zylbertal et al., 2015)
Persistent activity has been reported in many brain areas and is hypothesized to mediate working memory and emotional brain states and to rely upon network or biophysical feedback. Here we demonstrate a novel mechanism by which persistent neuronal activity can be generated without feedback, relying instead on the slow removal of Na+ from neurons following bursts of activity. This is a realistic conductance-based model that was constructed using the detailed morphology of a single typical accessory olfactory bulb (AOB) mitral cell for which the electrophysiological properties were characterized.
114.  AP back-prop. explains threshold variability and rapid rise (McCormick et al. 2007, Yu et al. 2008)
This simple axon-soma model explained how the rapid rising phase in the somatic spike is derived from the propagated axon initiated spike, and how the somatic spike threshold variance is affected by spike propagation.
115.  AP initiation and propagation in type II cochlear ganglion cell (Hossain et al 2005)
The model of type II cochlear ganglion cell was based on the immunostaining of the mouse auditory pathway. Specific antibodies were used to map the distribution of voltage-dependent sodium channels along the two unmyelinated axon-like processes of the bipolar ganglion cells. Three distinct hot spots were detected. A high density of sodium channels was present over the entire trajectory of sensory endings beneath the outer hair cells (the most distal portion of the peripheral axon). The other two hot spots were localized in the initial segments of both of the axons that flank the unmyelinated bipolar ganglion cell bodies. A biophysical model indicates that all three hot spots might play important roles in action potential initiation and propagation. For instance, the hot spot in the receptor segment is important for transforming the receptor potentials into a full blown action potential (Supplemental Fig. 1). The hot spots in the two paraganglionic axon initial segments are there to ensure the successful propagation of action potentials from the peripheral to the central axon through the cell body. The Readme.txt file provides step by step instructions on how to recreate Figures 6 and 7 of Hossain et al., 2005 paper.
116.  AP initiation, propagation, and cortical invasion in a Layer 5 pyramidal cell (Anderson et 2018)
" ... High frequency (~130 Hz) deep brain stimulation (DBS) of the subthalamic region is an established clinical therapy for the treatment of late stage Parkinson's disease (PD). Direct modulation of the hyperdirect pathway, defined as cortical layer V pyramidal neurons that send an axon collateral to the subthalamic nucleus (STN), has emerged as a possible component of the therapeutic mechanisms. ...We found robust AP propagation throughout the complex axonal arbor of the hyperdirect neuron. Even at therapeutic DBS frequencies, stimulation induced APs could reach all of the intracortical axon terminals with ~100% fidelity. The functional result of this high frequency axonal driving of the thousands of synaptic connections made by each directly stimulated hyperdirect neuron is a profound synaptic suppression that would effectively disconnect the neuron from the cortical circuitry. ..."
117.  AP shape and parameter constraints in optimization of compartment models (Weaver and Wearne 2006)
"... We construct an objective function that includes both time-aligned action potential shape error and errors in firing rate and firing regularity. We then implement a variant of simulated annealing that introduces a recentering algorithm to handle infeasible points outside the boundary constraints. We show how our objective function captures essential features of neuronal firing patterns, and why our boundary management technique is superior to previous approaches."
118.  Apical Length Governs Computational Diversity of Layer 5 Pyramidal Neurons (Galloni et al 2020)
"Anatomical similarity across the neocortex has led to the common assumption that the circuitry is modular and performs stereotyped computations. Layer 5 pyramidal neurons (L5PNs) in particular are thought to be central to cortical computation because of their extensive arborisation and nonlinear dendritic operations. Here, we demonstrate that computations associated with dendritic Ca2+ plateaus in mouse L5PNs vary substantially between the primary and secondary visual cortices. L5PNs in the secondary visual cortex show reduced dendritic excitability and smaller propensity for burst firing. This reduced excitability is correlated with shorter apical dendrites. Using numerical modelling, we uncover a universal principle underlying the influence of apical length on dendritic backpropagation and excitability, based on a Na+ channel-dependent broadening of backpropagating action potentials. In summary, we provide new insights into the modulation of dendritic excitability by apical dendrite length and show that the operational repertoire of L5PNs is not universal throughout the brain."
119.  Artificial neuron model (Izhikevich 2003, 2004, 2007)
A set of models is presented based on 2 related parameterizations to reproduce spiking and bursting behavior of multiple types of cortical neurons and thalamic neurons. These models combine the biologically plausibility of Hodgkin Huxley-type dynamics and the computational efficiency of integrate-and-fire neurons. Using these model, one can simulate tens of thousands of spiking cortical neurons in real time (1 ms resolution) using a desktop PC.
120.  Auditory nerve model with linear tuning (Heinz et al 2001)
A method for calculating psychophysical performance limits based on stochastic neural responses is introduced and compared to previous analytical methods for evaluating auditory discrimination of tone frequency and level. The method uses signal detection theory and a computational model for a population of auditory nerve (AN) fiber responses. Please see paper for details.
121.  Availability of low-threshold Ca2+ current in retinal ganglion cells (Lee SC et al. 2003)
"... we measured T-type current of isolated goldfish retinal ganglion cells with perforated-patch voltageclamp methods in solutions containing a normal extracellular Ca2+ concentration. The voltage sensitivities and rates of current activation, inactivation, deactivation, and recovery from inactivation were similar to those of expressed +1G (CaV3.1) Ca2+ channel clones, except that the rate of deactivation was significantly faster. We reproduced the amplitude and kinetics of measured T currents with a numerical simulation based on a kinetic model developed for an +1G Ca2+ channel. Finally, we show that this model predicts the increase of T-type current made available between resting potential and spike threshold by repetitive hyperpolarizations presented at rates that are within the bandwidth of signals processed in situ by these neurons."
122.  Ave. neuron model for slow-wave sleep in cortex Tatsuki 2016 Yoshida 2018 Rasmussen 2017 (all et al)
Averaged neuron(AN) model is a conductance-based (Hodgkin-Huxley type) neuron model which includes a mean-field approximation of a population of neurons. You can simulate previous models (AN model: Tatsuki et al., 2016 and SAN model: Yoshida et al., 2018), and various models with 'X model' based on channel and parameter modules. Also, intracellular and extracellular ion concentration can be taken into consideration using the Nernst equation (See Ramussen et al., 2017).
123.  Axon-somatic back-propagation in a detailed model of cat spinal motoneuron (Balbi et al, 2015)
Morphologically detailed conductance-based models of cat spinal alpha motoneurons have been developed, with the aim to reproduce and clarify some aspects of the electrophysiological behavior of the antidromic axon-somatic spike propagation. Fourteen 3D morphologically detailed somata and dendrites of cat spinal alpha motoneurons have been imported from an open-access web-based database of neuronal morphologies,, and instantiated in neurocomputational models.
124.  Axonal gap junctions produce fast oscillations in cerebellar Purkinje cells (Traub et al. 2008)
Examines how electrical coupling between proximal axons produces fast oscillations in cerebellar Purkinje cells. Traub RD, Middleton SJ, Knopfel T, Whittington MA (2008) Model of very fast (>75 Hz) network oscillations generated by electrical coupling between the proximal axons of cerebellar Purkinje cells. European Journal of Neuroscience.
125.  Axonal NaV1.6 Sodium Channels in AP Initiation of CA1 Pyramidal Neurons (Royeck et al. 2008)
"... We show that the Na+ channel NaV1.6 displays a striking aggregation at the AIS of cortical neurons. ... In combination with simulations using a realistic computer model of a CA1 pyramidal cell, our results imply that a hyperpolarized voltage-dependence of activation of AIS NaV1.6 channels is important both in determining spike threshold and localizing spike initiation to the AIS. ... These results suggest that NaV1.6 subunits at the AIS contribute significantly to its role as spike trigger zone and shape repetitive discharge properties of CA1 neurons."
126.  Axonal Projection and Interneuron Types (Helmstaedter et al. 2008)
"Interneurons in layer 2/3 (L2/3) of the somatosensory cortex show 4 types of axonal projection patterns with reference to the laminae and borders of columns in rat barrel cortex (Helmstaedter et al. 2008a). Here, we analyzed the dendritic geometry and electrical excitability of these interneurons. ... We conclude that 1) dendritic polarity is correlated to intrinsic electrical excitability, and 2) the axonal projection pattern represents an independent classifier of interneurons. "
127.  Balance of excitation and inhibition (Carvalho and Buonomano 2009)
" ... Here, theoretical analyses reveal that excitatory synaptic strength controls the threshold of the neuronal input-output function, while inhibitory plasticity alters the threshold and gain. Experimentally, changes in the balance of excitation and inhibition in CA1 pyramidal neurons also altered their input-output function as predicted by the model. These results support the existence of two functional modes of plasticity that can be used to optimize information processing: threshold and gain plasticity."
128.  Basal ganglia-thalamic network model for deep brain stimulation (So et al. 2012)
This is a model of the basal ganglia-thalamic network, modified from the Rubin and Terman model (High frequency stimulation of the Subthalamic Nucleus, Rubin and Terman 2004). We subsequently used this model to investigate the effectiveness of STN and GPi DBS as well as lesion when various proportions of local cells and fibers of passage were activated or silenced. The BG network exhibited characteristics consistent with published experimental data, both on the level of single cells and on the network level. Perhaps most notably, and in contrast to the original RT model, the changes in the thalamic error index with changes in the DBS frequency matched well the changes in clinical symptoms with changes in DBS frequency.
129.  Basket cell extrasynaptic inhibition modulates network oscillations (Proddutur et al., 2013)
Among the rhythmic firing patterns observed in brain, gamma oscillations, which are involved in memory formation and retrieval, are generated by networks of fast-spiking basket cells (FS-BCs) with robust interconnectivity through fast GABA synapses. Recently, we identified presence of extrasynaptic tonic GABA currents in FS-BCs and showed that experimentally-induced seizures enhance extrasynaptic tonic GABA currents and render GABA reversal potential (EGABA) depolarizing (Yu et al., 2013). Extrasynaptic GABA currents are mediated by extra- and peri-synaptically located GABAARs and can contribute to synaptic decay kinetics. Additionally, shunting rather than hyperpolarizing EGABA has been shown to increase the frequency and reduce coherence of network oscillations. Using homogeneous networks of biophysically-based, multi-compartmental model FS-BCs, we examined how the presence of extrasynaptic GABA currents and the experimentally identified seizure-induced alterations in GABA currents and EGABA modify the frequency and coherence of network firing.
130.  BCM-like synaptic plasticity with conductance-based models (Narayanan Johnston, 2010)
" ... Although the BCM-like plasticity framework has been a useful formulation to understand synaptic plasticity and metaplasticity, a mechanism for the activity-dependent regulation of this modification threshold has remained an open question. In this simulation study based on CA1 pyramidal cells, we use a modification of the calcium-dependent hypothesis proposed elsewhere and show that a change in the hyperpolarization-activated, nonspecific-cation h current is capable of shifting the modification threshold. ..."
131.  BDNF morphological contributions to AP enhancement (Galati et al. 2016)
" ... We quantified BDNF’s effect on cultured cortical neuron morphological parameters and found that BDNF stimulates dendrite growth and addition of dendrites while increasing both excitatory and inhibitory presynaptic inputs in a spatially restricted manner. To gain insight into how these combined changes in neuron structure and synaptic input impact AP generation, we used the morphological parameters we gathered to generate computational models. Simulations suggest that BDNF-induced neuron morphologies generate more APs under a wide variety of conditions. ..."
132.  Beta-cell hubs maintain Ca2+ oscillations in human and mouse islet simulations (Lei et al 2018)
"Islet ß-cells are responsible for secreting all circulating insulin in response to rising plasma glucose concentrations. These cells are a phenotypically diverse population that express great functional heterogeneity. In mice, certain ß-cells (termed 'hubs') have been shown to be crucial for dictating the islet response to high glucose, with inhibition of these hub cells abolishing the coordinated Ca2+ oscillations necessary for driving insulin secretion. These ß-cell hubs were found to be highly metabolic and susceptible to pro-inflammatory and glucolipotoxic insults. In this study, we explored the importance of hub cells in human by constructing mathematical models of Ca2+ activity in human islets. Our simulations revealed that hubs dictate the coordinated Ca2+ response in both mouse and human islets; silencing a small proportion of hubs abolished whole-islet Ca2+ activity. We also observed that if hubs are assumed to be preferentially gap junction coupled, then the simulations better adhere to the available experimental data. Our simulations of 16 size-matched mouse and human islet architectures revealed that there are species differences in the role of hubs; ..."
133.  Binocular energy model set for binocular neurons in optic lobe of praying mantis (Rosner et al 2019)
This is a version of the binocular energy model with parameters chosen to reproduce individual cells in praying mantis optic lobe. The receptive fields are very coarsely sampled (6 different horizontal locations only) to match the coarse sampling of the data given very limited recording time.
134.  Biophysical and phenomenological models of spike-timing dependent plasticity (Badoual et al. 2006)
"Spike-timing dependent plasticity (STDP) is a form of associative synaptic modification which depends on the respective timing of pre- and post-synaptic spikes. The biophysical mechanisms underlying this form of plasticity are currently not known. We present here a biophysical model which captures the characteristics of STDP, such as its frequency dependency, and the effects of spike pair or spike triplet interactions. ... A simplified phenomenological model is also derived..."
135.  Biophysically detailed model of the mouse sino-atrial node cell (Kharche et al. 2011)
This model is developed to study the role of various electrophysiological mechanisms in generating cardiac pacemaking action potentials (APs).The model incorporates membrane ionic currents and intracellular mechanisms contributing to spontaneous mouse SAN APs. The model was validated by testing the functional roles of individual membrane currents in one and multiple parameter analyses.The roles of intracellular Ca2+-handling mechanisms on cardiac pacemaking were also investigated in the model.
136.  Biophysically realistic neuron models for simulation of cortical stimulation (Aberra et al. 2018)
This archive instantiates the single-cell cortical models used in (Aberra et al. 2018) and sets up extracellular stimulation with either a point-current source, to simulate intracortical microstimulation (ICMS), or a uniform E-field distribution, with a monophasic, rectangular pulse waveform in both cases.
137.  BK Channels Promote Bursting in Pituitary Cells (Tabak et al 2011)
"The electrical activity pattern of endocrine pituitary cells regulates their basal secretion level. Rat somatotrophs and lactotrophs exhibit spontaneous bursting and have high basal levels of hormone secretion, while gonadotrophs exhibit spontaneous spiking and have low basal hormone secretion. It has been proposed that the difference in electrical activity between bursting somatotrophs and spiking gonadotrophs is due to the presence of large conductance potassium (BK) channels on somatotrophs but not on gonadotrophs. This is one example where the role of an ion channel type may be clearly established. We demonstrate here that BK channels indeed promote bursting activity in pituitary cells. Blocking BK channels in bursting lacto-somatotroph GH4C1 cells changes their firing activity to spiking, while further adding an artificial BK conductance via dynamic clamp restores bursting. Importantly, this burst-promoting effect requires a relatively fast BK activation/deactivation, as predicted by computational models. We also show that adding a fast-activating BK conductance to spiking gonadotrophs converts the activity of these cells to bursting. Together, our results suggest that differences in BK channel expression may underlie the differences in electrical activity and basal hormone secretion levels among pituitary cell types and that the rapid rate of BK channel activation is key to its role in burst promotion."
138.  Boundary effects influence velocity in transverse propagation of cardiac APs (Sperelakis et al 2005)
... earlier experiments were carried out with 2-dimensional sheets of cells: 2 × 3, 3 × 4, and 5 × 5 models (where the first number is the number of parallel chains and the second is the number of cells in each chain). The purpose of the present study was to enlarge the model size to 7 × 7, thus enabling the transverse velocities to be compared in models of different sizes (where all circuit parameters are identical in all models). This procedure should enable the significance of the role of edge (boundary) effects in transverse propagation to be determined. See paper for more and details.
139.  Breakdown of accmmodation in nerve: a possible role for INAp (Hennings et al 2005)
The present modeling study suggests that persistent, low-threshold, rapidly activating sodium currents have a key role in breakdown of accommodation, and that breakdown of accommodation can be used as a tool for studying persistent sodium current under normal and pathological conditions. See paper for more and details.
140.  Brette-Gerstner model (Touboul and Brette 2008)
Brian code to simulate the Brette-Gerstner model and reproduce the figures of Touboul and Brette, Biol Cyber (2008).
141.  Bursting activity of neuron R15 in Aplysia (Canavier et al 1991, Butera et al 1995)
An equivalent circuit model of the R15 bursting neuron in Aplysia has been combined with a fluid compartment model, resulting in a model that incorporates descriptions of most of the membrane ion channels that are known to exist in the somata of R15, as well as providing a Ca2+ balance on the cell. ... (from the second paper) we have implemented proposed mechanisms for the modulation of two ionic currents (IR and ISI) that play key roles in regulating its spontaneous electrical activity. The model was sufficient to simulate a wide range of endogenous activity in the presence of various concentrations of 5-HT or DA. See papers for more and details.
142.  Bursting and oscillations in RD1 Retina driven by AII Amacrine Neuron (Choi et al. 2014)
"In many forms of retinal degeneration, photoreceptors die but inner retinal circuits remain intact. In the rd1 mouse, an established model for blinding retinal diseases, spontaneous activity in the coupled network of AII amacrine and ON cone bipolar cells leads to rhythmic bursting of ganglion cells. Since such activity could impair retinal and/or cortical responses to restored photoreceptor function, understanding its nature is important for developing treatments of retinal pathologies. Here we analyzed a compartmental model of the wild-type mouse AII amacrine cell to predict that the cell's intrinsic membrane properties, specifically, interacting fast Na and slow, M-type K conductances, would allow its membrane potential to oscillate when light-evoked excitatory synaptic inputs were withdrawn following photoreceptor degeneration. ..."
143.  Bursting and resonance in cerebellar granule cells (D'Angelo et al. 2001)
In this study we report theta-frequency (3-12 Hz) bursting and resonance in rat cerebellar granule cells and show that these neurons express a previously unidentified slow repolarizing K1 current (IK-slow ). Our experimental and modeling results indicate that IK-slow was necessary for both bursting and resonance. See paper for more.
144.  Bursting in dopamine neurons (Li YX et al 1996)
"Burst firing of dopaminergic neurons of the substantia nigra pars compacta can be induced in vitro by the glutamate agonist N-methyl-D-aspartate. It has been suggested that the interburst hyperpolarization is due to Na+ extrusion by a ouabain-sensitive pump (Johnson et al. (1992) Science 258, 665-667). We formulate and explore a theoretical model, with a minimal number of currents, for this novel mechanism of burst generation. This minimal model is further developed into a more elaborate model based on observations of additional currents and hypotheses about their spatial distribution in dopaminergic neurons ... Responses of the model to a number of electrophysiological and pharmacological stimuli are consistent with known responses observed under similar conditions. ..."
145.  Ca+/HCN channel-dependent persistent activity in multiscale model of neocortex (Neymotin et al 2016)
"Neuronal persistent activity has been primarily assessed in terms of electrical mechanisms, without attention to the complex array of molecular events that also control cell excitability. We developed a multiscale neocortical model proceeding from the molecular to the network level to assess the contributions of calcium regulation of hyperpolarization-activated cyclic nucleotide-gated (HCN) channels in providing additional and complementary support of continuing activation in the network. ..."
146.  CA1 PV+ fast-firing hippocampal interneuron (Ferguson et al. 2013)
This two-variable simple model is derived based on patch-clamp recordings from the CA1 region of a whole hippocampus preparation of PV+ fast-firing cells. Since basket cells, axo-axonic cells and bistratified cells can be PV+ and fast-firing, this model could be representative of these cell types. The model code will also be made available on OSB.
147.  CA1 pyr cell: Inhibitory modulation of spatial selectivity+phase precession (Grienberger et al 2017)
Spatially uniform synaptic inhibition enhances spatial selectivity and temporal coding in CA1 place cells by suppressing broad out-of-field excitation.
148.  CA1 pyramidal cell: I_NaP and I_M contributions to somatic bursting (Golomb et al 2006)
To study the mechanisms of bursting, we have constructed a conductance-based, one-compartment model of CA1 pyramidal neurons. In this neuron model, reduced [Ca2+]o is simulated by negatively shifting the activation curve of the persistent Na+ current (INaP), as indicated by recent experimental results. The neuron model accounts, with different parameter sets, for the diversity of firing patterns observed experimentally in both zero and normal [Ca2+]o. Increasing INaP in the neuron model induces bursting and increases the number of spikes within a burst, but is neither necessary nor sufficient for bursting. We show, using fast-slow analysis and bifurcation theory, that the M-type K+ current (IM) allows bursting by shifting neuronal behavior between a silent and a tonically-active state, provided the kinetics of the spike generating currents are sufficiently, though not extremely, fast. We suggest that bursting in CA1 pyramidal cells can be explained by a single compartment *square bursting* mechanism with one slow variable, the activation of IM. See paper for more and details.
149.  CA1 pyramidal neuron (Combe et al 2018)
"Gamma oscillations are thought to play a role in learning and memory. Two distinct bands, slow (25-50 Hz) and fast (65-100 Hz) gamma, have been identified in area CA1 of the rodent hippocampus. Slow gamma is phase-locked to activity in area CA3 and presumably driven by the Schaffer collaterals. We used a combination of computational modeling and in vitro electrophysiology in hippocampal slices of male rats to test whether CA1 neurons responded to Schaffer collateral stimulation selectively at slow gamma frequencies, and to identify the mechanisms involved. Both approaches demonstrated that in response to temporally precise input at Schaffer collaterals, CA1 pyramidal neurons fire preferentially in the slow gamma range regardless of whether the input is at fast or slow gamma frequencies, suggesting frequency selectivity in CA1 output with respect to CA3 input. In addition, phase-locking, assessed by the vector strength, was more precise for slow gamma than fast gamma input. ..."
150.  CA1 pyramidal neuron (Ferguson et al. 2014)
Izhikevich-based models of CA1 pyramidal cells, with parameters constrained based on a whole hippocampus preparation. Strongly and weakly adapting models based on the experimental data have been developed. Code produces example model output. The code will also be made available on OSB.
151.  CA1 pyramidal neuron (Migliore et al 1999)
Hippocampal CA1 pyramidal neuron model from the paper M.Migliore, D.A Hoffman, J.C. Magee and D. Johnston (1999) Role of an A-type K+ conductance in the back-propagation of action potentials in the dendrites of hippocampal pyramidal neurons, J. Comput. Neurosci. 7, 5-15. Instructions are provided in the below README file.Contact if you have any questions about the implementation of the model.
152.  CA1 pyramidal neuron synaptic integration (Bloss et al. 2016)
"... We examined synaptic connectivity between molecularly defined inhibitory interneurons and CA1 pyramidal cell dendrites using correlative light-electron microscopy and large-volume array tomography. We show that interneurons can be highly selective in their connectivity to specific dendritic branch types and, furthermore, exhibit precisely targeted connectivity to the origin or end of individual branches. Computational simulations indicate that the observed subcellular targeting enables control over the nonlinear integration of synaptic input or the initiation and backpropagation of action potentials in a branchselective manner. Our results demonstrate that connectivity between interneurons and pyramidal cell dendrites is more precise and spatially segregated than previously appreciated, which may be a critical determinant of how inhibition shapes dendritic computation."
153.  CA1 pyramidal neuron synaptic integration (Jarsky et al. 2005)
"The perforant-path projection to the hippocampus forms synapses in the apical tuft of CA1 pyramidal neurons. We used computer modeling to examine the function of these distal synaptic inputs, which led to three predictions that we confirmed in experiments using rat hippocampal slices. ... This 'gating' of dendritic spike propagation may be an important activation mode of CA1 pyramidal neurons, and its modulation by neurotransmitters or long-term, activity-dependent plasticity may be an important feature of dendritic integration during mnemonic processing in the hippocampus."
154.  CA1 pyramidal neuron synaptic integration (Li and Ascoli 2006, 2008)
The model shows how different input patterns (irregular & asynchronous, irregular & synchronous, regular & asynchronous, regular & synchronous) affect the neuron's output rate when 1000 synapses are distributed in the proximal apical dendritic tree of a hippocampus CA1 pyramidal neuron.
155.  CA1 pyramidal neuron to study INaP properties and repetitive firing (Uebachs et al. 2010)
A model of a CA1 pyramidal neuron containing a biophysically realistic morphology and 15 distributed voltage and Ca2+-dependent conductances. Repetitive firing is modulated by maximal conductance and the voltage dependence of the persistent Na+ current (INaP).
156.  CA1 pyramidal neuron: as a 2-layer NN and subthreshold synaptic summation (Poirazi et al 2003)
We developed a CA1 pyramidal cell model calibrated with a broad spectrum of in vitro data. Using simultaneous dendritic and somatic recordings, and combining results for two different response measures (peak vs. mean EPSP), two different stimulus formats (single shock vs. 50 Hz trains), and two different spatial integration conditions (within vs. between-branch summation), we found the cell's subthreshold responses to paired inputs are best described as a sum of nonlinear subunit responses, where the subunits correspond to different dendritic branches. In addition to suggesting a new type of experiment and providing testable predictions, our model shows how conclusions regarding synaptic arithmetic can be influenced by an array of seemingly innocuous experimental design choices.
157.  CA1 pyramidal neuron: calculation of MRI signals (Cassara et al. 2008)
NEURON mod files from the paper: Cassarà AM, Hagberg GE, Bianciardi M, Migliore M, Maraviglia B. Realistic simulations of neuronal activity: A contribution to the debate on direct detection of neuronal currents by MRI. Neuroimage. 39:87-106 (2008). In this paper, we use a detailed calculation of the magnetic field produced by the neuronal currents propagating over a hippocampal CA1 pyramidal neuron placed inside a cubic MR voxel of length 1.2 mm to estimate the Magnetic Resonance signal.
158.  CA1 pyramidal neuron: conditional boosting of dendritic APs (Watanabe et al 2002)
Model files from the paper Watanabe S, Hoffman DA, Migliore M, Johnston D (2002). The experimental and modeling results support the hypothesis that dendritic K-A channels and the boosting of back-propagating action potentials contribute to the induction of LTP in CA1 neurons. See the paper for details. Questions about the model may be addressed to Michele Migliore:
159.  CA1 pyramidal neuron: dendritic Ca2+ inhibition (Muellner et al. 2015)
In our experimental study, we combined paired patch-clamp recordings and two-photon Ca2+ imaging to quantify inhibition exerted by individual GABAergic contacts on hippocampal pyramidal cell dendrites. We observed that Ca2+ transients from back-propagating action potentials were significantly reduced during simultaneous activation of individual nearby GABAergic synapses. To simulate dendritic Ca2+ inhibition by individual GABAergic synapses, we employed a multi-compartmental CA1 pyramidal cell model with detailed morphology, voltage-gated channel distributions, and calcium dynamics, based with modifications on the model of Poirazi et al., 2003, modelDB accession # 20212.
160.  CA1 pyramidal neuron: Dendritic Na+ spikes are required for LTP at distal synapses (Kim et al 2015)
This model simulates the effects of dendritic sodium spikes initiated in distal apical dendrites on the voltage and the calcium dynamics revealed by calcium imaging. It shows that dendritic sodium spike promotes large and transient calcium influxes via NMDA receptor and L-type voltage-gated calcium channels, which contribute to the induction of LTP at distal synapses.
161.  CA1 pyramidal neuron: depolarization block (Bianchi et al. 2012)
NEURON files from the paper: On the mechanisms underlying the depolarization block in the spiking dynamics of CA1 pyramidal neurons by D.Bianchi, A. Marasco, A.Limongiello, C.Marchetti, H.Marie,B.Tirozzi, M.Migliore (2012). J Comput. Neurosci. In press. DOI: 10.1007/s10827-012-0383-y. Experimental findings shown that under sustained input current of increasing strength neurons eventually stop firing, entering a depolarization block. We analyze the spiking dynamics of CA1 pyramidal neuron models using the same set of ionic currents on both an accurate morphological reconstruction and on its reduction to a single-compartment. The results show the specic ion channel properties and kinetics that are needed to reproduce the experimental findings, and how their interplay can drastically modulate the neuronal dynamics and the input current range leading to depolarization block.
162.  CA1 pyramidal neuron: effects of Lamotrigine on dendritic excitability (Poolos et al 2002)
NEURON mod files from N. Poolos, M. Migliore, and D. Johnston, Nature Neuroscience (2002). The experimental and modeling results in this paper demonstrate for the first time that neuronal excitability can be altered by pharmaceuticals acting selectively on dendrites, and suggest an important role for Ih in controlling dendritic excitability and epileptogenesis.
163.  CA1 pyramidal neuron: functional significance of axonal Kv7 channels (Shah et al. 2008)
The model used in this paper confirmed the experimental findings suggesting that axonal Kv7 channels are critically and uniquely required for determining the inherent spontaneous firing of hippocampal CA1 pyramids, independently of alterations in synaptic activity. The model predicts that the axonal Kv7 density could be 3-5 times that at the soma.
164.  CA1 pyramidal neuron: nonlinear a5-GABAAR controls synaptic NMDAR activation (Schulz et al 2018)
The study shows that IPSCs mediated by a5-subunit containing GABAA receptors are strongly outward-rectifying generating 4-fold larger conductances above -50?mV than at rest. Experiments and modeling show that synaptic activation of these receptors can very effectively control voltage-dependent NMDA-receptor activation in a spatiotemporally controlled manner in fine dendrites of CA1 pyramidal cells. The files contain the NEURON code for Fig.8, Fig.S8 and Fig.S9 of the paper. The model is based on the model published by Bloss et al., 2017. Physiological properties of GABA synapses were modified as determined by optogenetic activation of inputs during voltage-clamp recordings in Schulz et al. 2018. Other changes include stochastic synaptic release and short-term synaptic plasticity. All changes of mechanisms and parameters are detailed in the Methods of the paper. Simulation can be run by starting start_simulation.hoc after running mknrndll. The files that model the individual figures have to be uncommented in start_simulation.hoc beforehand.
165.  CA1 pyramidal neuron: Persistent Na current mediates steep synaptic amplification (Hsu et al 2018)
This paper shows that persistent sodium current critically contributes to the subthreshold nonlinear dynamics of CA1 pyramidal neurons and promotes rapidly reversible conversion between place-cell and silent-cell in the hippocampus. A simple model built with realistic axo-somatic voltage-gated sodium channels in CA1 (Carter et al., 2012; Neuron 75, 1081–1093) demonstrates that the biophysics of persistent sodium current is sufficient to explain the synaptic amplification effects. A full model built previously (Grienberger et al., 2017; Nature Neuroscience, 20(3): 417–426) with detailed morphology, ion channel types and biophysical properties of CA1 place cells naturally reproduces the steep voltage dependence of synaptic responses.
166.  CA1 Pyramidal Neuron: slow Na+ inactivation (Migliore 1996)
Model files from the paper: M. Migliore, Modeling the attenuation and failure of action potentials in the dendrites of hippocampal neurons, Biophys. J. 71:2394-403 (1996). Please see the below readme file for installation and use instructions. Contact if you have any questions about the implementation of the model.
167.  CA1 Pyramidal Neuron: Synaptic Scaling (London, Segev 2001)
London and Segev (2001) discuss location dependent and location independent synaptic scaling in a model CA1 neuron with passive dendrites. The freely available text is followed by a critique by Maggee and Cook who comment that the London and Segev model is accurate and informative and however needs to be augmented by active channels in dendrites. Note: the zip files for this model are stored at the nature neuroscience website - Click above Supplementary Source Code in the readme.html in the model files
168.  CA1 pyramidal neuron: Synaptic Scaling (Magee, Cook 2000)
Jeffrey Magee and Erik Cook found evidence in experiments and modeling that support the hypothesis that an increase in synaptic conductance for synapses at larger distances from the soma is responsible for reducing the location dependence (relative to the soma) of synapses.
169.  CA1 pyramidal neuron: synaptically-induced bAP predicts synapse location (Sterratt et al. 2012)
This is an adaptation of Poirazi et al.'s (2003) CA1 model that is used to measure BAP-induced voltage and calcium signals in spines after simulated Schaffer collateral synapse stimulation. In the model, the peak calcium concentration is highly correlated with soma-synapse distance under a number of physiologically-realistic suprathreshold stimulation regimes and for a range of dendritic morphologies. There are also simulations demonstrating that peak calcium can be used to set up a synaptic democracy in a homeostatic manner, whereby synapses regulate their synaptic strength on the basis of the difference between peak calcium and a uniform target value.
170.  CA1 pyramidal neurons: effects of Kv7 (M-) channels on synaptic integration (Shah et al. 2011)
NEURON mod files from the paper: Shah et al., 2011. In this study, using a combination of electrophysiology and computational modelling, we show that these channels selectively influence peri-somatic but not dendritic post-synaptic excitatory synaptic potential (EPSP) integration in CA1 pyramidal cells. This may be important for their relative contributions to physiological processes such as synaptic plasticity as well as patho-physiological conditions such as epilepsy.
171.  CA1 pyramidal: Stochastic amplification of KCa in Ca2+ microdomains (Stanley et al. 2011)
This minimal model investigates stochastic amplification of calcium-activated potassium (KCa) currents. Amplification results from calcium being released in short high amplitude pulses associated with the stochastic gating of calcium channels in microdomains. This model predicts that such pulsed release of calcium significantly increases subthreshold SK2 currents above what would be produced by standard deterministic models. However, there is little effect on a simple sAHP current kinetic scheme. This suggests that calcium stochasticity and microdomains should be considered when modeling certain KCa currents near subthreshold conditions.
172.  CA1 SOM+ (OLM) hippocampal interneuron (Ferguson et al. 2015)
This two-variable simple model is derived based on patch-clamp recordings from the CA1 region of a whole hippocampus preparation of SOM+ inhibitory cells. The model code will also be made available on OSB.
173.  CA1 stratum radiatum interneuron multicompartmental model (Katona et al. 2011)
The model examines dendritic NMDA-spike generation and propagation in the dendrites of CA1 stratum radiatum interneurons. It contains NMDA-channels in a clustered pattern on a dendrite and K-channels. The simulation shows the whole NMDA spike and the rising phase of the traces in separate windows.
174.  Ca2+ Oscillations in Sympathetic neurons (Friel 1995)
" ... This study focuses on caffeine-induced [Ca2+]i oscillations in sympathetic neurons. ... The aim of the study was to understand the mechanism responsible for the oscillations. As a starting point, [Ca2+]i relaxations were examined after membrane depolarization and exposure to caffeine. For both stimuli, post-stimulus relaxations could be described by the sum of two decaying exponential functions, consistent with a one-pool system in which Ca2+ transport between compartments is regulated by linear Ca2+ pumps and leaks. After modifying the store to include a [Ca2+]i-sensitive leak, the model also exhibits oscillations such as those observed experimentally. ... Thus, a one-pool model with a single [Ca2+]i-sensitive Ca2+ permeability is adequate to account for many of the quantitative properties of steady-state [Ca2+]i oscillations in sympathetic neurons. ..."
175.  Ca2+-activated I_CAN and synaptic depression promotes network-dependent oscil. (Rubin et al. 2009)
"... the preBotzinger complex... we present and analyze a mathematical model demonstrating an unconventional mechanism of rhythm generation in which glutamatergic synapses and the short-term depression of excitatory transmission play key rhythmogenic roles. Recurrent synaptic excitation triggers postsynaptic Ca2+- activated nonspecific cation current (ICAN) to initiate a network-wide burst. Robust depolarization due to ICAN also causes voltage-dependent spike inactivation, which diminishes recurrent excitation and thus attenuates postsynaptic Ca2+ accumulation. ..."
176.  CA3 pyramidal cell: rhythmogenesis in a reduced Traub model (Pinsky, Rinzel 1994)
Fig. 2A and 3 are reproduced in this simulation of Pinsky PF, Rinzel J (1994).
177.  CA3 pyramidal neuron (Lazarewicz et al 2002)
The model shows how using a CA1-like distribution of active dendritic conductances in a CA3 morphology results in dendritic initiation of spikes during a burst.
178.  CA3 Pyramidal Neuron (Migliore et al 1995)
Model files from the paper: M. Migliore, E. Cook, D.B. Jaffe, D.A. Turner and D. Johnston, Computer simulations of morphologically reconstructed CA3 hippocampal neurons, J. Neurophysiol. 73, 1157-1168 (1995). Demonstrates how the same cell could be bursting or non bursting according to the Ca-independent conductance densities. Includes calculation of intracellular Calcium. Instructions are provided in the below README file. Contact if you have any questions about the implementation of the model.
179.  CA3 Radiatum/Lacunosum-Moleculare interneuron, Ih (Anderson et al. 2011)
"The present study examines the biophysical properties and functional implications of Ih in hippocampal area CA3 interneurons with somata in strata radiatum and lacunosum-moleculare.... The functional consequences of Ih were examined with regard to temporal summation and impedance measurements. ... From impedance measurements, we found that Ih did not confer theta-band resonance, but flattened the impedance–frequency relations instead. ... Finally, a model of Ih was employed in computational analyses to confirm and elaborate upon the contributions of Ih to impedance and temporal summation."
180.  Caffeine-induced electrical oscillations in Aplysia neurons (Komendantov, Kononenko 2000)
It has been found that in cultured Aplysia neurons bath applications of 40 mM cafffeine evokes oscillations of the membrane potential with about a 40 mV amplitude with a frequency of 0.2 to 0.5 Hz. The most probable mechanism of these caffeine-induced oscillations is inhibition of voltage-activated outward potassium current and, as can be seen from our mathematical modeling, slowdown of inactivation of inward sodium current. It seems likely that these oscillations have a purely membrane origin. Please see paper for results and details.
181.  Calcium and potassium currents of olfactory bulb juxtaglomerular cells (Masurkar and Chen 2011)
Inward and outward currents of the olfactory bulb juxtaglomerular cells are characterized in the experiments and modeling in these two Masurkar and Chen 2011 papers.
182.  Calcium influx during striatal upstates (Evans et al. 2013)
"... To investigate the mechanisms that underlie the relationship between calcium and AP timing, we have developed a realistic biophysical model of a medium spiny neuron (MSN). ... Using this model, we found that either the slow inactivation of dendritic sodium channels (NaSI) or the calcium inactivation of voltage-gated calcium channels (CDI) can cause high calcium corresponding to early APs and lower calcium corresponding to later APs. We found that only CDI can account for the experimental observation that sensitivity to AP timing is dependent on NMDA receptors. Additional simulations demonstrated a mechanism by which MSNs can dynamically modulate their sensitivity to AP timing and show that sensitivity to specifically timed pre- and postsynaptic pairings (as in spike timing-dependent plasticity protocols) is altered by the timing of the pairing within the upstate. …"
183.  Calcium response prediction in the striatal spines depending on input timing (Nakano et al. 2013)
We construct an electric compartment model of the striatal medium spiny neuron with a realistic morphology and predict the calcium responses in the synaptic spines with variable timings of the glutamatergic and dopaminergic inputs and the postsynaptic action potentials. The model was validated by reproducing the responses to current inputs and could predict the electric and calcium responses to glutamatergic inputs and back-propagating action potential in the proximal and distal synaptic spines during up and down states.
184.  Calcium spikes in basal dendrites (Kampa and Stuart 2006)
This model was published in Kampa & Stuart (2006) J Neurosci 26(28):7424-32. The simulation creates two plots showing voltage and calcium changes in basal dendrites of layer 5 pyramidal neurons during action potential backpropagation. created by B. Kampa (2006)
185.  Calcium waves and mGluR-dependent synaptic plasticity in CA1 pyr. neurons (Ashhad & Narayanan 2013)
A morphologically realistic, conductance-based model equipped with kinetic schemes that govern several calcium signalling modules and pathways in CA1 pyramidal neurons
186.  Calcium waves in neuroblastoma cells (Fink et al. 2000)
Uses a model of IP3-mediated release of Ca from endoplasmic reticulum (ER) to study how initiation and propagation of Ca waves are affected by cell geometry, spatial distributions of ER and IP3 generation, and diffusion of Ca and mobile buffer.
187.  Calculating the consequences of left-shifted Nav channel activity in sick cells (Joos et al 2018)
"Two features common to diverse sick excitable cells are “leaky” Nav channels and bleb damage-damaged membranes. The bleb damage, we have argued, causes a channel kinetics based “leakiness.” Recombinant (node of Ranvier type) Nav1.6 channels voltage-clamped in mechanically-blebbed cell-attached patches undergo a damage intensity dependent kinetic change. Specifically, they experience a coupled hyperpolarizing (left) shift of the activation and inactivation processes. The biophysical observations on Nav1.6 currents formed the basis of Nav-Coupled Left Shift (Nav-CLS) theory. Node of Ranvier excitability can be modeled with Nav-CLS imposed at varying LS intensities and with varying fractions of total nodal membrane affected. Mild damage from which sick excitable cells might recover is of most interest pathologically. Accordingly, Na+/K+ ATPase (pump) activity was included in the modeling. As we described more fully in our other recent reviews, Nav-CLS in nodes with pumps proves sufficient to predict many of the pathological excitability phenomena reported for sick excitable cells. ..."
188.  Cancelling redundant input in ELL pyramidal cells (Bol et al. 2011)
The paper investigates the property of the electrosensory lateral line lobe (ELL) of the brain of weakly electric fish to cancel predictable stimuli. Electroreceptors on the skin encode all signals in their firing activity, but superficial pyramidal (SP) cells in the ELL that receive this feedforward input do not respond to constant sinusoidal signals. This cancellation putatively occurs using a network of feedback delay lines and burst-induced synaptic plasticity between the delay lines and the SP cell that learns to cancel the redundant input. Biologically, the delay lines are parallel fibres from cerebellar-like granule cells in the eminentia granularis posterior. A model of this network (e.g. electroreceptors, SP cells, delay lines and burst-induced plasticity) was constructed to test whether the current knowledge of how the network operates is sufficient to cancel redundant stimuli.
189.  Carbon nanotubes as electrical interfaces to neurons (Giugliano et al. 2008)
In the present NEURON model, we explore simple phenomenological models of the extracellular coupling, occurring at the neuron-metal microelectrode junction and (possibly) at the neuron-carbon nanotube junction.
190.  Cardiac action potentials and pacemaker activity of sinoatrial node (DiFrancesco & Noble 1985)
"Equations have been developed to describe cardiac action potentials and pacemaker activity. The model takes account of extensive developments in experimental work ..."
191.  Cardiac Atrial Cell (Courtemanche et al 1998)
Marc Courtemanche, Rafael J. Ramirez, and Stanley Nattel. Ionic mechanisms underlying human atrial action potential properties insights from a mathematical model Am J Physiol Heart Circ Physiol 1998 275: H301-H321. The implementation of this model in NEURON was contributed by Ingemar Jacobson.
192.  Cardiac Atrial Cell (Courtemanche et al 1998) (C++)
The mechanisms underlying many important properties of the human atrial action potential (AP) are poorly understood. Using specific formulations of the K+, Na+, and Ca2+ currents based on data recorded from human atrial myocytes, along with representations of pump, exchange, and background currents, we developed a mathematical model of the AP. The model AP resembles APs recorded from human atrial samples and responds to rate changes, L-type Ca2+ current blockade, Na+/Ca2+ exchanger inhibition, and variations in transient outward current amplitude in a fashion similar to experimental recordings. Rate-dependent adaptation of AP duration, an important determinant of susceptibility to atrial fibrillation, was attributable to incomplete L-type Ca2+ current recovery from inactivation and incomplete delayed rectifier current deactivation at rapid rates. Experimental observations of variable AP morphology could be accounted for by changes in transient outward current density, as suggested experimentally. We conclude that this mathematical model of the human atrial AP reproduces a variety of observed AP behaviors and provides insights into the mechanisms of clinically important AP properties.
193.  Cardiac sarcomere dynamics (Negroni and Lascano 1996)
"A muscle model establishing the link between cross-bridge dynamics and intracellular Ca2+ kinetics was assessed by simulation of experiments performed in isolated cardiac muscle. The model is composed by the series arrangement of muscle units formed by inextensible thick and thin filaments in parallel with an elastic element. Attached cross-bridges act as independent force generators whose force is linearly related to the elongation of their elastic structure. Ca2+ kinetics is described by a four-state system of sites on the thin filament associated with troponin C: sites with free troponin C (T), sites with Ca2+ bound to troponin C (TCa); sites with Ca2+ bound to troponin C and attached cross-bridges (TCa*); and sites with troponin C not associated with Ca2+ and attached cross-bridges (T*). The intracellular Ca2+ concentration ([Ca2+]) is controlled solely by the sarcoplasmic reticulum through an inflow function and a saturated outflow pump function. ..."
194.  Cell signaling/ion channel variability effects on neuronal response (Anderson, Makadia, et al. 2015)
" ... We evaluated the impact of molecular variability in the expression of cell signaling components and ion channels on electrophysiological excitability and neuromodulation. We employed a computational approach that integrated neuropeptide receptor-mediated signaling with electrophysiology. We simulated a population of neurons in which expression levels of a neuropeptide receptor and multiple ion channels were simultaneously varied within a physiological range. We analyzed the effects of variation on the electrophysiological response to a neuropeptide stimulus. ..."
195.  CellExcite: an efficient simulation environment for excitable cells (Bartocci et al. 2008)
"We have developed CellExcite, a sophisticated simulation environment for excitable-cell networks. CellExcite allows the user to sketch a tissue of excitable cells, plan the stimuli to be applied during simulation, and customize the diffusion model. CellExcite adopts Hybrid Automata (HA) as the computational model in order to efficiently capture both discrete and continuous excitable-cell behavior."
196.  Cerebellar Golgi cell (Solinas et al. 2007a, 2007b)
"... Our results suggest that a complex complement of ionic mechanisms is needed to fine-tune separate aspects of the neuronal response dynamics. Simulations also suggest that the Golgi cell may exploit these mechanisms to obtain a fine regulation of timing of incoming mossy fiber responses and granular layer circuit oscillation and bursting."
197.  Cerebellar granule cell (Masoli et al 2020)
"The cerebellar granule cells (GrCs) are classically described as a homogeneous neuronal population discharging regularly without adaptation. We show that GrCs in fact generate diverse response patterns to current injection and synaptic activation, ranging from adaptation to acceleration of firing. Adaptation was predicted by parameter optimization in detailed computational models based on available knowledge on GrC ionic channels. The models also predicted that acceleration required additional mechanisms. We found that yet unrecognized TRPM4 currents specifically accounted for firing acceleration and that adapting GrCs outperformed accelerating GrCs in transmitting high-frequency mossy fiber (MF) bursts over a background discharge. This implied that GrC subtypes identified by their electroresponsiveness corresponded to specific neurotransmitter release probability values. Simulations showed that fine-tuning of pre- and post-synaptic parameters generated effective MF-GrC transmission channels, which could enrich the processing of input spike patterns and enhance spatio-temporal recoding at the cerebellar input stage."
198.  Cerebellar nuclear neuron (Sudhakar et al., 2015)
"... In this modeling study, we investigate different forms of Purkinje neuron simple spike pause synchrony and its influence on candidate coding strategies in the cerebellar nuclei. That is, we investigate how different alignments of synchronous pauses in synthetic Purkinje neuron spike trains affect either time-locking or rate-changes in the downstream nuclei. We find that Purkinje neuron synchrony is mainly represented by changes in the firing rate of cerebellar nuclei neurons. ..."
199.  Cerebellar Nucleus Neuron (Steuber, Schultheiss, Silver, De Schutter & Jaeger, 2010)
This is the GENESIS 2.3 implementation of a multi-compartmental deep cerebellar nucleus (DCN) neuron model with a full dendritic morphology and appropriate active conductances. We generated a good match of our simulations with DCN current clamp data we recorded in acute slices, including the heterogeneity in the rebound responses. We then examined how inhibitory and excitatory synaptic input interacted with these intrinsic conductances to control DCN firing. We found that the output spiking of the model reflected the ongoing balance of excitatory and inhibitory input rates and that changing the level of inhibition performed an additive operation. Rebound firing following strong Purkinje cell input bursts was also possible, but only if the chloride reversal potential was more negative than -70 mV to allow de-inactivation of rebound currents. Fast rebound bursts due to T-type calcium current and slow rebounds due to persistent sodium current could be differentially regulated by synaptic input, and the pattern of these rebounds was further influenced by HCN current. Our findings suggest that active properties of DCN neurons could play a crucial role for signal processing in the cerebellum.
200.  Cerebellar purkinje cell (De Schutter and Bower 1994)
Tutorial simulation of a cerebellar Purkinje cell. This tutorial is based upon a GENESIS simulation of a cerebellar Purkinje cell, modeled and fine-tuned by Erik de Schutter. The tutorial assumes that you have a basic knowledge of the Purkinje cell and its synaptic inputs. It gives visual insight in how different properties as concentrations and channel conductances vary and interact within a real Purkinje cell.
201.  Cerebellar purkinje cell: interacting Kv3 and Na currents influence firing (Akemann, Knopfel 2006)
Purkinje neurons spontaneously generate action potentials in the absence of synaptic drive and thereby exert a tonic, yet plastic, input to their target cells in the deep cerebellar nuclei. Purkinje neurons express two ionic currents with biophysical properties that are specialized for high-frequency firing: resurgent sodium currents and potassium currents mediated by Kv3.3. Numerical simulations indicated that Kv3.3 increases the spontaneous firing rate via cooperation with resurgent sodium currents. We conclude that the rate of spontaneous action potential firing of Purkinje neurons is controlled by the interaction of Kv3.3 potassium currents and resurgent sodium currents. See paper for more and details.
202.  Cerebellar purkinje cell: K and Ca channels regulate APs (Miyasho et al 2001)
We adopted De Schutter and Bower's model as the starting point, then modified the descriptions of several ion channels, such as the P-type Ca channel and the delayed rectifier K channel, and added class-E Ca channels and D-type K channels to the model. Our new model reproduces most of our experimental results and supports the conclusions of our experimental study that class-E Ca channels and D-type K channels are present and functioning in the dendrites of Purkinje neurons.
203.  Cerebellar Purkinje Cell: resurgent Na current and high frequency firing (Khaliq et al 2003)
These mod files supplied by Dr Raman are for the below two references. ... we modeled action potential firing by simulating eight currents directly recorded from Purkinje cells in both wild-type and (mutant) med mice. Regular, high-frequency firing was slowed in med Purkinje neurons. In addition to disrupted sodium currents, med neurons had small but significant changes in potassium and leak currents. Simulations indicated that these modified non-sodium currents could not account for the reduced excitability of med cells but instead slightly facilitated spiking. The loss of NaV1.6-specific kinetics, however, slowed simulated spontaneous activity. Together, the data suggest that across a range of conditions, sodium currents with a resurgent component promote and accelerate firing. See papers for more and details.
204.  Cerebellar stellate cells: changes in threshold, latency and frequency of firing (Mitry et al 2020)
"Cerebellar stellate cells are inhibitory molecular interneurons that regulate the firing properties of Purkinje cells, the sole output of cerebellar cortex. Recent evidence suggests that these cells exhibit temporal increase in excitability during whole-cell patch-clamp configuration in a phenomenon termed runup. They also exhibit a non-monotonic first-spike latency profile as a function of the holding potential in response to a fixed step-current. In this study, we use modeling approaches to unravel the dynamics of runup and categorize the firing behavior of cerebellar stellate cells as either type I or type II oscillators. We then extend this analysis to investigate how the non-monotonic latency profile manifests itself during runup. We employ a previously developed, but revised, Hodgkin–Huxley type model to show that stellate cells are indeed type I oscillators possessing a saddle node on an invariant cycle (SNIC) bifurcation. The SNIC in the model acts as a “threshold” for tonic firing and produces a slow region in the phase space called the ghost of the SNIC. The model reveals that (i) the SNIC gets left-shifted during runup with respect to I app = I test in the current-step protocol, and (ii) both the distance from the stable limit cycle along with the slow region produce the non-monotonic latency profile as a function of holding potential. Using the model, we elucidate how latency can be made arbitrarily large for a specific range of holding potentials close to the SNIC during pre-runup (post-runup). We also demonstrate that the model can produce transient single spikes in response to step- currents entirely below I SNIC , and that a pair of dynamic inhibitory and excitatory post- synaptic inputs can robustly evoke action potentials, provided that the magnitude of the inhibition is either low or high but not intermediate. Our results show that the topology of the SNIC is the key to explaining such behaviors."
205.  Channel density variability among CA1 neurons (Migliore et al. 2018)
The peak conductance of many ion channel types measured in any given animal is highly variable across neurons, both within and between neuronal populations. The current view is that this occurs because a neuron needs to adapt its intrinsic electrophysiological properties either to maintain the same operative range in the presence of abnormal inputs or to compensate for the effects of pathological conditions. Limited experimental and modeling evidence suggests this might be implemented via the correlation and/or degeneracy in the function of multiple types of conductances. To study this mechanism in hippocampal CA1 neurons and interneurons, we systematically generated a set of morphologically and biophysically accurate models. We then analyzed the ensembles of peak conductance obtained for each model neuron. The results suggest that the set of conductances expressed in the various neuron types may be divided into two groups: one group is responsible for the major characteristics of the firing behavior in each population and the other more involved with degeneracy. These models provide experimentally testable predictions on the combination and relative proportion of the different conductance types that should be present in hippocampal CA1 pyramidal cells and interneurons.
206.  Chirp stimulus responses in a morphologically realistic model (Narayanan and Johnston, 2007)
...we built a multicompartmental model with a morphologically realistic three-dimensional reconstruction of a CA1 pyramidal neuron. The only active conductance we added to the model was the h conductance. ... We conclude that experimentally observed gradient in density of h channels could theoretically account for experimentally observed gradient in resonance properties (Narayanan and Johnston, 2007).
207.  Cholinergic and nicotinic regulation of DA neuron firing (Morozova et al 2020)
The model describes the modulation of firing properties of DA neurons by acetylcholine (ACh) and nicotine in 5 cases: knock-out of ß2-containing nAChRs, ß2-containing nAChRs only on DA neurons, the nAChRs only on GABA neurons, the nAChRs on both DA and GABA neurons and “wild” type (the AChRs on DA, GABA and Glu neurons). The distinct responses to ACh and nicotine could be explained by distinct temporal patterns of these inputs: pulsatile vs. continuous.
208.  ClC-2 channels regulate neuronal excitability, not intracellular Cl- levels (Ratte & Prescott 2011)
"The model is for a generic, single compartment neuron with multiple ion currents. The most notable mechanisms include ClC-2 (a rectifying chloride-leak channel) and KCC2 (potassium chloride co-transporter 2). A significant feature of the model is that it tracks intracellular chloride concentration. Moreover, the GABA-A receptor is modeled as passing both chloride and bicarbonate ions, which is important for proper calculation of the GABA reversal potential. Ornstein-Unlenbeck processes to simulate synaptic inhibition and excitation are also included."
209.  CN bushy, stellate neurons (Rothman, Manis 2003)
Using kinetic data from three different K+ currents in acutely isolated neurons, a single electrical compartment model representing the soma of a ventral cochlear nucleus (VCN) neuron was created. The K+ currents include a fast transient current (IA), a slow-inactivating low-threshold current (ILT), and a noninactivating high-threshold current (IHT). The model also includes a fast-inactivating Na+ current, a hyperpolarization-activated cation current (Ih), and 1-50 auditory nerve synapses. With this model, the role IA, ILT, and IHT play in shaping the discharge patterns of VCN cells is explored. Simulation results indicate these currents have specific roles in shaping the firing patterns of stellate and bushy CN cells. (see readme.txt and the papers, esp 2003c, for details). Any questions regarding these implementations should be directed to: 2 April 2004 Paul B Manis, Ph.D.
210.  CN bushy, stellate neurons (Rothman, Manis 2003) (Brian 2)
This model is an updated version of Romain Brette's adaptation of Rothman & Manis (2003). The model now uses Brian 2 instead of Brian 1 and can be configured to use n cells instead of a single cell. The included figure shows that Brian 2 is more efficient than Brian 1 once the number of cells exceeds 1,000.
211.  CN bushy, stellate neurons (Rothman, Manis 2003) (Brian)
Cochlear neuron model of Rothman & Manis (2003). Adapted from the Neuron implementation.
212.  CN pyramidal fusiform cell (Kanold, Manis 2001)
Pyramidal cells in the dorsal cochlear nucleus (DCN) show three characteristic discharge patterns in response tones: pauser, buildup, and regular firing. Experimental evidence suggests that a rapidly inactivating K+ current (I(KIF)) plays a critical role in generating these discharge patterns. To explore the role of I(KIF), we used a computational model based on the biophysical data. The model replicated the dependence of the discharge pattern on the magnitude and duration of hyperpolarizing prepulses, and I(KIF) was necessary to convey this dependence. Experimentally, half-inactivation voltage and kinetics of I(KIF) show wide variability. Varying these parameters in the model ... suggests that pyramidal cells can adjust their sensitivity to different temporal patterns of inhibition and excitation by modulating the kinetics of I(KIF). Overall, I(KIF) is a critical conductance controlling the excitability of DCN pyramidal cells. (See readme.txt and paper for details). Any questions regarding these implementations should be directed to: 2 April 2004 Paul B Manis, Ph.D.
213.  Cochlea: inner ear models in Python (Zilany et al 2009, 2014; Holmberg M 2007)
Collection of inner ear models in Python.
214.  Coincidence detection in avian brainstem (Simon et al 1999)
A detailed biophysical model of coincidence detector neurons in the nucleus laminaris (auditory brainstem) which are purported to detect interaural time differences (ITDs) from Simon et al 1999.
215.  Combination sensitivity and active conductances (Carlson and Kawasaki 2006)
"... The weakly electric fish Gymnarchus discriminates the sign of the frequency difference (Df) between a neighbor’s electric organ discharge (EOD) and its own EOD by comparing temporal patterns of amplitude modulation (AM) and phase modulation (PM). Sign-selective neurons in the midbrain respond preferentially to either positive frequency differences (Df >0 selective) or negative frequency differences (Df <0 selective). To study the mechanisms of combination sensitivity, we made whole cell intracellular recordings from sign-selective midbrain neurons in vivo and recorded postsynaptic potential (PSP) responses to AM, PM, Df >0, and Df <0. ... Responses to the nonpreferred sign of Df, but not the preferred sign of Df, were substantially weaker than linear predictions, causing a significant increase in the actual degree of sign selectivity. By using various levels of current clamp and comparing our results to simple models of synaptic integration, we demonstrate that this decreased response to the nonpreferred sign of Df is caused by a reduction in voltage-dependent excitatory conductances. This finding reveals that nonlinear decoders, in the form of voltage-dependent conductances, can enhance the selectivity of single neurons for particular combinations of stimulus attributes." See paper for more and details.
216.  Comparison of DA-based Stochastic Algorithms (Pezo et al. 2014)
" ... Here we review and test a set of the most recently published DA (Langevin-based Diffusion Approximation) implementations (Goldwyn et al., 2011; Linaro et al., 2011; Dangerfield et al., 2012; Orio and Soudry, 2012; Schmandt and Galán, 2012; Güler, 2013; Huang et al., 2013a), comparing all of them in a set of numerical simulations that asses numerical accuracy and computational efficiency on three different models: the original Hodgkin and Huxley model, a model with faster sodium channels, and a multi-compartmental model inspired in granular cells. ..."
217.  Comparison of full and reduced globus pallidus models (Hendrickson 2010)
In this paper, we studied what features of realistic full model activity patterns can and cannot be preserved by morphologically reduced models. To this end, we reduced the morphological complexity of a full globus pallidus neuron model possessing active dendrites and compared its spontaneous and driven responses to those of the reduced models.
218.  Compartmental model of a mitral cell (Popovic et al. 2005)
Usage of a morphologically realistic compartmental model of a mitral cell and data obtained from whole-cell patch-clamp and voltage-imaging experiments in order to explore passive parameter space in which reported low EPSP attenuation is observed.
219.  Competition for AP initiation sites in a circuit controlling simple learning (Cruz et al. 2007)
"The spatial and temporal patterns of action potential initiations were studied in a behaving leech preparation to determine the basis of increased firing that accompanies sensitization, a form of non-associative learning requiring the S-interneurons. ... The S-interneurons, one in each ganglion and linked by electrical synapses with both neighbors to form a chain, are interposed between sensory and motor neurons. ... the single site with the largest initiation rate, the S-cell in the stimulated segment, suppressed initiations in adjacent ganglia. Experiments showed this was both because (1) it received the earliest, greatest input and (2) the delayed synaptic input to the adjacent S-cells coincided with the action potential refractory period. A compartmental model of the S-cell and its inputs showed that a simple, intrinsic mechanism of inexcitability after each action potential may account for suppression of impulse initiations. Thus, a non-synaptic competition between neurons alters synaptic integration in the chain. In one mode, inputs to different sites sum independently, whereas in another, synaptic input to a single site precisely specifies the overall pattern of activity."
220.  Complex CA1-neuron to study AP initiation (Wimmer et al. 2010)
Complex model of a pyramidal CA1-neuron, adapted from Royeck, M., et al. Role of axonal NaV1.6 sodium channels in action potential initiation of CA1 pyramidal neurons. Journal of neurophysiology 100, 2361-2380 (2008). It contains a biophysically realistic morphology comprising 265 compartments (829 segments) and 15 different distributed Ca2+- and/or voltage-dependent conductances.
221.  Complex dynamics: reproducing Golgi cell electroresponsiveness (Geminiani et al 2018, 2019ab)
Excerpts from three papers abstracts: "Brain neurons exhibit complex electroresponsive properties – including intrinsic subthreshold oscillations and pacemaking, resonance and phase-reset – which are thought to play a critical role in controlling neural network dynamics. Although these properties emerge from detailed representations of molecular-level mechanisms in “realistic” models, they cannot usually be generated by simplified neuronal models (although these may show spike-frequency adaptation and bursting). We report here that this whole set of properties can be generated by the extended generalized leaky integrate-and-fire (E-GLIF) neuron model. ..." "... In order to reproduce these properties in single-point neuron models, we have optimized the Extended-Generalized Leaky Integrate and Fire (E-GLIF) neuron through a multi-objective gradient-based algorithm targeting the desired input–output relationships. ..." " ... In order to investigate how single neuron dynamics and geometrical modular connectivity affect cerebellar processing, we have built an olivocerebellar Spiking Neural Network (SNN) based on a novel simplification algorithm for single point models (Extended Generalized Leaky Integrate and Fire, EGLIF) capturing essential non-linear neuronal dynamics (e.g., pacemaking, bursting, adaptation, oscillation and resonance). ..."
222.  Comprehensive models of human cortical pyramidal neurons (Eyal et al 2018)
"We present detailed models of pyramidal cells from human neocortex, including models on their excitatory synapses, dendritic spines, dendritic NMDA- and somatic/axonal Na+ spikes that provided new insights into signal processing and computational capabilities of these principal cells. Six human layer 2 and layer 3 pyramidal cells (HL2/L3 PCs) were modeled, integrating detailed anatomical and physiological data from both fresh and postmortem tissues from human temporal cortex. The models predicted particularly large AMPA- and NMDA- conductances per synaptic contact (0.88 nS and 1.31nS, respectively) and a steep dependence of the NMDA-conductance on voltage..."
223.  Computational model of bladder small DRG neuron soma (Mandge & Manchanda 2018)
Bladder small DRG neurons, which are putative nociceptors pivotal to urinary bladder function, express more than a dozen different ionic membrane mechanisms: ion channels, pumps and exchangers. Small-conductance Ca2+-activated K+ (SKCa) channels which were earlier thought to be gated solely by intracellular Ca2+ concentration ([Ca]i ) have recently been shown to exhibit inward rectification with respect to membrane potential. The effect of SKCa inward rectification on the excitability of these neurons is unknown. Furthermore, studies on the role of KCa channels in repetitive firing and their contributions to different types of afterhyperpolarization (AHP) in these neurons are lacking. In order to study these phenomena, we first constructed and validated a biophysically detailed single compartment model of bladder small DRG soma constrained by physiological data. The model includes twenty-two major known membrane mechanisms along with intracellular Ca2+ dynamics comprising Ca2+ diffusion, cytoplasmic buffering, and endoplasmic reticulum (ER) and mitochondrial mechanisms. Using modelling studies, we show that inward rectification of SKCa is an important parameter regulating neuronal repetitive firing and that its absence reduces action potential (AP) firing frequency. We also show that SKCa is more potent in reducing AP spiking than the large-conductance KCa channel (BKCa) in these neurons. Moreover, BKCa was found to contribute to the fast AHP (fAHP) and SKCa to the medium-duration (mAHP) and slow AHP (sAHP). We also report that the slow inactivating A-type K+ channel (slow KA) current in these neurons is composed of 2 components: an initial fast inactivating (time constant ~ 25-100 ms) and a slow inactivating (time constant ~ 200-800 ms) current. We discuss the implications of our findings, and how our detailed model can help further our understanding of the role of C-fibre afferents in the physiology of urinary bladder as well as in certain disorders.
224.  Computational modeling of ultrasonic Subthalamic Nucleus stimulation (Tarnaud et al 2019)
"Objective: To explore the potential of ultrasonic modulation of plateau-potential generating subthalamic nucleus neurons (STN), by modeling their interaction with continuous and pulsed ultrasonic waves. Methods: A computational model for ultrasonic stimulation of the STN is created by combining the Otsuka-model with the bilayer sonophore model. The neuronal response to continuous and pulsed ultrasonic waves is computed in parallel for a range of frequencies, duty cycles, pulse repetition frequencies, and intensities. ..."
225.  Computational modelling of channelrhodopsin-2 photocurrent characteristics (Stefanescu et al. 2013)
The codes are directly related with the results presented in the manuscript; in brief, it is a computational investigation on the effects of optogenetic actuation on excitatory and inhibitory neurons when 3- and 4- state model is used to implement the ChR2 kinetics. Different parameters of optostimulation are investigated and the results compared with experimental data previously published by other research groups.
226.  Computational neuropharmacology of CA1 pyramidal neuron (Ferrante et al. 2008)
In this paper, the model was used to show how neuroactive drugs targeting different neuronal mechanisms affect the signal integration in CA1 pyramidal neuron. Ferrante M, Blackwell KT, Migliore M, Ascoli GA (2008)
227.  Computer models of corticospinal neurons replicate in vitro dynamics (Neymotin et al. 2017)
"Corticospinal neurons (SPI), thick-tufted pyramidal neurons in motor cortex layer 5B that project caudally via the medullary pyramids, display distinct class-specific electrophysiological properties in vitro: strong sag with hyperpolarization, lack of adaptation, and a nearly linear frequency-current (FI) relationship. We used our electrophysiological data to produce a pair of large archives of SPI neuron computer models in two model classes: 1. Detailed models with full reconstruction; 2. Simplified models with 6 compartments. We used a PRAXIS and an evolutionary multiobjective optimization (EMO) in sequence to determine ion channel conductances. ..."
228.  Computer simulations of neuron-glia interactions mediated by ion flux (Somjen et al. 2008)
"... To examine the effect of glial K+ uptake, we used a model neuron equipped with Na+, K+, Ca2+ and Cl&#8722; conductances, ion pumps and ion exchangers, surrounded by interstitial space and glia. The glial membrane was either “passive”, incorporating only leak channels and an ion exchange pump, or it had rectifying K+ channels. We computed ion fluxes, concentration changes and osmotic volume changes. ... We conclude that voltage gated K+ currents can boost the effectiveness of the glial “potassium buffer” and that this buffer function is important even at moderate or low levels of excitation, but especially so in pathological states."
229.  Conditions of dominant effectiveness of distal dendrites (Korogod, Kulagina 1998)
The model illustrates and explains bistable spatial patterns of the current transfer effectiveness in the active dendrite with distributed (multiple) tonic excitatory, NMDA type, synaptic input.
230.  Conductance-based model of rodent thoracic sympathetic postganglionic neuron (McKinnon et al 2019)
"Thoracic sympathetic postganglionic neurons (tSPNs) represent the final neural output for control of vasomotor and thermoregulatory function. We used whole-cell recordings and computational modeling to provide broad insight on intrinsic cellular mechanisms controlling excitability and capacity for synaptic integration. Compared to past intracellular recordings using microelectrode impalement, we observed dramatically higher membrane resistivity with primacy in controlling enhanced tSPN excitability and recruitment via synaptic integration. Compared to reported phasic firing, all tSPNs fire repetitively and linearly encode injected current magnitude to firing frequency over a broad range. Modeling studies suggest microelectrode impalement injury accounts for differences in tSPN properties previously observed. Overall, intrinsic tSPN excitability plays a much greater role in the integration and maintenance of sympathetic output than previously thought."
231.  Constructed Tessellated Neuronal Geometries (CTNG) (McDougal et al. 2013)
We present an algorithm to form watertight 3D surfaces consistent with the point-and-diameter based neuronal morphology descriptions widely used with spatial electrophysiology simulators. ... This (point-and-diameter) representation is well-suited for electrophysiology simulations, where the space constants are larger than geometric ambiguities. However, the simple interpretations used for pure electrophysiological simulation produce geometries unsuitable for multi-scale models that also involve three-dimensional reaction–diffusion, as such models have smaller space constants. ... Although one cannot exactly reproduce an original neuron's full shape from point-and-diameter data, our new constructive tessellated neuronal geometry (CTNG) algorithm uses constructive solid geometry to define a plausible reconstruction without gaps or cul-de-sacs. CTNG then uses “constructive cubes” to produce a watertight triangular mesh of the neuron surface, suitable for use in reaction–diffusion simulations. ..."
232.  Contibutions of input and history to motoneuron output (Powers et al 2005)
"The present study presents results based on recordings of noise-driven discharge in rat hypoglossal motoneurones ... First, we show that the hyperpolarizing trough is larger in Average Current Trajectories (ACTs) calculated from spikes preceded by long interspike intervals, and minimal or absent in those based on short interspike intervals. Second, we show that the trough is present for ACTs calculated from the discharge of a threshold-crossing neurone model with a postspike after- hyperpolarization (AHP), but absent from those calculated from the discharge of a model without an AHP. We show that it is possible to represent noise-driven discharge using a two-component linear model that predicts discharge probability based on the sum of a feedback kernel and a stimulus kernel. The feedback kernel reflects the influence of prior discharge mediated by the AHP, and it increases in amplitude when AHP amplitude is increased by pharmacological manipulations. Finally, we show that the predictions of this model are virtually identical to those based on the first-order Wiener kernel. This suggests that the Wiener kernel derived from standard white-noise analysis of noise-driven discharge in neurones actually reflect the effects of both stimulus and discharge history." See paper for more and details.
233.  Contrast invariance by LGN synaptic depression (Banitt et al. 2007)
"Simple cells in layer 4 of the primary visual cortex of the cat show contrast-invariant orientation tuning, in which the amplitude of the peak response is proportional to the stimulus contrast but the width of the tuning curve hardly changes with contrast. This study uses a detailed model of spiny stellate cells (SSCs) from cat area 17 to explain this property. The model integrates our experimental data, including morphological and intrinsic membrane properties and the number and spatial distribution of four major synaptic input sources of the SSC: the dorsal lateral geniculate nucleus (dLGN) and three cortical sources. ... The model response is in close agreement with experimental results, in terms of both output spikes and membrane voltage (amplitude and fluctuations), with reasonable exceptions given that recurrent connections were not incorporated."
234.  Contribution of the axon initial segment to APs recorded extracellularly (Telenczuk et al 2018)
"... It was recently proposed that at onset of an (Action Potential) AP the soma and the (Axon Initial Segment) AIS form a dipole. We study the extracellular signature (the extracellular action potential, EAP) generated by such a dipole. First, we demonstrate the formation of the dipole and its extracellular signature in detailed morphological models of a reconstructed pyramidal neuron. Then, we study the EAP waveform and its spatial dependence in models with axonal AP initiation and contrast it with the EAP obtained in models with somatic AP initiation. We show that in the models with axonal AP initiation the dipole forms between somatodendritic compartments and the AIS, and not between soma and dendrites as in the classical models. ..."
235.  Control of oscillations and spontaneous firing in dopamine neurons (Rumbell & Kozloski 2019)
Model of Substantia Nigra pars Compacta Dopamine Neuron. 'Toy' morphology with 4 dendrites, one of which is the axon-bearing dendrite, with an axon branching from it. The axon is a short 'axon initial segment' compartment, followed by a longer 'axon'. 727 parameter sets for ion channel conductance and kinetic parameters were found using evolutionary optimization, all of which are viable candidates representing a plausible model of a SNc DA.
236.  Control of vibrissa motoneuron firing (Harish and Golomb 2010)
We construct and analyze a single-compartment, conductance-based model of vibrissa motoneurons. Low firing rates are supported in extended regimes by adaptation currents and the minimal firing rate decreases with the persistent sodium conductance gNaP and increases with M-potassium and h-cation conductances. Suprathreshold resonance results from the locking properties of vMN firing to stimuli and from reduction of firing rates at low frequencies by slow M and afterhyperpolarization potassium conductances. h conductance only slightly affects the suprathreshold resonance. When a vMN is subjected to a small periodic CPG input, serotonergically induced gNaP elevation may transfer the system from quiescence to a firing state that is highly locked to the CPG input.
237.  Correcting space clamp in dendrites (Schaefer et al. 2003 and 2007)
In voltage-clamp experiments, incomplete space clamp distorts the recorded currents, rendering accurate analysis impossible. Here, we present a simple numerical algorithm that corrects such distortions. The method enabled accurate retrieval of the local densities, kinetics, and density gradients of somatic and dendritic channels. The correction method was applied to two-electrode voltage-clamp recordings of K currents from the apical dendrite of layer 5 neocortical pyramidal neurons. The generality and robustness of the algorithm make it a useful tool for voltage-clamp analysis of voltage-gated currents in structures of any morphology that is amenable to the voltage-clamp technique.
238.  Cortical Layer 5b pyr. cell with [Na+]i mechanisms, from Hay et al 2011 (Zylbertal et al 2017)
" ... Based on a large body of experimental recordings from both the soma and dendrites of L5b pyramidal cells in adult rats, we characterized key features of the somatic and dendritic firing and quantified their statistics. We used these features to constrain the density of a set of ion channels over the soma and dendritic surface via multi-objective optimization with an evolutionary algorithm, thus generating a set of detailed conductance-based models that faithfully replicate the back-propagating action potential activated Ca(2+) spike firing and the perisomatic firing response to current steps, as well as the experimental variability of the properties. Furthermore, we show a useful way to analyze model parameters with our sets of models, which enabled us to identify some of the mechanisms responsible for the dynamic properties of L5b pyramidal cells as well as mechanisms that are sensitive to morphological changes. ..."
239.  Cortical network model of posttraumatic epileptogenesis (Bush et al 1999)
This simulation from Bush, Prince, and Miller 1999 shows the epileptiform response (Fig. 6C) to a brief single stimulation in a 500 cell network of multicompartment models, some of which have active dendrites. The results which I obtained under Redhat Linux is shown in result.gif. Original 1997 code from Paul Bush modified slightly by Bill Lytton to make it work with current version of NEURON (5.7.139). Thanks to Paul Bush and Ken Miller for making the code available.
240.  Cortico-striatal plasticity in medium spiny neurons (Gurney et al 2015)
In the associated paper (Gurney et al, PLoS Biology, 2015) we presented a computational framework that addresses several issues in cortico-striatal plasticity including spike timing, reward timing, dopamine level, and dopamine receptor type. Thus, we derived a complete model of dopamine and spike-timing dependent cortico-striatal plasticity from in vitro data. We then showed this model produces the predicted activity changes necessary for learning and extinction in an operant task. Moreover, we showed the complex dependencies of cortico-striatal plasticity are not only sufficient but necessary for learning and extinction. The model was validated in a wider setting of action selection in basal ganglia, showing how it could account for behavioural data describing extinction, renewal, and reacquisition, and replicate in vitro experimental data on cortico-striatal plasticity. The code supplied here allows reproduction of the proposed process of learning in medium spiny neurons, giving the results of Figure 7 of the paper.
241.  Criticality,degeneracy in injury-induced changes in primary afferent excitability (Ratte et al 2014)
"Neuropathic pain remains notoriously difficult to treat despite numerous drug targets. Here, we offer a novel explanation for this intractability. Computer simulations predicted that qualitative changes in primary afferent excitability linked to neuropathic pain arise through a switch in spike initiation dynamics when molecular pathologies reach a tipping point (criticality), and that this tipping point can be reached via several different molecular pathologies (degeneracy). ..."
242.  Currents contributing to decision making in neurons B31-B32 of Aplysia (Hurwitz et al. 2008)
"Biophysical properties of neurons contributing to the ability of an animal to decide whether or not to respond were examined. B31/B32, two pairs of bilaterally symmetrical Aplysia neurons, are major participants in deciding to initiate a buccal motor program, the neural correlate of a consummatory feeding response. B31/B32 respond to an adequate stimulus after a delay, during which time additional stimuli influence the decision to respond. B31/B32 then respond with a ramp depolarization followed by a sustained soma depolarization and axon spiking that is the expression of a commitment to respond to food. Four currents contributing to decision making in B31/B32 were characterized, and their functional effects were determined, in current- and voltage-clamp experiments and with simulations. ... Hodgkin-Huxley kinetic analyses were performed on the outward currents. Simulations using equations from these analyses showed that IK-V and IK-A slow the ramp depolarization preceding the sustained depolarization. The three outward currents contribute to braking the B31/B32 depolarization and keeping the sustained depolarization at a constant voltage. The currents identified are sufficient to explain the properties of B31/B32 that play a role in generating the decision to feed."
243.  Cytoplasmic electric fields and electroosmosis (Andreev 2013)
The paper presents two mathematical models describing the role of electroosmosis in the transport of the negatively charged messenger proteins to the negatively charged nucleus and in the recovery of the fluorescence after photobleaching. The parameters of the models were derived from the extensive review of the literature data. Computer simulations were performed within the COMSOL 4.2a software environment. The first model demonstrated that the presence of electroosmosis might intensify the flux of messenger proteins to the nucleus and allow the efficient transport of the negatively charged phosphorylated messenger proteins against the electrostatic repulsion of the negatively charged nucleus. The second model revealed that the presence of the electroosmotic flow made the time of fluorescence recovery dependent on the position of the bleaching spot relative to cellular membrane.
244.  D2 dopamine receptor modulation of interneuronal activity (Maurice et al. 2004)
"... Using a combination of electrophysiological, molecular, and computational approaches, the studies reported here show that D2 dopamine receptor modulation of Na+ currents underlying autonomous spiking contributes to a slowing of discharge rate, such as that seen in vivo. Four lines of evidence support this conclusion. ... Fourth, simulation of cholinergic interneuron pacemaking revealed that a modest increase in the entry of Na+ channels into the slow-inactivated state was sufficient to account for the slowing of pacemaker discharge. These studies establish a cellular mechanism linking dopamine and the reduction in striatal cholinergic interneuron activity seen in the initial stages of associative learning." See paper for more and details.
245.  Data-driven, HH-type model of the lateral pyloric (LP) cell in the STG (Nowotny et al. 2008)
This model was developed using voltage clamp data and existing LP models to assemble an initial set of currents which were then adjusted by extensive fitting to a long data set of an isolated LP neuron. The main points of the work are a) automatic fitting is difficult but works when the method is carefully adjusted to the problem (and the initial guess is good enough). b) The resulting model (in this case) made reasonable predictions for manipulations not included in the original data set, e.g., blocking some of the ionic currents. c) The model is reasonably robust against changes in parameters but the different parameters vary a lot in this respect. d) The model is suitable for use in a network and has been used for this purpose (Ivanchenko et al. 2008)
246.  DBS of a multi-compartment model of subthalamic nucleus projection neurons (Miocinovic et al. 2006)
We built a comprehensive computational model of subthalamic nucleus (STN) deep brain stimulation (DBS) in parkinsonian macaques to study the effects of stimulation in a controlled environment. The model consisted of three fundamental components: 1) a three-dimensional (3D) anatomical model of the macaque basal ganglia, 2) a finite element model of the DBS electrode and electric field transmitted to the tissue medium, and 3) multicompartment biophysical models of STN projection neurons, GPi fibers of passage, and internal capsule fibers of passage. Populations of neurons were positioned within the 3D anatomical model. Neurons were stimulated with electrode positions and stimulation parameters defined as clinically effective in two parkinsonian monkeys. The model predicted axonal activation of STN neurons and GPi fibers during STN DBS. Model predictions regarding the degree of GPi fiber activation matched well with experimental recordings in both monkeys.
247.  DCN fusiform cell (Ceballos et al. 2016)
Dorsal cochlear nucleus principal neurons, fusiform neurons, display heterogeneous spontaneous action potential activity and thus represent an appropriate model to study the role of different conductances in establishing firing heterogeneity. Particularly, fusiform neurons are divided into quiet, with no spontaneous firing, or active neurons, presenting spontaneous, regular firing. These modes are determined by the expression levels of an intrinsic membrane conductance, an inwardly rectifying potassium current (IKir). We used a computational model to test whether other subthreshold conductances vary homeostatically to maintain membrane excitability constant across the two subtypes. We found that Ih expression covaries specifically with IKir in order to maintain membrane resistance constant. The impact of Ih on membrane resistance is dependent on the level of IKir expression, being much smaller in quiet neurons with bigger IKir, but Ih variations are not relevant for creating the quiet and active phenotypes. We conclude that in fusiform neurons the variations of their different subthreshold conductances are limited to specific conductances in order to create firing heterogeneity and maintain membrane homeostasis.
248.  Dendrites enable a robust mechanism for neuronal stimulus selectivity (Caze et al 2017)
"... Using a multi-subunit nonlinear model, we demonstrate that stimulus selectivity can arise from the spatial distribution of synapses. We propose this as a general mechanism for information processing by neurons possessing dendritic trees. Moreover, we show that this implementation of stimulus selectivity increases the neuron's robustness to synaptic and dendritic failure. ..."
249.  Dendritic Discrimination of Temporal Input Sequences (Branco et al. 2010)
Compartmental model of a layer 2/3 pyramidal cell in the rat somatosensory cortex, exploring NMDA-dependent sensitivity to the temporal sequence of synaptic activation.
250.  Dendritic L-type Ca currents in motoneurons (Carlin et al 2000)
A component of recorded currents demonstrated kinetics consistent with a current originating at a site spatially segregated from the soma. In response to step commands this component was seen as a late-onset, low amplitude persistent current whilst in response to depolarizing-repolarizing ramp commands a low voltage clockwise current hysteresis was recorded. Simulations using a neuromorphic motoneuron model could reproduce these currents only if a noninactivating calcium conductance was placed in the dendritic compartments.
251.  Dendritic Na inactivation drives a decrease in ISI (Fernandez et al 2005)
We use a combination of dynamical analysis and electrophysiological recordings to demonstrate that spike broadening in dendrites is primarily caused by a cumulative inactivation of dendritic Na(+) current. We further show that a reduction in dendritic Na(+) current increases excitability by decreasing the interspike interval (ISI) and promoting burst firing.
252.  Dendritic Na+ spike initiation and backpropagation of APs in active dendrites (Nevian et al. 2007)
NEURON model used to create simulations shown in figure 6 of the paper. The model includes two point processes; one for dendritic spike initiation and the other for somatic action potential generation. The effect of filtering by imperfect recording electrode can be examined in somatic and dendritic locations.
253.  Dendritic processing of excitatory synaptic input in GnRH neurons (Roberts et al. 2006)
"... we used electrophysiological recordings and neuronal reconstructions to generate computer models of (Gonadotopin-Releasing Hormone) GnRH neurons to examine the effects of synaptic inputs at varying distances from the soma along dendrites. ... analysis of reduced morphology models indicated that this population of cells is unlikely to exhibit low-frequency tonic spiking in the absence of synaptic input. ... applying realistic patterns of synaptic input to modeled GnRH neurons indicates that synapses located more than about 30% of the average dendrite length from the soma cannot drive firing at frequencies consistent with neuropeptide release. Thus, processing of synaptic input to dendrites of GnRH neurons is probably more complex than simple summation."
254.  Dendritic signals command firing dynamics in a Cerebellar Purkinje Cell model (Genet et al. 2010)
This model endows the dendrites of a reconstructed Purkinje cells (PC) with the mechanism of Ca-dependent plateau potentials and spikes described in Genet, S., and B. Delord. 2002. A biophysical model of nonlinear dynamics underlying plateau potentials and calcium spikes in Purkinje cell dendrites. J. Neurophysiol. 88:2430–2444). It is a part of a comprehensive mathematical study suggesting that active electric signals in the dendrites of PC command epochs of firing and silencing of the PC soma.
255.  Dendritic spikes enhance stimulus selectivity in cortical neurons in vivo (Smith et al 2013)
"Neuronal dendrites are electrically excitable: they can generate regenerative events such as dendritic spikes in response to sufficiently strong synaptic input. Although such events have been observed in many neuronal types, it is not well understood how active dendrites contribute to the tuning of neuronal output in vivo. Here we show that dendritic spikes increase the selectivity of neuronal responses to the orientation of a visual stimulus (orientation tuning). ...".
256.  Dendritic tip geometry effects electrical properties (Tsutsui, Oka 2001)
In their teleost thalamic neuron models the authors demonstrate a dramatic increase in the passive propagation of synaptic inputs through the dendritic stalk to the soma in cells with larger tips.
257.  Dentate Basket Cell: spatial summation of inhibitory synaptic inputs (Bartos et al 2001)
Spatial summation of inhibitory synaptic input in a passive model of a basket cell from the dentate gyrus of rat hippocampus. Reproduces Figs. 5Ac and d in Bartos, M., Vida, I., Frotscher, M., Geiger, J.R.P, and Jonas, P.. Rapid signaling at inhibitory synapses in a dentate gyrus interneuron network. Journal of Neuroscience 21:2687-2698, 2001.
258.  Dentate granule cell: mAHP & sAHP; SK & Kv7/M channels (Mateos-Aparicio et al., 2014)
The model is based on that of Aradi & Holmes (1999; Journal of Computational Neuroscience 6, 215-235). It was used to help understand the contribution of M and SK channels to the medium afterhyperpolarization (mAHP) following one or seven spikes, as well as the contribution of M channels to the slow afterhyperpolarization (sAHP). We found that SK channels are the main determinants of the mAHP, in contrast to CA1 pyramidal cells where the mAHP is primarily caused by the opening of M channels. The model reproduced these experimental results, but we were unable to reproduce the effects of the M-channel blocker XE991 on the sAHP. It is suggested that either the XE991-sensitive component of the sAHP is not due to M channels, or that when contributing to the sAHP, these channels operate in a mode different from that associated with the mAHP.
259.  Dentate Gyrus Feed-forward inhibition (Ferrante et al. 2009)
In this paper, the model was used to show how that FFI can change a steeply sigmoidal input-output (I/O) curve into a double-sigmoid typical of buffer systems.
260.  Dentate gyrus granule cell: calcium and calcium-dependent conductances (Aradi and Holmes 1999)
We have constructed a detailed model of a hippocampal dentate granule (DG) cell that includes nine different channel types. Channel densities and distributions were chosen to reproduce reported physiological responses observed in normal solution and when blockers were applied. The model was used to explore the contribution of each channel type to spiking behavior with particular emphasis on the mechanisms underlying postspike events. ... The model was used to predict changes in channel densities that could lead to epileptogenic burst discharges and to predict the effect of altered buffering capacity on firing behavior. We conclude that the clustered spatial distributions of calcium related channels, the presence of slow delayed rectifier potassium currents in dendrites, and calcium buffering properties, together, might explain the resistance of DG cells to the development of epileptogenic burst discharges.
261.  Dentate gyrus granule cell: subthreshold signal processing (Schmidt-Hieber et al. 2007)
Detailed compartmental cable models of 8 hippocampal granule cells of adult mice were obtained from dual patch-clamp whole-cell recordings and subsequent 3D reconstructions. This code allows to reproduce figures 6-8 from the paper.
262.  Dentate gyrus network model pattern separation and granule cell scaling in epilepsy (Yim et al 2015)
The dentate gyrus (DG) is thought to enable efficient hippocampal memory acquisition via pattern separation. With patterns defined as spatiotemporally distributed action potential sequences, the principal DG output neurons (granule cells, GCs), presumably sparsen and separate similar input patterns from the perforant path (PP). In electrophysiological experiments, we have demonstrated that during temporal lobe epilepsy (TLE), GCs downscale their excitability by transcriptional upregulation of ‘leak’ channels. Here we studied whether this cell type-specific intrinsic plasticity is in a position to homeostatically adjust DG network function. We modified an established conductance-based computer model of the DG network such that it realizes a spatiotemporal pattern separation task, and quantified its performance with and without the experimentally constrained leaky GC phenotype. ...
263.  Dependence of neuronal firing on astroglial membrane transport mechanisms (Oyehaug et al 2012)
"Exposed to a sufficiently high extracellular potassium concentration ([K?+?]o), the neuron can fire spontaneous discharges or even become inactivated due to membrane depolarisation (‘depolarisation block’). Since these phenomena likely are related to the maintenance and propagation of seizure discharges, it is of considerable importance to understand the conditions under which excess [K?+?]o causes them. To address the putative effect of glial buffering on neuronal activity under elevated [K?+?]o conditions, we combined a recently developed dynamical model of glial membrane ion and water transport with a Hodgkin–Huxley type neuron model. In this interconnected glia-neuron model we investigated the effects of natural heterogeneity or pathological changes in glial membrane transporter density by considering a large set of models with different, yet empirically plausible, sets of model parameters. ..."
264.  Detailed passive cable model of Dentate Gyrus Basket Cells (Norenberg et al. 2010)
Fast-spiking, parvalbumin-expressing basket cells (BCs) play a key role in feedforward and feedback inhibition in the hippocampus. ... To quantitatively address this question, we developed detailed passive cable models of BCs in the dentate gyrus based on dual somatic or somatodendritic recordings and complete morphologic reconstructions. Both specific membrane capacitance and axial resistivity were comparable to those of pyramidal neurons, but the average somatodendritic specific membrane resistance (R(m)) was substantially lower in BCs. Furthermore, R(m) was markedly nonuniform, being lowest in soma and proximal dendrites, intermediate in distal dendrites, and highest in the axon. ... Further computational analysis revealed that these unique cable properties accelerate the time course of synaptic potentials at the soma in response to fast inputs, while boosting the efficacy of slow distal inputs. These properties will facilitate both rapid phasic and efficient tonic activation of BCs in hippocampal microcircuits.
265.  Determinants of the intracellular and extracellular waveforms in DA neurons (Lopez-Jury et al 2018)
To systematically address the contribution of AIS, dendritic and somatic compartments to shaping the two-component action potentials (APs), we modeled APs of male mouse and rat dopaminergic neurons. A parsimonious two-domain model, with high (AIS) and lower (dendro-somatic) Na+ conductance, reproduced the notch in the temporal derivatives, but not in the extracellular APs, regardless of morphology. The notch was only revealed when somatic active currents were reduced, constraining the model to three domains. Thus, an initial AIS spike is followed by an actively generated spike by the axon-bearing dendrite (ABD), in turn followed mostly passively by the soma. Larger AISs and thinner ABD (but not soma-to-AIS distance) accentuate the AIS component.
266.  Deterministic chaos in a mathematical model of a snail neuron (Komendantov and Kononenko 1996)
"Chaotic regimes in a mathematical model of pacemaker activity in the bursting neurons of a snail Helix pomatia, have been investigated. The model includes a slow-wave generating mechanism, a spike-generating mechanism, an inward Ca current, intracellular Ca ions, [Ca2+]in, their fast buffering and uptake by intracellular Ca stores, and a [Ca2+]in-inhibited Ca current. Chemosensitive voltage-activated conductance, gB*, responsible for termination of the spike burst, and chemosensitive sodium conductance, gNa*, responsible for the depolarization phase of the slow-wave, were used as control parameters. ... Time courses of the membrane potential and [Ca2+]in were employed to analyse different regimes in the model. ..."
267.  Development and Binocular Matching of Orientation Selectivity in Visual Cortex (Xu et al 2020)
This model investigates the development of orientation selectivity and its binocular matching in visual cortex by implementing a neuron that has plastic synapses for its inputs from the left and right eye. The plasticity is taken to be voltage-based with homeostasis (Clopath et al 2010). The neuron is modeled as an adaptive exponential integrate-fire neuron. The uploaded model has been used in Xu, Cang & Riecke (2020) to analyze the impact of ocular dominance and orientation selectivity on the matching process. There it has been found that the matching can proceed by a slow shifting or a sudden switching of the preferred orientation.
268.  Development of orientation-selective simple cell receptive fields (Rishikesh and Venkatesh, 2003)
Implementation of a computational model for the development of simple-cell receptive fields spanning the regimes before and after eye-opening. The before eye-opening period is governed by a correlation-based rule from Miller (Miller, J. Neurosci., 1994), and the post eye-opening period is governed by a self-organizing, experience-dependent dynamics derived in the reference below.
269.  Dichotomy of action-potential backpropagation in CA1 pyramidal neuron dendrites (Golding et al 2001)
From reference below and Corrigendum: J Neurophysiol 87:1a, 2002 (better versions of figures 2, 3, 5 and 7 because of poor print quality in the original article; as of 2/2006, these figures are perfectly fine in the PDF of the original article that is currently available from the publisher's WWW site). Examines the anatomical and biophysical factors that account for the fact that retrograde invasion of spikes into the apical dendritic tree past 300 um succeeds in some CA1 pyramidal neurons but fails in others.
270.  Differences between type A and B photoreceptors (Blackwell 2006)
In Hermissenda crassicornis, the memory of light associated with turbulence is stored as changes in intrinsic and synaptic currents in both type A and type B photoreceptors. These photoreceptor types exhibit qualitatively different responses to light and current injection, and these differences shape the spatiotemporal firing patterns that control behavior. Thus the objective of the study was to identify the mechanisms underlying these differences. The approach was to develop a type B model that reproduced characteristics of type B photoreceptors recorded in vitro, and then to create a type A model by modifying a select number of ionic currents. Comparison of type A models with characteristics of type A photoreceptors recorded in vitro revealed that type A and type B photoreceptors have five main differences, three that have been characterized experimentally and two that constitute hypotheses to be tested with experiments in the future. See paper for more and details.
271.  Differential modulation of pattern and rate in a dopamine neuron model (Canavier and Landry 2006)
"A stylized, symmetric, compartmental model of a dopamine neuron in vivo shows how rate and pattern can be modulated either concurrently or differentially. If two or more parameters in the model are varied concurrently, the baseline firing rate and the extent of bursting become decorrelated, which provides an explanation for the lack of a tight correlation in vivo and is consistent with some independence of the mechanisms that generate baseline firing rates versus bursting. ..." See paper for more and details.
272.  Discharge hysteresis in motoneurons (Powers & Heckman 2015)
"Motoneuron activity is strongly influenced by the activation of persistent inward currents (PICs) mediated by voltage-gated sodium and calcium channels. ... It has recently been suggested that a number of factors other than PIC can contribute to delta F (firing rate differences between motoneurons) values, including mechanisms underlying spike frequency adaptation and spike threshold accommodation. In the present study, we used a set of compartmental models representing a sample of 20 motoneurons with a range of thresholds to investigate how several different intrinsic motoneuron properties can potentially contribute to variations in F values. ... Our results indicate that, although other factors can contribute, variations in discharge hysteresis and delta F values primarily reflect the contribution of dendritic PICs to motoneuron activation.
273.  Discrete event simulation in the NEURON environment (Hines and Carnevale 2004)
A short introduction to how "integrate and fire" cells are implemented in NEURON. Network simulations that use only artificial spiking cells are extremely efficient, with runtimes proportional to the total number of synaptic inputs received and independent of the number of cells or problem time.
274.  Discrimination on behavioral time-scales mediated by reaction-diffusion in dendrites (Bhalla 2017)
Sequences of events are ubiquitous in sensory, motor, and cognitive function. Key computational operations, including pattern recognition, event prediction, and plasticity, involve neural discrimination of spatio-temporal sequences. Here we show that synaptically-driven reaction diffusion pathways on dendrites can perform sequence discrimination on behaviorally relevant time-scales. We used abstract signaling models to show that selectivity arises when inputs at successive locations are aligned with, and amplified by, propagating chemical waves triggered by previous inputs. We incorporated biological detail using sequential synaptic input onto spines in morphologically, electrically, and chemically detailed pyramidal neuronal models based on rat data.
275.  Disrupted information processing in Fmr1-KO mouse layer 4 barrel cortex (Domanski et al 2019)
"Sensory hypersensitivity is a common and debilitating feature of neurodevelopmental disorders such as Fragile X Syndrome (FXS). How developmental changes in neuronal function culminate in network dysfunction that underlies sensory hypersensitivities is unknown. By systematically studying cellular and synaptic properties of layer 4 neurons combined with cellular and network simulations, we explored how the array of phenotypes in Fmr1-knockout (KO) mice produce circuit pathology during development. We show that many of the cellular and synaptic pathologies in Fmr1-KO mice are antagonistic, mitigating circuit dysfunction, and hence may be compensatory to the primary pathology. Overall, the layer 4 network in the Fmr1-KO exhibits significant alterations in spike output in response to thalamocortical input and distorted sensory encoding. This developmental loss of layer 4 sensory encoding precision would contribute to subsequent developmental alterations in layer 4-to-layer 2/3 connectivity and plasticity observed in Fmr1-KO mice, and circuit dysfunction underlying sensory hypersensitivity."
276.  Distal inhibitory control of sensory-evoked excitation (Egger, Schmitt et al. 2015)
Model of a cortical layer (L) 2 pyramidal neuron embedded in an anatomically realistic network of two barrel columns in rat vibrissal cortex. This model is used to investigate the effects of spatially and temporally specific inhibition from L1 inhibitory interneurons on the sensory-evoked subthreshold responses of the L2 pyramidal neuron, and can be used to create simulation results underlying Figures 3D, 4B, 4C and 4E from (Egger, Schmitt et al. 2015).
277.  Distance-dependent synaptic strength in CA1 pyramidal neurons (Menon et al. 2013)
Menon et al. (2013) describes the experimentally-observed variation in synaptic AMPA and NMDA conductance as a function of distance from the soma. This model explores the effect of this variation on somatic EPSPs and dendritic spike initiation, as compared to the case of uniform AMPA and NMDA conductance.
278.  Distinct current modules shape cellular dynamics in model neurons (Alturki et al 2016)
" ... We hypothesized that currents are grouped into distinct modules that shape specific neuronal characteristics or signatures, such as resting potential, sub-threshold oscillations, and spiking waveforms, for several classes of neurons. For such a grouping to occur, the currents within one module should have minimal functional interference with currents belonging to other modules. This condition is satisfied if the gating functions of currents in the same module are grouped together on the voltage axis; in contrast, such functions are segregated along the voltage axis for currents belonging to different modules. We tested this hypothesis using four published example case models and found it to be valid for these classes of neurons. ..."
279.  Distinct integration properties of noisy inputs in active dendritic subunits (Poleg-Polsky 2019)
The brain operates surprisingly well despite the noisy nature of individual neurons. The central mechanism for noise mitigation in the nervous system is thought to involve averaging over multiple noise-corrupted inputs. Subsequently, there has been considerable interest recently to identify noise structures that can be integrated linearly in a way that preserves reliable signal encoding. By analyzing realistic synaptic integration in biophysically accurate neuronal models, I report a complementary de-noising approach that is mediated by focal dendritic spikes. Dendritic spikes might seem to be unlikely candidates for noise reduction due to their miniscule integration compartments and poor averaging abilities. Nonetheless, the extra thresholding step introduced by dendritic spike generation increases neuronal tolerance for a broad category of noise structures, some of which cannot be resolved well with averaging. This property of active dendrites compensates for compartment size constraints and expands the repertoire of conditions that can be processed by neuronal populations.
280.  Dopamine neuron of the vent. periaqu. gray and dors. raphe nucleus (vlPAG/DRN) (Dougalis et al 2017)
The following computer model describes the electrophysiological properties of dopamine (DA) neurons of the ventrolateral periaquaductal gray and dorsal raphe nucleus (vlPAG/DRN). the model and how to replicate Figures 7-10 of the manuscript (Dougalis et al., 2017 J Comput Neurosci). SUMMARY: We have conducted a voltage-clamp study to provide a kinetic description of major sodium, potassium and calcium ionic currents operant on adult DA vlPAG/DRN neurons in brain slices obtained from pitx3-GFP mice. Based on experimentally derived voltage-clamp data, we then constructed a simplified, conductance-based, Hodgkin and Huxley-type, computer model and validated its behaviour against in vitro neurophysiological data. Using simulations in the computational DA model, we explored the contribution of individual ionic currents in vlPAG/DRN DA neuron’s spontaneous firing, pacemaker frequency and threshold for spike frequency adaptation in silico. The data presented here extend our previous physiological characterization (Dougalis et al. 2012) and argue that DA neurons of the vlPAG/DRN express autorhythmicity in the absence of synaptic transmission via the interplay of potassium and sodium currents without the absolute need of calcium currents. The properties of the ionic currents recorded here (IH current, IA current), the lack of small oscillating potentials in the presence of sodium channel blockers taken together with the mechanisms for autorhythmicity (reliance more on sodium rather than calcium currents) also support further the idea that vlPAG/DRN DA neurons are operationally similar to VTA, rather than SNc, DA neurons. In particular, the properties of a slowly inactivating IA current in conjunction with the small and slowly activating IH current described herein pinpoint that vlPAG/DRN DA neurons are most similar to prefrontal cortex or medial shell of nucleus accumbens projecting DA neurons (see Lammel et al. 2008, 2011).
281.  Dopamine-modulated medium spiny neuron, reduced model (Humphries et al. 2009)
We extended Izhikevich's reduced model of the striatal medium spiny neuron (MSN) to account for dopaminergic modulation of its intrinsic ion channels and synaptic inputs. We tuned our D1 and D2 receptor MSN models using data from a recent (Moyer et al, 2007) large-scale compartmental model. Our new models capture the input-output relationships for both current injection and spiking input with remarkable accuracy, despite the order of magnitude decrease in system size. They also capture the paired pulse facilitation shown by MSNs. Our dopamine models predict that synaptic effects dominate intrinsic effects for all levels of D1 and D2 receptor activation. Our analytical work on these models predicts that the MSN is never bistable. Nonetheless, these MSN models can produce a spontaneously bimodal membrane potential similar to that recently observed in vitro following application of NMDA agonists. We demonstrate that this bimodality is created by modelling the agonist effects as slow, irregular and massive jumps in NMDA conductance and, rather than a form of bistability, is due to the voltage-dependent blockade of NMDA receptors
282.  Dopaminergic cell bursting model (Kuznetsov et al 2006)
Dopaminergic neurons of the midbrain fire spontaneously at rates <10/s and ordinarily will not exceed this range even when driven with somatic current injection. During spontaneous bursting of dopaminergic neurons in vivo, bursts related to reward expectation in behaving animals, and bursts generated by dendritic application of N-methyl-D-aspartate (NMDA) agonists, transient firing attains rates well above this range. We suggest a way such highfrequency firing may occur in response to dendritic NMDA receptor activation. We have extended the coupled oscillator model of the dopaminergic neuron, which represents the soma and dendrites as electrically coupled compartments with different natural spiking frequencies, by addition of dendritic AMPA (voltage-independent) or NMDA (voltage-dependent) synaptic conductance. Both soma and dendrites contain a simplified version of the calcium-potassium mechanism known to be the mechanism for slow spontaneous oscillation and background firing in dopaminergic cells. We show that because of its voltage dependence, NMDA receptor activation acts to amplify the effect on the soma of the high-frequency oscillation of the dendrites, which is normally too weak to exert a large influence on the overall oscillation frequency of the neuron.
283.  Dorsal root ganglion (DRG) neuronal model (Amir, Devor 2003)
The model shows that an electrically excitable soma is not necessary for spike through-conduction in the t-shaped geometry of a dorsal root ganglion neuron axon. Electrical excitability of the soma is required, however, for soma spike invasion. See papers for details and more.
284.  Dorsal root ganglion (DRG) neuronal model (Kovalsky et al. 2009)
This model, diverged from oscillatory parameters seen in live cells and failed to produce characteristic ectopic discharge patterns. Here we show that use of a more complete set of Na+ conductances--which includes several delayed components--enables simulation of the entire repertoire of oscillation-triggered electrogenic phenomena seen in live dorsal root ganglion (DRG) neurons. This includes a physiological window of induction and natural patterns of spike discharge. An INa+ component at 2-20 ms was particularly important, even though it represented only a tiny fraction of overall INa+ amplitude. With the addition of a delayed rectifier IK+ the singlet firing seen in some DRG neurons can also be simulated. The model reveals the key conductances that underlie afferent ectopia, conductances that are potentially attractive targets in the search for more effective treatments of neuropathic pain.
285.  Double boundary value problem (A. Bose and J.E. Rubin, 2015)
For two neurons coupled with mutual inhibition, we investigate the strategies that each neuron should utilize in order to maximize the number of spikes it can fire (or equivalently the amount of time it is active) before the other neuron takes over. We derive a one-dimensional map whose fixed points correspond to periodic anti-phase bursting solutions. The model here solves a novel double boundary value problem that can be used to obtain the graph of this map. Read More:
286.  Double cable myelinated axon (Layer 5 pyramidal neuron; Cohen et al 2020)
The periaxonal space in myelinated axons is conductive (~50 ohm cm). Together with a rapidly charging myelin sheath and relatively sealed paranodes, periaxonal conduction shapes the saltating voltage profiles of transaxonal (Vm), transmyelin (Vmy) and transfibre (Vmym) potentials. This model exemplifies double cable saltatory conduction across both time and space, and is the same cell (#6) as seen in Movie S4 of Cohen et al. 2020. This model version allows one to visualize and manipulate the controlling parameters of a propagating action potential. Further notes: The corresponding potentials in NEURON to those named above are v, vext (or vext[0]) and v+vext, respectively. The loaded biophysical parameters were those optimized for this cell (Cohen et al. 2020).
287.  DRG neuron models investigate how ion channel levels regulate firing properties (Zheng et al 2019)
We present computational models for an Abeta-LTMR (low-threshold mechanoreceptor) and a C-LTMR expressing four Na channels and four K channels to investigate how the expression level of Kv1 and Kv4 regulate number of spikes (repetitive firing) and onset latency to action potentials in Abeta-LTMRs and C-LTMRs, respectively.
288.  Drosophila 3rd instar larval aCC motoneuron (Gunay et al. 2015)
Single compartmental, ball-and-stick models implemented in XPP and full morphological model in Neuron. Paper has been submitted and correlates anatomical properties with electrophysiological recordings from these hard-to-access neurons. For instance we make predictions about location of the spike initiation zone, channel distributions, and synaptic input parameters.
289.  Drosophila circadian clock neurone model of essential tremor (Smith et al 2018)
Model of Drosophila ventral lateral neuron (LNV) used to study a human ion channel associated with essential tremor.
290.  Drosophila lateral ventral clock neuron (LNV) model (Smith et al 2019)
LNVmodel models the activity of a Drosophila lateral ventral clock neurons (LNV) neurone.
291.  Drosophila projection neuron electrotonic structure (Gouwens and Wilson 2009)
We address the issue of how electrical signals propagate in Drosophila neurons by modeling the electrotonic structure of the antennal lobe projection neurons innervating glomerulus DM1. The readme file contains instructions for running the model.
292.  DRt neuron model (Sousa et al., 2014)
Despite the importance and significant clinical impact of understanding information processing in the nociceptive system, the functional properties of neurons in many parts of this system are still unknown. In this work we performed whole-cell patch-clamp recording in rat brainstem blocks to characterize the electrophysiological properties of neurons in the dorsal reticular nucleus (DRt), a region known to be involved in pronociceptive modulation. We also compared properties of DRt neurons with those in the adjacent parvicellular reticular nucleus (PCRt) and in neighboring regions outside the reticular formation. We found that neurons in the DRt and PCRt had similar electrophysiological properties and exhibited mostly tonic-like firing patterns, whereas neurons outside the reticular formation showed a larger diversity of firing-patterns. The dominance of tonic neurons in the DRt supports previous conclusions that these neurons encode stimulus intensity through their firing frequency.
293.  Duration-tuned neurons from the inferior colliculus of the big brown bat (Aubie et al. 2009)
dtnet is a generalized neural network simulator written in C++ with an easy to use XML description language to generate arbitrary neural networks and then run simulations covering many different parameter values. For example, you can specify ranges of parameter values for several different connection weights and then automatically run simulations over all possible parameters. Graphing ability is built in as long as the free, open-source, graphing application GLE ( is installed. Included in the examples folder are simulation descriptions that were used to generate the results in Aubie et al. (2009). Refer to the README file for instructions on compiling and running these examples. The most recent source code can be obtained from GitHub: <a href=""></a>
294.  Duration-tuned neurons from the inferior colliculus of vertebrates (Aubie et al. 2012)
These models reproduce the responses of duration-tuned neurons in the auditory midbrain of the big brown bat, the rat, the mouse and the frog (Aubie et al. 2012). They are written in the Python interface to NEURON and a subset of the figures from Aubie et al. (2012) are pre-set in (raw data is generated and a separate graphing program must be used to visualize the results).
295.  Dynamical assessment of ion channels during in vivo-like states (Guet-McCreight & Skinner 2020)
" ... Methods: We employ two morphologically-detailed multi-compartment models of a specific type of inhibitory interneuron, the oriens lacunosum moleculare (OLM) cell. The OLM cell is a well-studied cell type in CA1 hippocampus that is important in gating sensory and contextual information. We create in vivo-like states for these cellular models by including levels of synaptic bombardment that would occur in vivo. Using visualization tools and analyses we assess the ion channel current contribution profile across the different somatic and dendritic compartments of the models. Results: We identify changes in dendritic excitability, ion channel current contributions and co-activation patterns between in vitro and in vivo-like states. Primarily, we find that the relative timing between ion channel currents are mostly invariant between states, but exhibit changes in magnitudes and decreased propagation across dendritic compartments. We also find enhanced dendritic hyperpolarization-activated cyclic nucleotide-gated channel (h-channel) activation during in vivo-like states, which suggests that dendritically located h-channels are functionally important in altering signal propagation in the behaving animal. ..."
296.  Dynamical model of olfactory bulb mitral cell (Rubin, Cleland 2006)
This four-compartment mitral cell exhibits endogenous subthreshold oscillations, phase resetting, and evoked spike phasing properties as described in electrophysiological studies of mitral cells. It is derived from the prior work of Davison et al (2000) and Bhalla and Bower (1993). See readme.txt for details.
297.  Dynamics of Spike Initiation (Prescott et al. 2008)
"Transduction of graded synaptic input into trains of all-or-none action potentials (spikes) is a crucial step in neural coding. Hodgkin identified three classes of neurons with qualitatively different analog-to-digital transduction properties. Despite widespread use of this classification scheme, a generalizable explanation of its biophysical basis has not been described. We recorded from spinal sensory neurons representing each class and reproduced their transduction properties in a minimal model. With phase plane and bifurcation analysis, each class of excitability was shown to derive from distinct spike initiating dynamics. Excitability could be converted between all three classes by varying single parameters; moreover, several parameters, when varied one at a time, had functionally equivalent effects on excitability. From this, we conclude that the spike-initiating dynamics associated with each of Hodgkin’s classes represent different outcomes in a nonlinear competition between oppositely directed, kinetically mismatched currents. ..."
298.  E-I-E direction-selective motion discrimination visual cortex traveling waves (Heitmann et al 2020)
The direction-selective responses of neurons in visual cortex cannot be separated into independent spatial and temporal processes. Contemporary theories of how neurons compute non-separable responses typically rely on finely tuned transmission delays. However the existence of such delays is controversial. We propose an alternative neural mechanism for computing non-separable responses that relies on the predisposition of the cortical tissue to spontaneously generate traveling waves of neural activity. We propose that these endogenous waves resonate with the visual stimulus to elicit direction-selective neural responses without resort to time delays.
299.  Effect of ionic diffusion on extracellular potentials (Halnes et al 2016)
"Recorded potentials in the extracellular space (ECS) of the brain is a standard measure of population activity in neural tissue. Computational models that simulate the relationship between the ECS potential and its underlying neurophysiological processes are commonly used in the interpretation of such measurements. Standard methods, such as volume-conductor theory and current-source density theory, assume that diffusion has a negligible effect on the ECS potential, at least in the range of frequencies picked up by most recording systems. This assumption remains to be verified. We here present a hybrid simulation framework that accounts for diffusive effects on the ECS potential. ..."
300.  Effect of riluzole on action potential in cultured human skeletal muscle cells (Wang YJ et al. 2008)
Simulation studies also unraveled that both decreased conductance of I(Na) and increased conductance of I(K(Ca)) utilized to mimic riluzole actions in skeletal muscle cells could combine to decrease the amplitude of action potentials and increase the repolarization of action potentials.
301.  Effect of slowly inactivating IKdr to delayed firing of action potentials (Wu et al. 2008)
"The properties of slowly inactivating delayed-rectifier K+ current (IKdr) were investigated in NG108-15 neuronal cells differentiated with long-term exposure to dibutyryl cyclic AMP. ... The computer model, in which state-dependent inactivation of IKdr was incorporated, was also implemented to predict the firing behavior present in NG108-15 cells. ... Our theoretical work and the experimental results led us to propose a pivotal role of slowly inactivating IKdr in delayed firing of APs in NG108-15 cells. The results also suggest that aconitine modulation of IKdr gating is an important molecular mechanism through which it can contribute to neuronal firing."
302.  Effect of voltage sensitive fluorescent proteins on neuronal excitability (Akemann et al. 2009)
"Fluorescent protein voltage sensors are recombinant proteins that are designed as genetically encoded cellular probes of membrane potential using mechanisms of voltage-dependent modulation of fluorescence. Several such proteins, including VSFP2.3 and VSFP3.1, were recently reported with reliable function in mammalian cells. ... Expression of these proteins in cell membranes is accompanied by additional dynamic membrane capacitance, ... We used recordings of sensing currents and fluorescence responses of VSFP2.3 and of VSFP3.1 to derive kinetic models of the voltage-dependent signaling of these proteins. Using computational neuron simulations, we quantitatively investigated the perturbing effects of sensing capacitance on the input/output relationship in two central neuron models, a cerebellar Purkinje and a layer 5 pyramidal neuron. ... ". The Purkinje cell model is included in ModelDB.
303.  Effects of Acetyl-L-carnitine on neural transmission (Lombardo et al 2004)
Acetyl-L-carnitine is known to improve many aspects of the neural activity even if its exact role in neurotransmission is still unknown. This study investigates the effects of acetyl-L-carnitine in T segmental sensory neurons of the leech Hirudo medicinalis. These neurons are involved in some forms of neural plasticity associated with learning processes. Their physiological firing is accompanied by a large afterhyperpolarization that is mainly due to the Na+/K+ ATPase activity and partially to a Ca2+-dependent K+ current. A clear-cut hyperpolarization and a significant increase of the afterhyperpolarization have been recorded in T neurons of leeches injected with 2 mM acetyl-L-carnitine some days before. Acute treatments of 50 mM acetyl-L-carnitine induced similar effects in T cells of naive animals. Moreover, in these cells, widely arborized, the afterhyperpolarization seems to play an important role in determining the action potential transmission at neuritic bifurcations. A computational model of a T cell has been previously developed considering detailed data for geometry and the modulation of the pump current. Herein, we showed that to a larger afterhyperpolarization, due to the acetyl-L-carnitine-induced effects, corresponds a decrement in the number of action potentials reaching synaptic terminals.
304.  Effects of Chloride accumulation and diffusion on GABAergic transmission (Jedlicka et al 2011)
"In the CNS, prolonged activation of GABA(A) receptors (GABA(A)Rs) has been shown to evoke biphasic postsynaptic responses, consisting of an initial hyperpolarization followed by a depolarization. A potential mechanism underlying the depolarization is an acute chloride (Cl(-)) accumulation resulting in a shift of the GABA(A) reversal potential (E(GABA)). The amount of GABA-evoked Cl(-) accumulation and accompanying depolarization depends on presynaptic and postsynaptic properties of GABAergic transmission, as well as on cellular morphology and regulation of Cl(-) intracellular concentration ([Cl(-)](i)). To analyze the influence of these factors on the Cl(-) and voltage behavior, we studied spatiotemporal dynamics of activity-dependent [Cl(-)](i) changes in multicompartmental models of hippocampal cells based on realistic morphological data. ..."
305.  Effects of electric fields on cognitive functions (Migliore et al 2016)
The paper discusses the effects induced by an electric field at power lines frequency on neuronal activity during cognitive processes.
306.  Effects of KIR current inactivation in NAc Medium Spiny Neurons (Steephen and Manchanda 2009)
"Inward rectifying potassium (KIR) currents in medium spiny (MS) neurons of nucleus accumbens inactivate significantly in ~40% of the neurons but not in the rest, which may lead to differences in input processing by these two groups. Using a 189-compartment computational model of the MS neuron, we investigate the influence of this property using injected current as well as spatiotemporally distributed synaptic inputs. Our study demonstrates that KIR current inactivation facilitates depolarization, firing frequency and firing onset in these neurons. ..."
307.  Effects of neural morphology on global and focal NMDA-spikes (Poleg-Polsky 2015)
This entry contains the NEURON files required to recreate figures 4-8 of the paper "Effects of Neural Morphology and Input Distribution on Synaptic Processing by Global and Focal NMDA-spikes" by Alon Poleg-Polsky
308.  Effects of the membrane AHP on the Lateral Superior Olive (LSO) (Zhou & Colburn 2010)
This simulation study investigated how membrane afterhyperpolarization (AHP) influences spiking activity of neurons in the Lateral Superior Olive (LSO). The model incorporates a general integrate-and-fire spiking mechanism with a first-order adaptation channel. Simulations focus on differentiating the effects of GAHP, tauAHP, and input strength on (1) spike interval statistics, such as negative serial correlation and chopper onset, and (2) neural sensitivity to interaural level difference (ILD) of LSO neurons. The model simulated electrophysiological data collected in cat LSO (Tsuchitani and Johnson, 1985).
309.  Efficient estimation of detailed single-neuron models (Huys et al. 2006)
"Biophysically accurate multicompartmental models of individual neurons ... depend on a large number of parameters that are difficult to estimate. ... We propose a statistical approach to the automatic estimation of various biologically relevant parameters, including 1) the distribution of channel densities, 2) the spatiotemporal pattern of synaptic input, and 3) axial resistances across extended dendrites. ... We demonstrate that the method leads to accurate estimations on a wide variety of challenging model data sets that include up to about 10,000 parameters (roughly two orders of magnitude more than previously feasible) and describe how the method gives insights into the functional interaction of groups of channels."
310.  Efficient simulation environment for modeling large-scale cortical processing (Richert et al. 2011)
"We have developed a spiking neural network simulator, which is both easy to use and computationally efficient, for the generation of large-scale computational neuroscience models. The simulator implements current or conductance based Izhikevich neuron networks, having spike-timing dependent plasticity and short-term plasticity. ..."
311.  Electrical compartmentalization in neurons (Wybo et al 2019)
"The dendritic tree of neurons plays an important role in information processing in the brain. While it is thought that dendrites require independent subunits to perform most of their computations, it is still not understood how they compartmentalize into functional subunits. Here, we show how these subunits can be deduced from the properties of dendrites. We devised a formalism that links the dendritic arborization to an impedance-based tree graph and show how the topology of this graph reveals independent subunits. This analysis reveals that cooperativity between synapses decreases slowly with increasing electrical separation and thus that few independent subunits coexist. We nevertheless find that balanced inputs or shunting inhibition can modify this topology and increase the number and size of the subunits in a context-dependent manner. We also find that this dynamic recompartmentalization can enable branch-specific learning of stimulus features. Analysis of dendritic patch-clamp recording experiments confirmed our theoretical predictions."
312.  Electrically-coupled Retzius neurons (Vazquez et al. 2009)
"Dendritic electrical coupling increases the number of effective synaptic inputs onto neurons by allowing the direct spread of synaptic potentials from one neuron to another. Here we studied the summation of excitatory postsynaptic potentials (EPSPs) produced locally and arriving from the coupled neuron (transjunctional) in pairs of electrically-coupled Retzius neurons of the leech. We combined paired recordings of EPSPs, the production of artificial EPSPs (APSPs) in neuron pairs with different coupling coefficients and simulations of EPSPs produced in the coupled dendrites. ..."
313.  Electrodiffusive astrocytic and extracellular ion concentration dynamics model (Halnes et al. 2013)
An electrodiffusive formalism was developed for computing the dynamics of the membrane potential and ion concentrations in the intra- and extracellular space in a one-dimensional geometry (cable). This (general) formalism was implemented in a model of astrocytes exchanging K+, Na+ and Cl- ions with the extracellular space (ECS). A limited region (0< x<l/10 where l is the astrocyte length) of the ECS was exposed to an increase in the local K+ concentration. The model is used to explore how astrocytes contribute in transporting K+ out from high-concentration regions via a mechanism known as spatial buffering, which involves local uptake from high concentration regions, intracellular transport, and release of K+ in regions with lower ECS concentrations.
314.  ELL Medium Ganglion cell (Muller et al 2019)
"Distributing learning across multiple layers has proven extremely powerful in artificial neural networks. However, little is known about how multi-layer learning is implemented in the brain. Here, we provide an account of learning across multiple processing layers in the electrosensory lobe (ELL) of mormyrid fish and report how it solves problems well known from machine learning. Because the ELL operates and learns continuously, it must reconcile learning and signaling functions without switching its mode of operation. We show that this is accomplished through a functional compartmentalization within intermediate layer neurons in which inputs driving learning differentially affect dendritic and axonal spikes. We also find that connectivity based on learning rather than sensory response selectivity assures that plasticity at synapses onto intermediate-layer neurons is matched to the requirements of output neurons. The mechanisms we uncover have relevance to learning in the cerebellum, hippocampus, and cerebral cortex, as well as in artificial systems."
315.  Emergent properties of networks of biological signaling pathways (Bhalla, Iyengar 1999)
Biochemical signaling networks were constructed with experimentally obtained constants and analyzed by computational methods to understand their role in complex biological processes. These networks exhibit emergent properties such as integration of signals across multiple time scales, generation of distinct outputs depending on input strength and duration, and self-sustaining feedback loops. Properties of signaling networks raise the possibility that information for "learned behavior" of biological systems may be stored within intracellular biochemical reactions that comprise signaling pathways.
316.  Endothelin action on pituitary latotrophs (Bertram et al. 2006)
Endothelin (ET-1, -2, and -3 designate three genes which produce different endothelin isopeptides) is a prolactin inhibiting substance of hypothalmic origin. ET-1 binding is part of at least four G protein signaling pathways in lactotrophs. The sequence of events in these pathways following the presentation of nano- and pico-molar concentrations of ET-1 is modeled in the paper.
317.  Enhanced Excitability in Hermissenda: modulation by 5-HT (Cai et al 2003)
Serotonin (5-HT) applied to the exposed but otherwise intact nervous system results in enhanced excitability of Hermissenda type-B photoreceptors. Several ion currents in the type-B photoreceptors are modulated by 5-HT, including the A-type K+ current (IK,A), sustained Ca2+ current (ICa,S), Ca-dependent K+ current (IK,Ca), and a hyperpolarization-activated inward rectifier current (Ih). In this study,we developed a computational model that reproduces physiological characteristics of type B photoreceptors, e.g. resting membrane potential, dark-adapted spike activity, spike width, and the amplitude difference between somatic and axonal spikes. We then used the model to investigate the contribution of different ion currents modulated by 5-HT to the magnitudes of enhanced excitability produced by 5-HT. See paper for results and more details.
318.  Enhancing the HH eqs: simulations based on the first publication in Biophys J (Moore 2015)
"The experiments in the Cole and Moore article in the first issue of the Biophysical Journal provided the first independent experimental confirmation of the Hodgkin-Huxley (HH) equations. A log-log plot of the K current versus time showed that raising the HH variable n to the sixth power provided the best fit to the data. Subsequent simulations using n6 and setting the resting potential at the in vivo value simplifies the HH equations by eliminating the leakage term. ..."
319.  Ephaptic coupling in passive cable and MSO neuron models (Goldwyn & Rinzel 2016)
Simulation code to explore how the synchronous activity of a bundle of neurons generates extracellular voltage, and how this extracellular voltage influences the membrane potential of "nearby" neurons. A non-synaptic mechanism known as ephaptic coupling. A model of a passive cable population (including user-friendly matlab GUI) and a model of medial superior olive neurons are included.
320.  ERG current in repolarizing plateau potentials in dopamine neurons (Canavier et al 2007)
"Blocking the small-conductance (SK) calcium-activated potassium channel promotes burst firing in dopamine neurons both in vivo and in vitro. ... We focus on the underlying plateau potential oscillation generated in the presence of both apamin and TTX, so that action potentials are not considered. We find that although the plateau potentials are mediated by a voltage-gated Ca2+ current, they do not depend on the accumulation of cytosolic Ca2+, then use a computational model to test the hypothesis that the slowly voltage-activated ether-a-go-go–related gene (ERG) potassium current repolarizes the plateaus. The model, which includes a material balance on calcium, is able to reproduce the time course of both membrane potential and somatic calcium concentration, and can also mimic the induction of plateau potentials by the calcium chelator BAPTA." See paper for more.
321.  Estimation and Production of Time Intervals (Migliore et al 2001)
NEURON model files from the paper M. Migliore, L. Messineo, M. Cardaci, G.F. Ayala, Quantitative modeling of perception and production of time intervals, J.Neurophysiol. 86, 2754-2760 (2001). Contact if you have any questions about the implementation of the model.
322.  Evolving simple models of diverse dynamics in hippocampal neuron types (Venkadesh et al 2018)
" ... we present an automated pipeline based on evolutionary algorithms to quantitatively reproduce features of various classes of neuronal spike patterns using the Izhikevich model. Employing experimental data from, a comprehensive knowledgebase of neuron types in the rodent hippocampus, we demonstrate that our approach reliably fit Izhikevich models to nine distinct classes of experimentally recorded spike patterns, including delayed spiking, spiking with adaptation, stuttering, and bursting. ..."
323.  Excitability of DA neurons and their regulation by synaptic input (Morozova et al. 2016a, 2016b)
This code contains conductance-based models of Dopaminergic (DA) and GABAergic neurons, used in Morozova et al 2016 PLOS Computational Biology paper in order to study the type of excitability of the DA neurons and how it is influenced by the intrinsic and synaptic currents. We identified the type of excitability by calculating bifurcation diagrams and F-I curves using XPP file. This model was also used in Morozova et al 2016 J. Neurophysiology paper in order to study the effect of synchronization in GABAergic inputs on the firing dynamics of the DA neuron.
324.  Excitability of PFC Basal Dendrites (Acker and Antic 2009)
".. We carried out multi-site voltage-sensitive dye imaging of membrane potential transients from thin basal branches of prefrontal cortical pyramidal neurons before and after application of channel blockers. We found that backpropagating action potentials (bAPs) are predominantly controlled by voltage-gated sodium and A-type potassium channels. In contrast, pharmacologically blocking the delayed rectifier potassium, voltage-gated calcium or Ih, conductance had little effect on dendritic action potential propagation. Optically recorded bAP waveforms were quantified and multicompartmental modeling (NEURON) was used to link the observed behavior with the underlying biophysical properties. The best-fit model included a non-uniform sodium channel distribution with decreasing conductance with distance from the soma, together with a non-uniform (increasing) A-type potassium conductance. AP amplitudes decline with distance in this model, but to a lesser extent than previously thought. We used this model to explore the mechanisms underlying two sets of published data involving high frequency trains of action potentials, and the local generation of sodium spikelets. ..."
325.  Excitability of the soma in central nervous system neurons (Safronov et al 2000)
The ability of the soma of a spinal dorsal horn neuron, a spinal ventral horn neuron, and a hippocampal pyramidal neuron to generate action potentials was studied using experiments and computer simulations. By comparing recordings ... of a dorsal horn neuron with simulated responses, it was shown that computer models can be adequate for the study of somatic excitability. The modeled somata of both spinal neurons were unable to generate action potentials, showing only passive and local responses to current injections. ... In contrast to spinal neurons, the modeled soma of the hippocampal pyramidal neuron generated spikes with an overshoot of +9 mV. It is concluded that the somata of spinal neurons cannot generate action potentials and seem to resist their propagation from the axon to dendrites. ... See paper for more and details.
326.  Excitation-contraction coupling in an integrative heart cell model (Greenstein et al 2006)
"... In this study, we generalize a recently developed analytical approach for deriving simplified mechanistic models of CICR (Ca(2+)-induced Ca(2+) release) to formulate an integrative model of the canine cardiac myocyte which is computationally efficient. The resulting model faithfully reproduces experimentally measured properties of EC (excitation-contraction) coupling and whole cell phenomena. The model is used to study the role of local redundancy in L-type Ca(2+) channel gating and the role of dyad configuration on EC coupling. Simulations suggest that the characteristic steep rise in EC coupling gain observed at hyperpolarized potentials is a result of increased functional coupling between LCCs (L-type Ca(2+) channels) and RyRs (ryanodine-sensitive Ca(2+) release channels). We also demonstrate mechanisms by which alterations in the early repolarization phase of the action potential, resulting from reduction of the transient outward potassium current, alters properties of EC coupling."
327.  Excitation-contraction coupling/mitochondrial energetics (ECME) model (Cortassa et al. 2006)
"An intricate network of reactions is involved in matching energy supply with demand in the heart. This complexity arises because energy production both modulates and is modulated by the electrophysiological and contractile activity of the cardiac myocyte. Here, we present an integrated mathematical model of the cardiac cell that links excitation-contraction coupling with mitochondrial energy generation. The dynamics of the model are described by a system of 50 ordinary differential equations. The formulation explicitly incorporates cytoplasmic ATP-consuming processes associated with force generation and ion transport, as well as the creatine kinase reaction. Changes in the electrical and contractile activity of the myocyte are coupled to mitochondrial energetics through the ATP, Ca21, and Na1 concentrations in the myoplasmic and mitochondrial matrix compartments. ..."
328.  Excitatory synaptic interactions in pyramidal neuron dendrites (Behabadi et al. 2012)
" ... We hypothesized that if two excitatory pathways bias their synaptic projections towards proximal vs. distal ends of the basal branches, the very different local spike thresholds and attenuation factors for inputs near and far from the soma might provide the basis for a classical-contextual functional asymmetry. Supporting this possibility, we found both in compartmental models and electrophysiological recordings in brain slices that the responses of basal dendrites to spatially separated inputs are indeed strongly asymmetric. ..."
329.  External Tufted Cell Model (Ryan Viertel, Alla Borisyuk 2019)
ODE model of the Mammalian External Tufted Cell
330.  Extracellular fields for a three-dimensional network of cells using NEURON (Appukuttan et al 2017)
" ... In the present work, we demonstrate a technique to couple the extracellular fields of individual cells within the NEURON simulation environment. The existing features of the simulator are extended by explicitly defining current balance equations, resulting in the coupling of the extracellular fields of adjacent cells. ..."
331.  Extraction and classification of three cortical neuron types (Mensi et al. 2012)
This script proposes a new convex fitting procedure that allows the parameters estimation of a large class of stochastic Integrate-and-Fire model upgraded with spike-triggered current and moving threshold from patch-clamp experiments (i.e. given the injected current and the recorded membrane potential). This script applies the method described in the paper to estimate the parameters of a reference model from a single voltage trace and the corresponding input current and evaluate the performance of the fitted model on a separated test set.
332.  Factors contribution to GDP-induced [Cl-]i transients (Lombardi et al 2019)
This models are used to evaluate which factors influence the GDP (giant depolarizing potential) induced [Cl-]I transients based on a initial model of P. Jedlicka
333.  Fast sodium channel gating in mossy fiber axons (Schmidt-Hieber et al. 2010)
"... To study the mechanisms underlying AP initiation in unmyelinated hippocampal mossy fibers of adult mice, we recorded sodium currents in axonal and somatic membrane patches. We demonstrate that sodium channel density in the proximal axon is ~5 times higher than in the soma. Furthermore, sodium channel activation and inactivation are ~2 times faster. Modeling revealed that the fast activation localized the initiation site to the proximal axon even upon strong synaptic stimulation, while fast inactivation contributed to energy-efficient membrane charging during APs. ..."
334.  Fast Spiking Basket cells (Tzilivaki et al 2019)
"Interneurons are critical for the proper functioning of neural circuits. While often morphologically complex, dendritic integration and its role in neuronal output have been ignored for decades, treating interneurons as linear point neurons. Exciting new findings suggest that interneuron dendrites support complex, nonlinear computations: sublinear integration of EPSPs in the cerebellum, coupled to supralinear calcium accumulations and supralinear voltage integration in the hippocampus. These findings challenge the point neuron dogma and call for a new theory of interneuron arithmetic. Using detailed, biophysically constrained models, we predict that dendrites of FS basket cells in both hippocampus and mPFC come in two flavors: supralinear, supporting local sodium spikes within large-volume branches and sublinear, in small-volume branches. Synaptic activation of varying sets of these dendrites leads to somatic firing variability that cannot be explained by the point neuron reduction. Instead, a 2-stage Artificial Neural Network (ANN), with both sub- and supralinear hidden nodes, captures most of the variance. We propose that FS basket cells have substantially expanded computational capabilities sub-served by their non-linear dendrites and act as a 2-layer ANN."
335.  Fast-spiking cortical interneuron (Golomb et al. 2007)
Cortical fast-spiking (FS) interneurons display highly variable electrophysiological properties. We hypothesize that this variability emerges naturally if one assumes a continuous distribution of properties in a small set of active channels. We construct a minimal, single-compartment conductance-based model of FS cells that includes transient Na+, delayed-rectifier K+, and slowly inactivating d-type K+ conductances. The model may display delay to firing. Stuttering (elliptic bursting) and subthreshold oscillations may be observed for small Na+ window current.
336.  Feedforward inhibition in pyramidal cells (Ferrante & Ascoli 2015)
"Feedforward inhibition (FFI) enables pyramidal cells in area CA1 of the hippocampus (CA1PCs) to remain easily excitable while faithfully representing a broad range of excitatory inputs without quickly saturating. Despite the cortical ubiquity of FFI, its specific function is not completely understood. FFI in CA1PCs is mediated by two physiologically and morphologically distinct GABAergic interneurons: fast-spiking, perisomatic-targeting basket cells and regular-spiking, dendritic-targeting bistratified cells. These two FFI pathways might create layer-specific computational sub-domains within the same CA1PC, but teasing apart their specific contributions remains experimentally challenging. We implemented a biophysically realistic model of CA1PCs using 40 digitally reconstructed morphologies and constraining synaptic numbers, locations, amplitude, and kinetics with available experimental data. ..."
337.  Firing neocortical layer V pyramidal neuron (Reetz et al. 2014; Stadler et al. 2014)
Neocortical Layer V model with firing behaviour adjusted to in vitro observations. The model was used to investigate the effects of IFN and PKC on the excitability of neurons (Stadler et al 2014, Reetz et al. 2014). The model contains new channel simulations for HCN1, HCN2 and the big calcium dependent potassium channel BK.
338.  Firing patterns in stuttering fast-spiking interneurons (Klaus et al. 2011)
This is a morphologically extended version of the fast-spiking interneuron by Golomb et al. (2007). The model captures the stuttering firing pattern and subthreshold oscillations in response to step current input as observed in many cortical and striatal fast-spiking cells.
339.  Firing patterns of CA3 hippocampal neurons (Soldado-Magraner et al. 2019)
" ... Here we demonstrate that the intrinsic firing patterns of CA3 neurons of the rat hippocampus in vitro undergo rapid long-term plasticity in response to a few minutes of only subthreshold synaptic conditioning. This plasticity on the spike-timing could also be induced by intrasomatic injection of subthreshold depolarizing pulses and was blocked by kinase inhibitors, indicating that discharge dynamics are modulated locally. Cluster analysis of firing patterns before and after conditioning revealed systematic transitions towards adapting and intrinsic burst behaviours, irrespective of the patterns initially exhibited by the cells. We used a conductance-based model to decide appropriate pharmacological blockade, and found that the observed transitions are likely due to recruitment of low-voltage calcium and Kv7 potassium conductances. We conclude that CA3 neurons adapt their conductance profile to the subthreshold activity of their input, so that their intrinsic firing pattern is not a static signature, but rather a reflection of their history of subthreshold activity. In this way, recurrent output from CA3 neurons may collectively shape the temporal dynamics of their embedding circuits."
340.  Fly lobular plate VS cell (Borst and Haag 1996, et al. 1997, et al. 1999)
In a series of papers the authors conducted experiments to develop understanding and models of fly visual system HS, CS, and VS neurons. This model recreates the VS neurons from those papers with enough success to merit approval by Borst although some discrepancies remain (see readme).
341.  Fractional leaky integrate-and-fire model (Teka et al. 2014)
We developed the Fractional Leaky Integrate-and-Fire model that can produce downward and upward spike time adaptions observed on pyramidal cells.The adaptation emerges from the fractional exponent of the voltage dynamics.
342.  Frog second-order vestibular neuron models (Rossert et al. 2011)
This implements spiking Hodgkin-Huxley type models of tonic and phasic second-order vestibular neurons. Models fitted to intracellular spike and membrane potential recordings from frog (Rana temporaria). The models can be stimulated by intracellular step current, frequency current (ZAP) or synaptic stimulation.
343.  FS Striatal interneuron: K currents solve signal-to-noise problems (Kotaleski et al 2006)
... We show that a transient potassium (KA) current allows the Fast Spiking (FS) interneuron to strike a balance between sensitivity to correlated input and robustness to noise, thereby increasing its signal-to-noise ratio (SNR). First, a compartmental FS neuron model was created to match experimental data from striatal FS interneurons in cortex–striatum–substantia nigra organotypic cultures. Densities of sodium, delayed rectifier, and KA channels were optimized to replicate responses to somatic current injection. Spontaneous AMPA and GABA synaptic currents were adjusted to the experimentally measured amplitude, rise time, and interevent interval histograms. Second, two additional adjustments were required to emulate the remaining experimental observations. GABA channels were localized closer to the soma than AMPA channels to match the synaptic population reversal potential. Correlation among inputs was required to produce the observed firing rate during up-states. In this final model, KA channels were essential for suppressing down-state spikes while allowing reliable spike generation during up-states. ... Our results suggest that KA channels allow FS interneurons to operate without a decrease in SNR during conditions of increased dopamine, as occurs in response to reward or anticipated reward. See paper for more and details.
344.  Fully continuous Pinsky-Rinzel model for bifurcation analysis (Atherton et al. 2016)
The original, 2-compartment, CA3 cell, Pinsky-Rinzel model (Pinsky, Rinzel 1994) has several discontinuous functions that prevent the use of standard bifurcation analysis tools to study the model. Here we present a modified, fully continuous system that captures the behaviour of the original model, while permitting the use of available numerical continuation software to perform full-system bifurcation and fast-slow analysis in XPPAUT.
345.  Function and energy constrain neuronal biophysics in coincidence detection (Remme et al 2018)
" ... We use models of conductance-based neurons constrained by experimentally observed characteristics with parameters varied within a physiologically realistic range. Our study shows that neuronal design of MSO cells does not compromise on function, but favors energetically less costly cell properties where possible without interfering with function."
346.  Functional properties of dendritic gap junctions in Cerebellar Golgi cells (Szoboszlay et al. 2016)
" ... We investigated the properties of gap junctions in cerebellar interneurons by combining paired somato-somatic and somato-dendritic recordings, anatomical reconstructions, immunohistochemistry, electron microscopy, and modeling. By fitting detailed compartmental models of Golgi cells to their somato-dendritic voltage responses, we determined their passive electrical properties and the mean gap junction conductance (0.9 nS). ..."
347.  Functional structure of mitral cell dendritic tuft (Djurisic et al. 2008)
The computational modeling component of Djurisic et al. 2008 addressed two primary questions: whether amplification by active currents is necessary to explain the relatively mild attenuation suffered by tuft EPSPs spreading along the primary dendrite to the soma; what accounts for the relatively uniform peak EPSP amplitude throughout the tuft. These simulations show that passive spread from tuft to soma is sufficient to yield the low attenuation of tuft EPSPs, and that random distribution of a biologically plausible number of excitatory synapses throughout the tuft can produce the experimentally observed uniformity of depolarization.
348.  Gamma and theta rythms in biophysical models of hippocampus circuits (Kopell et al. 2011)
" ... the main rhythms displayed by the hippocampus, the gamma (30–90 Hz) and theta (4–12 Hz) rhythms. We concentrate on modeling in vitro experiments, but with an eye toward possible in vivo implications. ... We use simpler biophysical models; all cells have a single compartment only, and the interneurons are restricted to two types: fast-spiking (FS) basket cells and oriens lacunosum-moleculare (O-LM) cells. ... , we aim not so much at reproducing dynamics in great detail, but at clarifying the essential mechanisms underlying the production of the rhythms and their interactions (Kopell, 2005). ..."
349.  Gamma genesis in the basolateral amygdala (Feng et al 2019)
Using in vitro and in vivo data we develop the first large-scale biophysically and anatomically realistic model of the basolateral amygdala nucleus (BL), which reproduces the dynamics of the in vivo local field potential (LFP). Significantly, it predicts that BL intrinsically generates the transient gamma oscillations observed in vivo. The model permitted exploration of the poorly understood synaptic mechanisms underlying gamma genesis in BL, and the model's ability to compute LFPs at arbitrary numbers of recording sites provided insights into the characteristics of the spatial properties of gamma bursts. Furthermore, we show how gamma synchronizes principal cells to overcome their low firing rates while simultaneously promoting competition, potentially impacting their afferent selectivity and efferent drive, and thus emotional behavior.
350.  Gap junction coupled network of striatal fast spiking interneurons (Hjorth et al. 2009)
Gap junctions between striatal FS neurons has very weak ability to synchronise spiking. Input uncorrelated between neighbouring neurons is shunted, while correlated input is not.
351.  Gap-junction coupled network activity depends on coupled dendrites diameter (Gansert et al. 2007)
"... We have previously shown that the amplitude of electrical signals propagating across gap-junctionally coupled passive cables is maximized at a unique diameter. This suggests that threshold-dependent signals may propagate through gap junctions for a finite range of diameters around this optimal value. Here we examine the diameter dependence of action potential propagation across model networks of dendro-dendritically coupled neurons. The neurons in these models have passive soma and dendrites and an action potential-generating axon. We show that propagation of action potentials across gap junctions occurs only over a finite range of dendritic diameters and that propagation delay depends on this diameter. ...". See paper for more and details.
352.  Gating of steering signals through phasic modulation of reticulospinal neurons (Kozlov et al. 2014)
" ... We use the lamprey as a model for investigating the role of this phasic modulation of the reticulospinal activity, because the brainstem–spinal cord networks are known down to the cellular level in this phylogenetically oldest extant vertebrate. We describe how the phasic modulation of reticulospinal activity from the spinal CPG ensures reliable steering/turning commands without the need for a very precise timing of on- or offset, by using a biophysically detailed large-scale (19,600 model neurons and 646,800 synapses) computational model of the lamprey brainstem–spinal cord network. To verify that the simulated neural network can control body movements, including turning, the spinal activity is fed to a mechanical model of lamprey swimming. ..."
353.  GC model (Beining et al 2017)
A companion modeldb entry (NEURON only) to modeldb accession number 231862.
354.  Generating neuron geometries for detailed 3D simulations using AnaMorph (Morschel et al 2017)
"Generating realistic and complex computational domains for numerical simulations is often a challenging task. In neuroscientific research, more and more one-dimensional morphology data is becoming publicly available through databases. This data, however, only contains point and diameter information not suitable for detailed three-dimensional simulations. In this paper, we present a novel framework, AnaMorph, that automatically generates water-tight surface meshes from one-dimensional point-diameter files. These surface triangulations can be used to simulate the electrical and biochemical behavior of the underlying cell. ..."
355.  Generic Bi-directional Real-time Neural Interface (Zrenner et al. 2010)
Matlab/Simulink toolkit for generic multi-channel short-latency bi-directional neural-computer interactions. High-bandwidth (> 10 megabit per second) neural recording data can be analyzed in real-time while simultaneously generating specific complex electrical stimulation feedback with deterministically timed responses at sub-millisecond resolution. The commercially available 60-channel extracellular multi-electrode recording and stimulation set-up (Multichannelsystems GmbH MEA60) is used as an example hardware implementation.
356.  Genetic, biochemical and bioelectrical dynamics in pattern regulation (Pietak & Levin 2017)
"Gene regulatory networks (GRNs) describe interactions between gene products and transcription factors that control gene expression. In combination with reaction–diffusion models, GRNs have enhanced comprehension of biological pattern formation. However, although it is well known that biological systems exploit an interplay of genetic and physical mechanisms, instructive factors such as transmembrane potential (Vmem) have not been integrated into full GRN models. Here we extend regulatory networks to include bioelectric signalling, developing a novel synthesis: the bioelectricity-integrated gene and reaction (BIGR) network. ..."
357.  Global and multiplexed dendritic computations under in vivo-like conditions (Ujfalussy et al 2018)
"The input-output transformation of neurons under in vivo conditions is unknown. Ujfalussy et al. use a model-based approach to show that linear integration with a single global dendritic nonlinearity can accurately predict the response of neurons to naturalistic synaptic input patterns."
358.  Global structure, robustness, and modulation of neuronal models (Goldman et al. 2001)
"The electrical characteristics of many neurons are remarkably robust in the face of changing internal and external conditions. At the same time, neurons can be highly sensitive to neuromodulators. We find correlates of this dual robustness and sensitivity in a global analysis of the structure of a conductance-based model neuron. ..."
359.  Globus pallidus multi-compartmental model neuron with realistic morphology (Gunay et al. 2008)
"Globus pallidus (GP) neurons recorded in brain slices show significant variability in intrinsic electrophysiological properties. To investigate how this variability arises, we manipulated the biophysical properties of GP neurons using computer simulations. ... Our results indicated that most of the experimental variability could be matched by varying conductance densities, which we confirmed with additional partial block experiments. Further analysis resulted in two key observations: (1) each voltage-gated conductance had effects on multiple measures such as action potential waveform and spontaneous or stimulated spike rates; and (2) the effect of each conductance was highly dependent on the background context of other conductances present. In some cases, such interactions could reverse the effect of the density of one conductance on important excitability measures. ..."
360.  Globus pallidus neuron models with differing dendritic Na channel expression (Edgerton et al., 2010)
A set of 9 multi-compartmental rat GP neuron models (585 compartments) differing only in their expression of dendritic fast sodium channels were compared in their synaptic integration properties. Dendritic fast sodium channels were found to increase the importance of distal synapses (both excitatory AND inhibitory), increase spike timing variability with in vivo-like synaptic input, and make the model neurons highly sensitive to clustered synchronous excitation.
361.  Goldfish Mauthner cell (Medan et al 2017)
" ...In fish, evasion of a diving bird that breaks the water surface depends on integrating visual and auditory stimuli with very different characteristics. How do neurons process such differential sensory inputs at the dendritic level? For that we studied the Mauthner-cells (M-cells) in the goldfish startle circuit, which receive visual and auditory inputs via two separate dendrites, both accessible for in vivo recordings. We asked if electrophysiological membrane properties and dendrite morphology, studied in vivo, play a role in selective sensory processing in the M-cell. Our results show that anatomical and electrophysiological differences between the dendrites combine to produce stronger attenuation of visually evoked post synaptic potentials (PSPs) than to auditory evoked PSPs. Interestingly, our recordings showed also cross-modal dendritic interaction, as auditory evoked PSPs invade the ventral dendrite (VD) as well as the opposite, visual PSPs invade the lateral dendrite (LD). However, these interactions were asymmetrical with auditory PSPs being more prominent in the VD than visual PSPs in the LD. Modelling experiments imply that this asymmetry is caused by active conductances expressed in the proximal segments of the VD. ..."
362.  GP Neuron, somatic and dendritic phase response curves (Schultheiss et al. 2011)
Phase response analysis of a GP neuron model showing type I PRCs for somatic inputs and type II PRCs for dendritic excitation. Analysis of intrinsic currents underlying type II dendritic PRCs.
363.  GPi/GPe neuron models (Johnson and McIntyre 2008)
Model files for two types of non-human primate neurons used in the paper: simplified versions of 1) a GPi neuron and 2) a GPe axon collateralizing in GPi en route to STN.
364.  Granule Cells of the Olfactory Bulb (Simoes_De_Souza et al. 2014)
Electrical responses of three classes of granule cells of the olfactory bulb to synaptic activation in different dendritic locations. The constructed models were based on morphological detailed compartmental reconstructions of three granule cell classes of the olfactory bulb with active dendrites described by Bhalla and Bower (J. Neurophysiol. 69: 1948-1965, 1993) and dendritic spine distributions described by Woolf et al. (J. Neurosci. 11: 1837-1854, 1991). The computational studies with the model neurons showed that different quantities of spines have to be activated in each dendritic region to induce an action potential, which always was originated in the active terminal dendrites, independently of the location of the stimuli and the morphology of the dendritic tree.
365.  Grid cell oscillatory interference with noisy network oscillators (Zilli and Hasselmo 2010)
To examine whether an oscillatory interference model of grid cell activity could work if the oscillators were noisy neurons, we implemented these simulations. Here the oscillators are networks (either synaptically- or gap-junction--coupled) of one or more noisy neurons (either Izhikevich's simple model or a Hodgkin-Huxley--type biophysical model) which drive a postsynaptic cell (which may be integrate-and-fire, resonate-and-fire, or the simple model) which should fire spatially as a grid cell if the simulation is successful.
366.  Grid cells from place cells (Castro & Aguiar, 2014)
" ...Here we present a novel model for the emergence of gridlike firing patterns that stands on two key hypotheses: (1) spatial information in GCs is provided from PC activity and (2) grid fields result from a combined synaptic plasticity mechanism involving inhibitory and excitatory neurons mediating the connections between PCs and GCs. ..."
367.  HERG K+ channels spike-frequency adaptation (Chiesa et al 1997)
Spike frequency adaptation has contributions from the IHERG current (encoded by the human eag-related gene (HERG); Warmke & Ganetzky, 1994), which develops with slow kinetics during depolarization and contributes to the repolarization of the long action potentials typically present in the heart. IHERG is one of the delayed rectifier currents (IK(r)) of the heart, and HERG mutations are associated with one of the cardiac arrhythmia LQT syndromes (LQT2). See paper for more and details.
368.  HH model neuron of the Suprachiasmatic Nucleus including a persistent Na+ channel (Paul et al 2016)
Hodgkin-Huxley style model for a neuron of the Suprachiasmatic Nucleus (SCN). Modified from DeWoskin et al, PNAS, 2015 to include a persistent sodium current. The model is used to study the role of the kinase GSK3 in regulating the electrical activity of SCN neurons through a persistent sodium current.
369.  High entrainment constrains synaptic depression in a globular bushy cell (Rudnicki & Hemmert 2017)
" ... Here we show how different levels of synaptic depression shape firing properties of GBCs in in vivo-like conditions using computer simulations. We analyzed how an interplay of synaptic depression (0 % to 70 %) and the number of auditory nerve fiber inputs (10 to 70) contributes to the variability of the experimental data from previous studies. ... Overall, this study helps to understand how synaptic properties shape temporal processing in the auditory system. It also integrates, compares, and reconciles results of various experimental studies."
370.  High frequency stimulation of the Subthalamic Nucleus (Rubin and Terman 2004)
" ... Using a computational model, this paper considers the hypothesis that DBS works by replacing pathologically rhythmic basal ganglia output with tonic, high frequency firing. In our simulations of parkinsonian conditions, rhythmic inhibition from GPi to the thalamus compromises the ability of thalamocortical relay (TC) cells to respond to depolarizing inputs, such as sensorimotor signals. High frequency stimulation of STN regularizes GPi firing, and this restores TC responsiveness, despite the increased frequency and amplitude of GPi inhibition to thalamus that result. We provide a mathematical phase plane analysis of the mechanisms that determine TC relay capabilities in normal, parkinsonian, and DBS states in a reduced model. This analysis highlights the differences in deinactivation of the low-threshold calcium T -current that we observe in TC cells in these different conditions. ..."
371.  Hippocampal CA1 pyramidal cell demonstrating dynamic mode switching (Berteau & Bullock 2020)
A simulated proposed single-cell mechanism for CA1’s behavior as an associative mismatch detector. Shifts in spiking mode (accomplished via KCNQ interaction with chloride leak currents) signal matches vs. mismatches.
372.  Hippocampal CA3 network and circadian regulation (Stanley et al. 2013)
This model produces the hippocampal CA3 neural network model used in the paper below. It has two modes of operation, a default mode and a circadian mode. In the circadian mode, parameters are swept through a range of values. This model can be quite easily adapted to produce theta and gamma oscillations, as certain parameter sweeps will reveal (see Figures). BASH scripts interact with GENESIS 2.3 to implement parameter sweeps. The model contains four cell types derived from prior papers. CA3 pyramidal are derived from Traub et al (1991); Basket, stratum oriens (O-LM), and Medial Septal GABAergic (MSG) interneurons are taken from Hajos et al (2004).
373.  Hippocampus CA1 Interneuron Specific 3 (IS3) in vivo-like virtual NN simulations (Luo et al 2020)
"Disinhibition is a widespread circuit mechanism for information selection and transfer. In the hippocampus, disinhibition of principal cells is provided by the interneuron-specific interneurons that express the vasoactive intestinal polypeptide (VIP-IS) and innervate selectively inhibitory interneurons. By combining optophysiological experiments with computational models, we determined the impact of synaptic inputs onto the network state-dependent recruitment of VIP-IS cells. We found that VIP-IS cells fire spikes in response to both the Schaffer collateral and the temporoammonic pathway activation. Moreover, by integrating their intrinsic and synaptic properties into computational models, we predicted recruitment of these cells between the rising phase and peak of theta oscillation and during ripples. Two-photon Ca2+-imaging in awake mice supported in part the theoretical predictions, revealing a significant speed modulation of VIP-IS cells and their preferential albeit delayed recruitment during theta-run epochs, with estimated firing at the rising phase and peak of the theta cycle. However, it also uncovered that VIP-IS cells are not activated during ripples. Thus, given the preferential theta-modulated firing of VIP-IS cells in awake hippocampus, we postulate that these cells may be important for information gating during spatial navigation and memory encoding."
374.  Hippocampus CA1 pyramidal model with Na channel exhibiting slow inactivation (Menon et al. 2009)
These NEURON simulations show the effect of prolonged inactivation of sodium channels on attenuation of trains of backpropagating action potentials (bAPs). The new sodium channel model is a Markov model derived using a state-mutating genetic algorithm, as described in the paper.
375.  Hodgkin-Huxley model of persistent activity in PFC neurons (Winograd et al. 2008) (NEURON python)
The paper demonstrate a form of graded persistent activity activated by hyperpolarization. This phenomenon is modeled based on a slow calcium regulation of Ih, similar to that introduced earlier for thalamic neurons (see Destexhe et al., J Neurophysiol. 1996). The only difference is that the calcium signal is here provided by the high-threshold calcium current (instead of the low-threshold calcium current in thalamic neurons).
376.  Hodgkin-Huxley model of persistent activity in prefrontal cortex neurons (Winograd et al. 2008)
The paper demonstrate a form of graded persistent activity activated by hyperpolarization. This phenomenon is modeled based on a slow calcium regulation of Ih, similar to that introduced earlier for thalamic neurons (see Destexhe et al., J Neurophysiol. 1996). The only difference is that the calcium signal is here provided by the high-threshold calcium current (instead of the low-threshold calcium current in thalamic neurons).
377.  Hodgkin-Huxley models of different classes of cortical neurons (Pospischil et al. 2008)
"We review here the development of Hodgkin- Huxley (HH) type models of cerebral cortex and thalamic neurons for network simulations. The intrinsic electrophysiological properties of cortical neurons were analyzed from several preparations, and we selected the four most prominent electrophysiological classes of neurons. These four classes are 'fast spiking', 'regular spiking', 'intrinsically bursting' and 'low-threshold spike' cells. For each class, we fit 'minimal' HH type models to experimental data. ..."
378.  Hodgkin-Huxley simplifed 2D and 3D models (Lundstrom et al. 2009)
"Neuronal responses are often characterized by the firing rate as a function of the stimulus mean, or the f–I curve. We introduce a novel classification of neurons into Types A, B&#8722;, and B+ according to how f–I curves are modulated by input fluctuations. ..."
379.  Hodgkin-Huxley with dynamic ion concentrations (Hubel and Dahlem, 2014)
The classical Hodgkin--Huxley (HH) model neglects the time-dependence of ion concentrations in spiking dynamics. The dynamics is therefore limited to a time scale of milliseconds, which is determined by the membrane capacitance multiplied by the resistance of the ion channels, and by the gating time constants. This model includes slow dynamics in an extended HH framework that simulates time-dependent ion concentrations, pumps, and buffers. Fluxes across the neuronal membrane change intra- and extracellular ion concentrations, whereby the latter can also change through contact to reservoirs in the surroundings. The dynamics on three distinct slow times scales is determined by the cell volume-to-surface-area ratio and the membrane permeability (seconds), the buffer time constants (tens of seconds), and the slower backward buffering (minutes to hours). The modulatory dynamics and the newly emerging excitable dynamics corresponds to pathological conditions observed in epileptiform burst activity, and spreading depression in migraine aura and stroke, respectively.
380.  Homeostatic synaptic plasticity (Rabinowitch and Segev 2006a,b)
(2006a): "We investigated analytically and numerically the interplay between two opposing forms of synaptic plasticity: positive-feedback, long-term potentiation/depression (LTP/LTD), and negative-feedback, homeostatic synaptic plasticity (HSP). A detailed model of a CA1 pyramidal neuron, with numerous HSP-modifiable dendritic synapses, demonstrates that HSP may have an important role in selecting which spatial patterns of LTP/LTD are to last. ... Despite the negative-feedback nature of HSP, under both local and global HSP, numerous synaptic potentiations/depressions can persist. These experimentally testable results imply that HSP could be significantly involved in shaping the spatial distribution of synaptic weights in the dendrites and not just normalizing it, as is currently believed." (2006b): "Homeostatic synaptic plasticity (HSP) is an important mechanism attributed with the slow regulation of the neuron's activity. Whenever activity is chronically enhanced, HSP weakens the weights of the synapses in the dendrites and vice versa. Because dendritic morphology and its electrical properties partition the dendritic tree into functional compartments, we set out to explore the interplay between HSP and dendritic compartmentalization. ... The spatial distribution of synaptic weights throughout the dendrites will markedly differ under the local versus global HSP mechanisms. We suggest an experimental paradigm to unravel which type of HSP mechanism operates in the dendritic tree. The answer to this question will have important implications to our understanding of the functional organization of the neuron."
381.  Hotspots of dendritic spine turnover facilitates new spines and NN sparsity (Frank et al 2018)
Model for the following publication: Adam C. Frank, Shan Huang, Miou Zhou, Amos Gdalyahu, George Kastellakis, Panayiota Poirazi, Tawnie K. Silva, Ximiao Wen, Joshua T. Trachtenberg, and Alcino J. Silva Hotspots of Dendritic Spine Turnover Facilitate Learning-related Clustered Spine Addition and Network Sparsity
382.  How adaptation makes low firing rates robust (Sherman & Ha 2017)
"Low frequency firing is modeled by Type 1 neurons with a SNIC (saddle node on an invariant circle), but, because of the vertical slope of the square-root-like f–I curve, low f only occurs over a narrow range of I. When an adaptive current is added, however, the f–I curve is linearized, and low f occurs robustly over a large I range. Ermentrout (Neural Comput. 10(7):1721-1729, 1998) showed that this feature of adaptation paradoxically arises from the SNIC that is responsible for the vertical slope. We show, using a simplified Hindmarsh–Rose neuron with negative feedback acting directly on the adaptation current, that whereas a SNIC contributes to linearization, in practice linearization over a large interval may require strong adaptation strength. We also find that a type 2 neuron with threshold generated by a Hopf bifurcation can also show linearization if adaptation strength is strong. Thus, a SNIC is not necessary. More fundamental than a SNIC is stretching the steep region near threshold, which stems from sufficiently strong adaptation, though a SNIC contributes if present. In a more realistic conductance-based model, Morris–Lecar, with negative feedback acting on the adaptation conductance, an additional assumption that the driving force of the adaptation current is independent of I is needed. If this holds, strong adaptive conductance is both necessary and sufficient for linearization of f–I curves of type 2 f–I curves."
383.  Hyperbolic model (Daneshzand et al 2017)
A modified Izhikevich neuron model to address the switching patterns of neuronal firing seen in Parkinson's Disease.
384.  I A in Kenyon cells resemble Shaker currents (Pelz et al 1999)
Cultured Kenyon cells from the mushroom body of the honeybee, Apis mellifera, show a voltage-gated, fast transient K1 current that is sensitive to 4-aminopyridine, an A current. The kinetic properties of this A current and its modulation by extracellular K1 ions were investigated in vitro with the whole cell patch-clamp technique. The A current was isolated from other voltage-gated currents either pharmacologically or with suitable voltage-clamp protocols. Hodgkin- and Huxley-style mathematical equations were used for the description of this current and for the simulation of action potentials in a Kenyon cell model. The data of the A current were incorporated into a reduced computational model of the voltage-gated currents of Kenyon cells. In addition, the model contained a delayed rectifier K current, a Na current, and a leakage current. The model reproduces several experimental features and makes predictions. See paper for details and results.
385.  I&F recurrent networks with current- or conductance-based synapses (Cavallari et al. 2014)
Recurrent networks of two populations (excitatory and inhibitory) of randomly connected Leaky Integrate-and-Fire (LIF) neurons with either current- or conductance-based synapses from the paper S. Cavallari, S. Panzeri and A. Mazzoni (2014)
386.  IA and IT interact to set first spike latency (Molineux et al 2005)
Using patch clamp and modeling, we illustrate that spike latency characteristics are the product of an interplay between I(A) and low-threshold calcium current (I(T)) that requires a steady-state difference in the inactivation parameters of the currents. Furthermore, we show that the unique first-spike latency characteristics of stellate cells have important implications for the integration of coincident IPSPs and EPSPs, such that inhibition can shift first-spike latency to differentially modulate the probability of firing.
387.  Ih levels roles in bursting and regular-spiking subiculum pyramidal neurons (van Welie et al 2006)
Pyramidal neurons in the subiculum typically display either bursting or regular-spiking behavior. ... Here we report that bursting neurons posses a hyperpolarization-activated cation current (Ih) that is two-fold larger (conductance: 5.3 ± 0.5 nS) than in regularspiking neurons (2.2 ± 0.6 nS), while Ih exhibits similar voltage-dependent and kinetic properties in both classes of neurons. Bursting and regular-spiking neurons display similar morphology. The difference in Ih between the two classes is not responsible for the distinct firing patterns, since neither pharmacological blockade of Ih nor enhancement of Ih using a dynamic clamp affects the qualitative firing patterns. Instead, the difference in Ih between bursting and regular-spiking neurons determines the temporal integration of evoked synaptic input from the CA1 area. In response to 50 Hz stimulation, bursting neurons, with a large Ih, show ~50% less temporal summation than regular-spiking neurons. ... A computer simulation model of a subicular neuron with the properties of either a bursting or a regular-spiking neuron confirmed the pivotal role of Ih in temporal integration of synaptic input. These data suggest that in the subicular network, bursting neurons are better suited to discriminate the content of high frequency input, such as that occurring during gamma oscillations, compared to regular-spiking neurons. See paper for more and details.
388.  Impact of dendritic atrophy on intrinsic and synaptic excitability (Narayanan & Chattarji, 2010)
These simulations examined the atrophy induced changes in electrophysiological properties of CA3 pyramidal neurons. We found these neurons change from bursting to regular spiking as atrophy increases. Region-specific atrophy induced region-specific increases in synaptic excitability in a passive dendritic tree. All dendritic compartments of an atrophied neuron had greater synaptic excitability and a larger voltage transfer to the soma than the control neuron.
389.  Impact of dendritic size and topology on pyramidal cell burst firing (van Elburg and van Ooyen 2010)
The code provided here was written to systematically investigate which of the physical parameters controlled by dendritic morphology underlies the differences in spiking behaviour observed in different realizations of the 'ping-pong'-model. Structurally varying dendritic topology and length in a simplified model allows us to separate out the physical parameters derived from morphology underlying burst firing. To perform the parameter scans we created a new NEURON tool the MultipleRunControl which can be used to easily set up a parameter scan and write the simulation results to file. Using this code we found that not input conductance but the arrival time of the return current, as measured provisionally by the average electrotonic path length, determines whether the pyramidal cell (with ping-pong model dynamics) will burst or fire single spikes.
390.  Impact of fast Na channel inact. on AP threshold & synaptic integration (Platkiewicz & Brette 2011)
Slope-threshold relationship with noisy inputs, in the adaptive threshold model.
391.  Impedance spectrum in cortical tissue: implications for LFP signal propagation (Miceli et al. 2017)
" ... Here, we performed a detailed investigation of the frequency dependence of the conductivity within cortical tissue at microscopic distances using small current amplitudes within the typical (neuro)physiological micrometer and sub-nanoampere range. We investigated the propagation of LFPs, induced by extracellular electrical current injections via patch-pipettes, in acute rat brain slice preparations containing the somatosensory cortex in vitro using multielectrode arrays. Based on our data, we determined the cortical tissue conductivity over a 100-fold increase in signal frequency (5-500 Hz). Our results imply at most very weak frequency-dependent effects within the frequency range of physiological LFPs. Using biophysical modeling, we estimated the impact of different putative impedance spectra. Our results indicate that frequency dependencies of the order measured here and in most other studies have negligible impact on the typical analysis and modeling of LFP signals from extracellular brain recordings."
392.  INa and IKv4.3 heterogeneity in canine LV myocytes (Flaim et al 2006)
"The roles of sustained components of INa and IKv43 in shaping the action potentials (AP) of myocytes isolated from the canine left ventricle (LV) have not been studied in detail. Here we investigate the hypothesis that these two currents can contribute substantially to heterogeneity of early repolarization and arrhythmic risk.... The resulting simulations illustrate ways in which KChIP2- and Ca2+- dependent control of IKv43 can result in a sustained outward current that can neutralize INaL in a rate- and myocyte subtype-dependent manner. Both these currents appear to play significant roles in modulating AP duration and rate dependence in midmyocardial myocytes. ... By design, these models allow upward integration into organ models or may be used as a basis for further investigations into cellular heterogeneities." See paper for more and details.
393.  Increased computational accuracy in multi-compartmental cable models (Lindsay et al. 2005)
Compartmental models of dendrites are the most widely used tool for investigating their electrical behaviour. Traditional models assign a single potential to a compartment. This potential is associated with the membrane potential at the centre of the segment represented by the compartment. All input to that segment, independent of its location on the segment, is assumed to act at the centre of the segment with the potential of the compartment. By contrast, the compartmental model introduced in this article assigns a potential to each end of a segment, and takes into account the location of input to a segment on the model solution by partitioning the effect of this input between the axial currents at the proximal and distal boundaries of segments. For a given neuron, the new and traditional approaches to compartmental modelling use the same number of locations at which the membrane potential is to be determined, and lead to ordinary differential equations that are structurally identical. However, the solution achieved by the new approach gives an order of magnitude better accuracy and precision than that achieved by the latter in the presence of point process input.
394.  Inferring connection proximity in electrically coupled networks (Cali et al. 2007)
In order to explore electrical coupling in the nervous system and its network-level organization, it is imperative to map the electrical synaptic microcircuits, in analogy with in vitro studies on monosynaptic and disynaptic chemical coupling. However, walking from cell to cell over large distances with a glass pipette is challenging, and microinjection of (fluorescent) dyes diffusing through gap-junctions remains so far the only method available to decipher such microcircuits even though technical limitations exist. Based on circuit theory, we derived analytical descriptions of the AC electrical coupling in networks of isopotential cells. We then proposed an operative electrophysiological protocol to distinguish between direct electrical connections and connections involving one or more intermediate cells. This method allows inferring the number of intermediate cells, generalizing the conventional coupling coefficient, which provides limited information. We provide here some analysis and simulation scripts that used to test our method through computer simulations, in vitro recordings, theoretical and numerical methods. Key words: Gap-Junctions; Electrical Coupling; Networks; ZAP current; Impedance.
395.  Influence of dendritic structure on neocortical neuron firing patterns (Mainen and Sejnowski 1996)
This package contains compartmental models of four reconstructed neocortical neurons (layer 3 Aspiny, layer 4 Stellate, layer 3 and layer 5 Pyramidal neurons) with active dendritic currents using NEURON. Running this simulation demonstrates that an entire spectrum of firing patterns can be reproduced in this set of model neurons which share a common distribution of ion channels and differ only in their dendritic geometry. The reference paper is: Z. F. Mainen and T. J. Sejnowski (1996) Influence of dendritic structure on firing pattern in model neocortical neurons. Nature 382: 363-366. See also and More info in readme.txt file below made visible by clicking on the patdemo folder and then on the readme.txt file.
396.  Information transmission in cerebellar granule cell models (Rossert et al. 2014)
" ... In this modeling study we analyse how electrophysiological granule cell properties and spike sampling influence information coded by firing rate modulation, assuming no signal-related, i.e., uncorrelated inhibitory feedback (open-loop mode). A detailed one-compartment granule cell model was excited in simulation by either direct current or mossy-fiber synaptic inputs. Vestibular signals were represented as tonic inputs to the flocculus modulated at frequencies up to 20 Hz (approximate upper frequency limit of vestibular-ocular reflex, VOR). Model outputs were assessed using estimates of both the transfer function, and the fidelity of input-signal reconstruction measured as variance-accounted-for. The detailed granule cell model with realistic mossy-fiber synaptic inputs could transmit infoarmation faithfully and linearly in the frequency range of the vestibular-ocular reflex. ... "
397.  Infraslow intrinsic rhythmogenesis in a subset of AOB projection neurons (Gorin et al 2016)
We investigated patterns of spontaneous neuronal activity in mouse accessory olfactory bulb mitral cells, the direct neural link between vomeronasal sensory input and limbic output. Both in vitro and in vivo, we identify a subpopulation of mitral cells that exhibit slow stereotypical rhythmic discharge. In intrinsically rhythmogenic neurons, these periodic activity patterns are maintained in absence of fast synaptic drive. The physiological mechanism underlying mitral cell autorhythmicity involves cyclic activation of three interdependent ionic conductances: subthreshold persistent Na(+) current, R-type Ca(2+) current, and Ca(2+)-activated big conductance K(+) current. Together, the interplay of these distinct conductances triggers infraslow intrinsic oscillations with remarkable periodicity, a default output state likely to affect sensory processing in limbic circuits. The model reproduces the intrinsic firing in a reconstructed single AOB mitral cell with ion channels kinetics fitted to experimental measurements of their steady state and time course.
398.  Inhibition of bAPs and Ca2+ spikes in a multi-compartment pyramidal neuron model (Wilmes et al 2016)
"Synaptic plasticity is thought to induce memory traces in the brain that are the foundation of learning. To ensure the stability of these traces in the presence of further learning, however, a regulation of plasticity appears beneficial. Here, we take up the recent suggestion that dendritic inhibition can switch plasticity of excitatory synapses on and off by gating backpropagating action potentials (bAPs) and calcium spikes, i.e., by gating the coincidence signals required for Hebbian forms of plasticity. We analyze temporal and spatial constraints of such a gating and investigate whether it is possible to suppress bAPs without a simultaneous annihilation of the forward-directed information flow via excitatory postsynaptic potentials (EPSPs). In a computational analysis of conductance-based multi-compartmental models, we demonstrate that a robust control of bAPs and calcium spikes is possible in an all-or-none manner, enabling a binary switch of coincidence signals and plasticity. ..."
399.  Inhibitory plasticity balances excitation and inhibition (Vogels et al. 2011)
"Cortical neurons receive balanced excitatory and inhibitory synaptic currents. Such a balance could be established and maintained in an experience-dependent manner by synaptic plasticity at inhibitory synapses. We show that this mechanism provides an explanation for the sparse firing patterns observed in response to natural stimuli and fits well with a recently observed interaction of excitatory and inhibitory receptive field plasticity. ... Our results suggest an essential role of inhibitory plasticity in the formation and maintenance of functional cortical circuitry."
400.  Input Fluctuations effects on f-I curves (Arsiero et al. 2007)
"... We examined in vitro frequency versus current (f-I) relationships of layer 5 (L5) pyramidal cells of the rat medial prefrontal cortex (mPFC) using fluctuating stimuli. ...our results show that mPFC L5 pyramidal neurons retain an increased sensitivity to input fluctuations, whereas their sensitivity to the input mean diminishes to near zero. This implies that the discharge properties of L5 mPFC neurons are well suited to encode input fluctuations rather than input mean in their firing rates, with important consequences for information processing and stability of persistent activity at the network level."
401.  Integrate and fire model code for spike-based coincidence-detection (Heinz et al. 2001, others)
Model code relevant to three papers; two on level discrimination and one on masked detection at low frequencies.
402.  Interacting synaptic conductances during, distorting, voltage clamp (Poleg-Polsky and Diamond 2011)
This simulation examines the accuracy of the voltage clamp technique in detecting the excitatory and the inhibitory components of the synaptic drive.
403.  Interneuron Specific 3 Interneuron Model (Guet-McCreight et al, 2016)
In this paper we develop morphologically detailed multi-compartment models of Hippocampal CA1 interneuron specific 3 interneurons using cell current-clamp recordings and dendritic calcium imaging data. In doing so, we developed several variant models, as outlined in the associated README.html file.
404.  Interplay between somatic and dendritic inhibition promotes place fields (Pedrosa & Clopath 2020)
Hippocampal pyramidal neurons are thought to encode spatial information. A subset of these cells, named place cells, are active only when the animal traverses a specific region within the environment. Although vastly studied experimentally, the development and stabilization of place fields are not fully understood. Here, we propose a mechanistic model of place cell formation in the hippocampal CA1 region. Using our model, we reproduce place field dynamics observed experimentally and provide a mechanistic explanation for the stabilization of place fields. Finally, our model provides specific predictions on protocols to shift place field location.
405.  Intracortical synaptic potential modulation by presynaptic somatic potential (Shu et al. 2006, 2007)
" ... Here we show that the voltage fluctuations associated with dendrosomatic synaptic activity propagate significant distances along the axon, and that modest changes in the somatic membrane potential of the presynaptic neuron modulate the amplitude and duration of axonal action potentials and, through a Ca21- dependent mechanism, the average amplitude of the postsynaptic potential evoked by these spikes. These results indicate that synaptic activity in the dendrite and soma controls not only the pattern of action potentials generated, but also the amplitude of the synaptic potentials that these action potentials initiate in local cortical circuits, resulting in synaptic transmission that is a mixture of triggered and graded (analogue) signals."
406.  Intrinsic sensory neurons of the gut (Chambers et al. 2014)
A conductance base model of intrinsic neurons neurons in the gastrointestinal tract. The model contains all the major voltage-gated and calcium-gated currents observed in these neurons. This model can reproduce physiological observations such as the response to multiple brief depolarizing currents, prolonged depolarizing currents and hyperpolarizing currents. This model can be used to predict how different currents influence the excitability of intrinsic sensory neurons in the gut.
407.  Inverse stochastic resonance of cerebellar Purkinje cell (Buchin et al. 2016)
This code shows the simulations of the adaptive exponential integrate-and-fire model ( at different stimulus conditions. The parameters of the model were tuned to the Purkinje cell of cerebellum to reproduce the inhibiion of these cells by noisy current injections. Similar experimental protocols were also applied to the detailed biophysical model of Purkinje cells, de Shutter & Bower (1994) model. The repository also includes the XPPaut version of the model with the corresponding bifurcation analysis.
408.  Investigation of different targets in deep brain stimulation for Parkinson`s (Pirini et al. 2009)
"We investigated by a computational model of the basal ganglia the different network effects of deep brain stimulation (DBS) for Parkinson’s disease (PD) in different target sites in the subthalamic nucleus (STN), the globus pallidus pars interna (GPi), and the globus pallidus pars externa (GPe). A cellular-based model of the basal ganglia system (BGS), based on the model proposed by Rubin and Terman (J Comput Neurosci 16:211–235, 2004), was developed. ... Our results suggest that DBS in the STN could functionally restore the TC relay activity, while DBS in the GPe and in the GPi could functionally over-activate and inhibit it, respectively. Our results are consistent with the experimental and the clinical evidences on the network effects of DBS."
409.  Ion concentration dynamics as a mechanism for neuronal bursting (Barreto & Cressman 2011)
"We describe a simple conductance-based model neuron that includes intra and extracellular ion concentration dynamics and show that this model exhibits periodic bursting. The bursting arises as the fast-spiking behavior of the neuron is modulated by the slow oscillatory behavior in the ion concentration variables and vice versa. By separating these time scales and studying the bifurcation structure of the neuron, we catalog several qualitatively different bursting profiles that are strikingly similar to those seen in experimental preparations. Our work suggests that ion concentration dynamics may play an important role in modulating neuronal excitability in real biological systems."
410.  Ionic basis of alternans and Timothy Syndrome (Fox et al. 2002), (Zhu and Clancy 2007)
From Zhu and Clancy: "... Here we employ theoretical simulations to examine the effects of a Timothy Syndrome (TS) mutation in the L-type Ca2+ channel on cardiac dynamics over multiple scales, from a gene mutation to protein, cell, tissue, and finally the ECG, to connect a defective Ca2+ channel to arrhythmia susceptibility. ..."
411.  Ionic current model of a Hypoglossal Motoneuron (Purvis & Butera 2005)
"We have developed a single-compartment, electrophysiological, hypoglossal motoneuron (HM) model based primarily on experimental data from neonatal rat HMs. The model is able to reproduce the fine features of the HM action potential: the fast afterhyperpolarization, the afterdepolarization, and the medium-duration afterhyperpolarization (mAHP). The model also reproduces the repetitive firing properties seen in neonatal HMs and replicates the neuron’s response to pharmacological experiments. The model was used to study the role of specific ionic currents in HM firing and how variations in the densities of these currents may account for age dependent changes in excitability seen in HMs. ..."
412.  Ionic mechanisms of bursting in CA3 pyramidal neurons (Xu and Clancy 2008)
"... We present a single-compartment model of a CA3 hippocampal pyramidal neuron based on recent experimental data. We then use the model to determine the roles of primary depolarizing currents in burst generation. The single compartment model incorporates accurate representations of sodium (Na+) channels (NaV1.1) and T-type calcium (Ca2+) channel subtypes (CaV3.1, CaV3.2, and CaV3.3). Our simulations predict the importance of Na+ and T-type Ca2+ channels in hippocampal pyramidal cell bursting and reveal the distinct contribution of each subtype to burst morphology. We also performed fastslow analysis in a reduced comparable model, which shows that our model burst is generated as a result of the interaction of two slow variables, the T-type Ca2+ channel activation gate and the Ca2+-dependent potassium (K+) channel activation gate. The model reproduces a range of experimentally observed phenomena including afterdepolarizing potentials, spike widening at the end of the burst, and rebound. Finally, we use the model to simulate the effects of two epilepsy-linked mutations: R1648H in NaV1.1 and C456S in CaV3.2, both of which result in increased cellular excitability."
413.  Kenyon cells in the honeybee (Wustenberg et al 2004)
The mushroom body of the insect brain is an important locus for olfactory information processing and associative learning. ... Current- and voltage-clamp analyses were performed on cultured Kenyon cells from honeybees. ... Voltage-clamp analyses characterized a fast transient Na+ current (INa), a delayed rectifier K+ current (IK,V) and a fast transient K+ current (IK,A). Using the neurosimulator SNNAP, a Hodgkin-Huxley type model was developed and used to investigate the roles of the different currents during spiking. The model led to the prediction of a slow transient outward current (IK,ST) that was subsequently identified by reevaluating the voltage-clamp data. Simulations indicated that the primary currents that underlie spiking are INa and IK,V, whereas IK,A and IK,ST primarily determined the responsiveness of the model to stimuli such constant or oscillatory injections of current. See paper for more details.
414.  KV1 channel governs cerebellar output to thalamus (Ovsepian et al. 2013)
The output of the cerebellum to the motor axis of the central nervous system is orchestrated mainly by synaptic inputs and intrinsic pacemaker activity of deep cerebellar nuclear (DCN) projection neurons. Herein, we demonstrate that the soma of these cells is enriched with KV1 channels produced by mandatory multi-merization of KV1.1, 1.2 alpha andKV beta2 subunits. Being constitutively active, the K+ current (IKV1) mediated by these channels stabilizes the rate and regulates the temporal precision of self-sustained firing of these neurons. ... Through the use of multi-compartmental modelling and ... the physiological significance of the described functions for processing and communication of information from the lateral DCN to thalamic relay nuclei is established.
415.  Kv4.3, Kv1.4 encoded K channel in heart cells & tachy. (Winslow et al 1999, Greenstein et al 2000)
(1999) We present a model of the canine midmyocardial ventricular action potential and Ca2+ transient. The model is used to estimate the degree of functional upregulation and downregulation of Na/Ca exchanger protein and sarcoplasmic reticulum Ca ATPase in heart failure using data obtained from 2 different experimental protocols. (2000): A model of canine I:(to1) (the Ca(2+)-independent transient outward current) is formulated as the combination of Kv4.3 and Kv1.4 currents and is incorporated into an existing canine ventricular myocyte model. Simulations demonstrate strong coupling between L-type Ca(2+) current and I:(Kv4.3) and predict a bimodal relationship between I:(Kv4.3) density and APD whereby perturbations in I:(Kv4.3) density may produce either prolongation or shortening of APD, depending on baseline I:(to1) current level. See each paper for more and details.
416.  L5 cortical neurons with recreated synaptic inputs in vitro correlation transfer (Linaro et al 2019)
"...We studied pyramidal neurons and two classes of GABAergic interneurons of layer 5 in neocortical brain slices obtained from rats of both sexes, and we stimulated them with biophysically realistic correlated inputs, generated using dynamic clamp. We found that the physiological differences between cell types manifested unique features in their capacity to transfer correlated inputs. We used linear response theory and computational modeling to gain clear insights into how cellular properties determine both the gain and timescale of correlation transfer, thus tying single-cell features with network interactions. Our results provide further ground for the functionally distinct roles played by various types of neuronal cells in the cortical microcircuit..."
417.  L5 PFC pyramidal neurons (Papoutsi et al. 2017)
" ... Here, we use a modeling approach to investigate whether and how the morphology of the basal tree mediates the functional output of neurons. We implemented 57 basal tree morphologies of layer 5 prefrontal pyramidal neurons of the rat and identified morphological types which were characterized by different response features, forming distinct functional types. These types were robust to a wide range of manipulations (distribution of active ionic mechanisms, NMDA conductance, somatic and apical tree morphology or the number of activated synapses) and supported different temporal coding schemes at both the single neuron and the microcircuit level. We predict that the basal tree morphological diversity among neurons of the same class mediates their segregation into distinct functional pathways. ..."
418.  L5 pyr. cell spiking control by oscillatory inhibition in distal apical dendrites (Li et al 2013)
This model examined how distal oscillatory inhibition influences the firing of a biophysically-detailed layer 5 pyramidal neuron model.
419.  L5b PC model constrained for BAC firing and perisomatic current step firing (Hay et al., 2011)
"... L5b pyramidal cells have been the subject of extensive experimental and modeling studies, yet conductance-based models of these cells that faithfully reproduce both their perisomatic Na+-spiking behavior as well as key dendritic active properties, including Ca2+ spikes and back-propagating action potentials, are still lacking. Based on a large body of experimental recordings from both the soma and dendrites of L5b pyramidal cells in adult rats, we characterized key features of the somatic and dendritic firing and quantified their statistics. We used these features to constrain the density of a set of ion channels over the soma and dendritic surface via multi-objective optimization with an evolutionary algorithm, thus generating a set of detailed conductance-based models that faithfully replicate the back-propagating action potential activated Ca2+ spike firing and the perisomatic firing response to current steps, as well as the experimental variability of the properties. ... The models we present provide several experimentally-testable predictions and can serve as a powerful tool for theoretical investigations of the contribution of single-cell dynamics to network activity and its computational capabilities. "
420.  Lamprey spinal CPG neuron (Huss et al. 2007)
This is a model of a generic locomotor network neuron in the lamprey spinal cord. The given version is assumed to correspond to an interneuron; motoneurons can also be modelled by changing the dendritic tree morphology.
421.  Lateral dendrodenditic inhibition in the Olfactory Bulb (David et al. 2008)
Mitral cells, the principal output neurons of the olfactory bulb, receive direct synaptic activation from primary sensory neurons. Shunting inhibitory inputs delivered by granule cell interneurons onto mitral cell lateral dendrites are believed to influence spike timing and underlie coordinated field potential oscillations. Lateral dendritic shunt conductances delayed spiking to a degree dependent on both their electrotonic distance and phase of onset. Recurrent inhibition significantly narrowed the distribution of mitral cell spike times, illustrating a tendency towards coordinated synchronous activity. This result suggests an essential role for early mechanisms of temporal coordination in olfaction. The model was adapted from Davison et al, 2003, but include additional noise mechanisms, long lateral dendrite, and specific synaptic point processes.
422.  Layer 5 Pyramidal Neuron (Shai et al., 2015)
This work contains a NEURON model for a layer 5 pyramidal neuron (based on Hay et al., 2011) with distributed groups of synapses across the basal and tuft dendrites. The results of that simulation are used to fit a phenomenological model, which is also included in this file.
423.  Layer V PFC pyramidal neuron used to study persistent activity (Sidiropoulou & Poirazi 2012)
"... Here, we use a compartmental modeling approach to search for discriminatory features in the properties of incoming stimuli to a PFC pyramidal neuron and/or its response that signal which of these stimuli will result in persistent activity emergence. Furthermore, we use our modeling approach to study cell-type specific differences in persistent activity properties, via implementing a regular spiking (RS) and an intrinsic bursting (IB) model neuron. ... Collectively, our results pinpoint to specific features of the neuronal response to a given stimulus that code for its ability to induce persistent activity and predict differential roles of RS and IB neurons in persistent activity expression. "
424.  Layer V pyramidal cell functions and schizophrenia genetics (Mäki-Marttunen et al 2019)
Study on how GWAS-identified risk genes of shizophrenia affect excitability and integration of inputs in thick-tufted layer V pyramidal cells
425.  Layer V pyramidal cell model with reduced morphology (Mäki-Marttunen et al 2018)
" ... In this work, we develop and apply an automated, stepwise method for fitting a neuron model to data with fine spatial resolution, such as that achievable with voltage sensitive dyes (VSDs) and Ca2+ imaging. ... We apply our method to simulated data from layer 5 pyramidal cells (L5PCs) and construct a model with reduced neuronal morphology. We connect the reduced-morphology neurons into a network and validate against simulated data from a high-resolution L5PC network model. ..."
426.  LCN-HippoModel: model of CA1 PCs deep-superficial theta firing dynamics (Navas-Olive et al 2020)
Using a biophysically realistic model of CA1 pyramidal cells together with a combination of single-cell and multisite electrophysiological recordings, we have studied factors underlying the internal theta phase preference of identified cell types from the dorsal CA1. We found that perisomatic inhibition delivered by complementary populations of basket cells interacts with input pathways to shape phase-locked specificity of deep and superficial CA1 pyramidal cells. Somatodendritic integration of fluctuating glutamatergic inputs defined cycle-by-cycle by nested waveforms demonstrated that firing selection is tuneable across sublayers under the relevant influence of intrinsic factors. Our data identify a set of testable physiological mechanisms underlying a phase specific firing reservoir that can be repurposed for high-level flexible dynamical representations. Documentation in More info:
427.  Leaky integrate-and-fire model of spike frequency adaptation in the LGMD (Gabbiani and Krapp 2006)
This will reproduce Figure 9 of Gabbiani and Krapp (2006) J Neurophysiol 96:2951-2962. The figure simply shows that a leaky-integrate-and-fire model cannot reproduce spike frequency adaptation as it is seen experimentally in the LGMD neuron.
428.  Learning intrinsic excitability in Medium Spiny Neurons (Scheler 2014)
"We present an unsupervised, local activation-dependent learning rule for intrinsic plasticity (IP) which affects the composition of ion channel conductances for single neurons in a use-dependent way. We use a single-compartment conductance-based model for medium spiny striatal neurons in order to show the effects of parameterization of individual ion channels on the neuronal membrane potential-curent relationship (activation function). We show that parameter changes within the physiological ranges are sufficient to create an ensemble of neurons with significantly different activation functions. ... "
429.  Leech Heart (HE) Motor Neuron conductances contributions to NN activity (Lamb & Calabrese 2013)
"... To explore the relationship between conductances, and in particular how they influence the activity of motor neurons in the well characterized leech heartbeat system, we developed a new multi-compartmental Hodgkin-Huxley style leech heart motor neuron model. To do so, we evolved a population of model instances, which differed in the density of specific conductances, capable of achieving specific output activity targets given an associated input pattern. ... We found that the strengths of many conductances, including those with differing dynamics, had strong partial correlations and that these relationships appeared to be linked by their influence on heart motor neuron activity. Conductances that had positive correlations opposed one another and had the opposite effects on activity metrics when perturbed whereas conductances that had negative correlations could compensate for one another and had similar effects on activity metrics. "
430.  Leech Mechanosensory Neurons: Synaptic Facilitation by Reflected APs (Baccus 1998)
This model by Stephen Baccus explores the phenomena of action potential (AP) propagation at branch boints in axons. APs are sometimes transmitted down the efferent processes and sometimes are reflected back to the axon of AP origin or neither. See the paper for details. The model zip file contains a readme.txt which list introductory steps to follow to run the simulation. Stephen Baccus's email address:
431.  LGMD impedance (Dewell & Gabbiani 2019)
"How neurons filter and integrate their complex patterns of synaptic inputs is central to their role in neural information processing . Synaptic filtering and integration are shaped by the frequency-dependent neuronal membrane impedance. Using single and dual dendritic recordings in vivo, pharmacology, and computational modeling, we characterized the membrane impedance of a collision detection neuron in the grasshopper, Schistocerca americana. This neuron, the lobula giant movement detector (LGMD), exhibits consistent impedance properties across frequencies and membrane potentials. Two common active conductances gH and gM, mediated respectively by hyperpolarization-activated cyclic nucleotide gated (HCN) channels and by muscarine sensitive M-type K+ channels, promote broadband integration with high temporal precision over the LGMD's natural range of membrane potentials and synaptic input frequencies. Additionally, we found that a model based on the LGMD's branching morphology increased the gain and decreased the delay associated with the mapping of synaptic input currents to membrane potential. More generally, this was true for a wide range of model neuron morphologies, including those of neocortical pyramidal neurons and cerebellar Purkinje cells. These findings show the unexpected role played by two widespread active conductances and by dendritic morphology in shaping synaptic integration."
432.  LGMD Variability and logarithmic compression in dendrites (Jones and Gabbiani, 2012, 2012B)
A compartmental model of the LGMD with a simplified, rake shaped, excitatory dendrite. It receives spontaneous input and excitatory and inhibitory synaptic inputs triggered by visual stimuli. It generates realistic responses to looming through the velocity dependent scaling and delay of individual excitatory synaptic inputs, with variability. We use the model to show that the key determinants of output variability are spontaneous input and temporal jitter of the excitatory inputs, rather than variability in magnitude of individual inputs (2012B, J Neurophysiol). We also use the model to analyze the transformation of the excitatory signals through the visual pathway; concluding that the representation of stimulus velocity is transformed from an expansive relationship at the level of the LGMD inputs to a logarithmic one at the level of its membrane potential (2012, J Neurosci).
433.  LGMD with 3D morphology and active dendrites (Dewell & Gabbiani 2018)
This is a model of the locust LGMD looming sensitive neuron from Dewell & Gabbiani 2018. The morphology was constructed based on 2-photon imaging, and active conductances throughout the neuron were based on sharp electrode recordings in vivo.
434.  Library of biophysically detailed striatal projection neurons (Lindroos and Hellgren Kotaleski 2020)
Library of compartmentalized models used to investigate dendritic integration in striatal projection neurons under neuromodulation.
435.  Linear vs non-linear integration in CA1 oblique dendrites (Gómez González et al. 2011)
The hippocampus in well known for its role in learning and memory processes. The CA1 region is the output of the hippocampal formation and pyramidal neurons in this region are the elementary units responsible for the processing and transfer of information to the cortex. Using this detailed single neuron model, it is investigated the conditions under which individual CA1 pyramidal neurons process incoming information in a complex (non-linear) as opposed to a passive (linear) manner. This detailed compartmental model of a CA1 pyramidal neuron is based on one described previously (Poirazi, 2003). The model was adapted to five different reconstructed morphologies for this study, and slightly modified to fit the experimental data of (Losonczy, 2006), and to incorporate evidence in pyramidal neurons for the non-saturation of NMDA receptor-mediated conductances by single glutamate pulses. We first replicate the main findings of (Losonczy, 2006), including the very brief window for nonlinear integration using single-pulse stimuli. We then show that double-pulse stimuli increase a CA1 pyramidal neuron’s tolerance for input asynchrony by at last an order of magnitude. Therefore, it is shown using this model, that the time window for nonlinear integration is extended by more than an order of magnitude when inputs are short bursts as opposed to single spikes.
436.  Locational influence of dendritic PIC on input-output properties of spinal motoneurons (Kim 2017)
How does the dendritic location of calcium persistent inward current (Ca-PIC) influence dendritic excitability and firing behavior across the spinal motoneuron pool? This issue was investigated developing a model motoneuron pool where model parameters were analytically determined to reflect key motoneuron type-specific properties experimentally identified. The simulation results point out the negative relationship between the distance of Ca-PIC source from the soma and cell recruitment threshold as a basis underlying the systematic variation in input-output properties of motoneurons over the motoneuron pool.
437.  Locust olfactory network with GGN and full KC population in the mushroom body (Ray et al 2020)
We reconstructed the GGN morphology from 3D confocal image stack, and built a passive model based on the morphology to study signal attenuation across this giant neuron. In order to study the effect of feedback inhibition from this cell on odor information processing, we created a model of the olfactory network in the locust mushroom body with 50,000 KCs reciprocally connected to this neuron. Finally, we added a model of the IG to reproduce in vivo odor responses in GGN.
438.  Low dose of dopamine may stimulate prolactin secretion by increasing K currents (Tabak et al. 2006)
".. We considered the fast K+ currents flowing through large-conductance BK channels and through A-type channels. We developed a minimal lactotroph model to investigate the effects of these two currents. Both IBK and IA could transform the electrical pattern of activity from spiking to bursting, but through distinct mechanisms. IBK always increased the intracellular Ca2+ concentration, while IA could either increase or decrease it. Thus, the stimulatory effects of DA could be mediated by a fast K+ conductance which converts tonically spiking cells to bursters. In addition, the study illustrates that a heterogeneous distribution of fast K+ conductances could cause heterogeneous lactotroph firing patterns."
439.  Low Threshold Calcium Currents in TC cells (Destexhe et al 1998)
In Destexhe, Neubig, Ulrich, and Huguenard (1998) experiments and models examine low threshold calcium current's (IT, or T-current) distribution in thalamocortical (TC) cells. Multicompartmental modeling supports the hypothesis that IT currents have a density at least several fold higher in the dendrites than the soma. The IT current contributes significantly to rebound bursts and is thought to have important network behavior consequences. See the paper for details. See also Correspondance may be addressed to Alain Destexhe:
440.  Low Threshold Calcium Currents in TC cells (Destexhe et al 1998) (Brian)
R Brette's implementation in Brian 2 of Destexhe et al 1998's model. The author's original code is also available from ModelDB with accession number 279 (yes, was one of the first models in ModelDB)!
441.  Mammalian Ventricular Cell (Beeler and Reuter 1977)
This classic model of ventricular myocardial fibres was implemented by Francois Gannier. "... Four individual components of ionic current were formulated mathematically in terms of Hodgkin-Huxley type equations. The model incorporates two voltage- and time-dependent inward currents, the excitatory inward sodium current, illa, and a secondary or slow inward current, is, primarily carried by calcium ions. A time-independent outward potassium current, iK1, exhibiting inward-going rectification, and a voltage- and time-dependent outward current, i.1, primarily carried by potassium ions, are further elements of the model...."
442.  Mapping function onto neuronal morphology (Stiefel and Sejnowski 2007)
"... We used an optimization procedure to find neuronal morphological structures for two computational tasks: First, neuronal morphologies were selected for linearly summing excitatory synaptic potentials (EPSPs); second, structures were selected that distinguished the temporal order of EPSPs. The solutions resembled the morphology of real neurons. In particular the neurons optimized for linear summation electrotonically separated their synapses, as found in avian nucleus laminaris neurons, and neurons optimized for spike-order detection had primary dendrites of significantly different diameter, as found in the basal and apical dendrites of cortical pyramidal neurons. ..."
443.  Markov Chain-based Stochastic Shielding Hodgkin Huxley Model (Schmandt, Galan 2012)
444.  Mathematical model for windup (Aguiar et al. 2010)
"Windup is characterized as a frequency-dependent increase in the number of evoked action potentials in dorsal horn neurons in response to electrical stimulation of afferent C-fibers. ... The approach presented here relies on mathematical and computational analysis to study the mechanism(s) underlying windup. From experimentally obtained windup profiles, we extract the time scale of the facilitation mechanisms that may support the characteristics of windup. Guided by these values and using simulations of a biologically realistic compartmental model of a wide dynamic range (WDR) neuron, we are able to assess the contribution of each mechanism for the generation of action potentials windup. ..."
445.  Mature and young adult-born dentate granule cell models (T2N interface) (Beining et al. 2017)
... Here, we present T2N, a powerful interface to control NEURON with Matlab and TREES toolbox, which supports generating models stable over a broad range of reconstructed and synthetic morphologies. We illustrate this for a novel, highly-detailed active model of dentate granule cells (GCs) replicating a wide palette of experiments from various labs. By implementing known differences in ion channel composition and morphology, our model reproduces data from mouse or rat, mature or adult-born GCs as well as pharmacological interventions and epileptic conditions. ... T2N is suitable for creating robust models useful for large-scale networks that could lead to novel predictions. ..." See modeldb accession number 231818 for NEURON only code.
446.  MCCAIS model (multicompartmental cooperative AIS) (Öz et al 2015)
Action potential initiation in a multi-compartmental model with cooperatively gating Na channels in the axon initial segment.
447.  MEC layer II stellate cell: Synaptic mechanisms of grid cells (Schmidt-Hieber & Hausser 2013)
This study investigates the cellular mechanisms of grid field generation in Medial Entorhinal Cortex (MEC) layer II stellate cells.
448.  Mechanisms of extraneuronal space shrinkage (Ostby et al 2009)
"Neuronal stimulation causes ~30% shrinkage of the extracellular space (ECS) between neurons and surrounding astrocytes in grey and white matter under experimental conditions. Despite its possible implications for a proper understanding of basic aspects of potassium clearance and astrocyte function, the phenomenon remains unexplained. Here we present a dynamic model that accounts for current experimental data related to the shrinkage phenomenon in wild-type as well as in gene knockout individuals. ... Considering the current state of knowledge, the model framework appears sufficiently detailed and constrained to guide future key experiments and pave the way for more comprehensive astroglia–neuron interaction models for normal as well as pathophysiological situations. "
449.  Mechanisms of fast rhythmic bursting in a layer 2/3 cortical neuron (Traub et al 2003)
This simulation is based on the reference paper listed below. This port was made by Roger D Traub and Maciej T Lazarewicz (mlazarew at Thanks to Ashlen P Reid for help with porting a morphology of the cell.
450.  Mechanisms of magnetic stimulation of central nervous system neurons (Pashut et al. 2011)
Transcranial magnetic stimulation (TMS) is a widely applied tool for probing cognitive function in humans and is one of the best tools for clinical treatments and interfering with cognitive tasks. Surprisingly, while TMS has been commercially available for decades, the cellular mechanisms underlying magnetic stimulation remain unclear. Here we investigate these mechanisms using compartmental modeling. We generated a numerical scheme allowing simulation of the physiological response to magnetic stimulation of neurons with arbitrary morphologies and active properties. Computational experiments using this scheme suggested that TMS affects neurons in the central nervous system (CNS) primarily by somatic stimulation.
451.  Mechanisms of very fast oscillations in axon networks coupled by gap junctions (Munro, Borgers 2010)
Axons connected by gap junctions can produce very fast oscillations (VFOs, > 80 Hz) when stimulated randomly at a low rate. The models here explore the mechanisms of VFOs that can be seen in an axonal plexus, (Munro & Borgers, 2009): a large network model of an axonal plexus, small network models of axons connected by gap junctions, and an implementation of the model underlying figure 12 in Traub et al. (1999) . The large network model consists of 3,072 5-compartment axons connected in a random network. The 5-compartment axons are the 5 axonal compartments from the CA3 pyramidal cell model in Traub et al. (1994) with a fixed somatic voltage. The random network has the same parameters as the random network in Traub et al. (1999), and axons are stimulated randomly via a Poisson process with a rate of 2/s/axon. The small network models simulate waves propagating through small networks of axons connected by gap junctions to study how local connectivity affects the refractory period.
452.  Mechanisms underlying subunit independence in pyramidal neuron dendrites (Behabadi and Mel 2014)
"...Using a detailed compartmental model of a layer 5 pyramidal neuron, and an improved method for quantifying subunit independence that incorporates a more accurate model of dendritic integration, we first established that the output of each dendrite can be almost perfectly predicted by the intensity and spatial configuration of its own synaptic inputs, and is nearly invariant to the rate of bAP-mediated 'cross-talk' from other dendrites over a 100-fold range..."
453.  Medial vestibular neuron models (Quadroni and Knopfel 1994)
The structure and the parameters of the model cells were chosen to reproduce the responses of type A and type B MVNns as described in electrophysiological recordings. The emergence of oscillatory firing under these two specific experimental conditions is consistent with electrophysiological recordings not used during construction of the model. We, therefore, suggest that these models have a high predictive value.
454.  Membrane potential changes in dendritic spines during APs and synaptic input (Palmer & Stuart 2009)
" ... Finally, we used simulations of our experimental observations in morphologically realistic models to estimate spine neck resistance. These simulations indicated that spine neck resistance ranges up to ~500 M Ohm. Spine neck resistances of this magnitude reduce somatic EPSPs by ~15%, indicating that the spine neck is unlikely to act as a physical device to significantly modify synaptic strength."
455.  Method for deriving general HH neuron model`s spiking input-output relation (Soudry & Meir 2014)
We derived in paper a method to find semi-analytic input-output relations for general HH-like neuron models (firing rates, spectra, linear filters)under sparse spike stimulation. Here we demonstrate the applicability of this method to various HH-type models (HH with slow sodium inactivation, with slow pottasium inactivation, with synaptic STD and other various extensions).
456.  Mice Somatosensory L2/3 Pyramidal cells (Iascone et al 2020)
Mice L2/3 pyramidal cells with full excitatory and inhibitory synaptic maps (Models used in Whole-neuron synaptic mapping reveals local balance between excitatory and inhibitory synapse organization - Iascone et at 2020)
457.  Midbrain dopamine neuron: firing patterns (Canavier 1999)
Sodium dynamics drives the generation of slow oscillations postulated to underly NMDA-evoked bursting activity.
458.  Midbrain torus semicircularis neuron model (Aumentado-Armstrong et al. 2015)
This paper investigates how midbrain electrosensory neurons give invariant responses to natural communication stimuli. A model explains that such invariance can be achieved by combining afferent input from ON and OFF cells.
459.  Minimal cell model (Av-Ron et al 1991)
The minimal cell model (MCM) is a reduced Hodgkin-Huxley model that can exhibit excitable and oscillatory behavior. It consists of two ordinary differential equations, dV/dt for membrane voltage and dW/dt for potassium activation and sodium inactivation. The MCM has a stable membrane potential of -60mV. With constant input current of 10uA/cm2, it exhibits oscillations of 150Hz. It is based on the work by FitzHugh and Rinzel.
460.  Mirror Neuron (Antunes et al 2017)
Modeling Mirror Neurons Through Spike-Timing Dependent Plasticity. This script reproduces Figure 3B.
461.  Mitral cell activity gating by respiration and inhibition in an olfactory bulb NN (Short et al 2016)
To explore interactions between respiration, inhibition, and olfaction, experiments using light to active channel rhodopsin in sensory neurons expressing Olfactory Marker Protein were performed in mice and modeled in silico. This archive contains NEURON models that were run on parallel computers to explore the interactions between varying strengths of respiratory activity and olfactory sensory neuron input and the roles of periglomerular, granule, and external tufted cells in shaping mitral cell responses.
462.  Mixed mode oscillations as a mechanism for pseudo-plateau bursting (Vo et al. 2010)
"We combine bifurcation analysis with the theory of canard-induced mixed mode oscillations to investigate the dynamics of a novel form of bursting. This bursting oscillation, which arises from a model of the electrical activity of a pituitary cell, is characterized by small impulses or spikes riding on top of an elevated voltage plateau. ..."
463.  MNTB Neuron: Kv3.1 currents (Wang et al 1998)
Model of Medial Nucleus of the Trapezoid Body (MNTB) neurons described in Lu-Yang Wang, Li Gan, Ian D. Forsythe and Leonard K. Kaczmarek. Contribution of the Kv3.1 potassium channel to high-frequency firing in mouse auditory neurones. J. Physiol (1998) 509.1 183-194. Created by David Kornfeld, Byram Hills High School, Armonk NY. Please email for questions about the model. See Readme.txt below for more info.
464.  Model for concentration invariant odor coding based on primacy hypothesis (Wilson et al 2017)
"... Here we propose that, in olfaction, a small and relatively stable set comprised of the earliest activated receptors forms a code for concentration-invariant odor identity. One prediction of this “primacy coding” scheme is that decisions based on odor identity can be made solely using early odor-evoked neural activity. Using an optogenetic masking paradigm, we define the sensory integration time necessary for odor identification and demonstrate that animals can use information occurring <100ms after inhalation onset to identify odors. ... We propose a computational model demonstrating how such a code can be read by neural circuits of the olfactory system."
465.  Model for K-ATP mediated bursting in mSNc DA neurons (Knowlton et al 2018)
"Burst firing in medial substantia nigra dopamine (mSN DA) neurons has been selectively linked to novelty-induced exploration behavior in mice. Burst firing in mSN DA neurons, in contrast to lateral SN DA neurons, requires functional ATP-sensitive potassium channels (K-ATP) both in vitro and in vivo. However, the precise role of K-ATP channels in promoting burst firing is un-known. We show experimentally that L-type calcium channel activity in mSN DA neurons en-hances open probability of K-ATP channels. We then generated a mathematical model to study the role of Ca2+ dynamics driving K-ATP channel function in mSN DA neurons during bursting. ..."
466.  Model of AngII signaling and membrane electrophysiology (Makadia, Anderson, Fey et al., 2015)
We developed a novel multiscale model to bridge neuropeptide receptor-activated signaling pathway with membrane electrophysiology. The model studies the effects of Angiotensin II (AngII) on neuronal excitability changes mediated by signaling dynamics and downstream phosphorylation of ion channels. The multiscale model was implemented as a set of ordinary differential equations solved using the ode15s solver in Matlab (Mathworks, USA). The signaling reactions were modeled with either mass-action or Michaelis--Menten kinetics and ion channel electrophysiology was modeled according to the Hodgkin-Huxley formalism. These models were initially validated against their respective data domains independently and were integrated to develop a multiscale model of signaling and electrophysiology.
467.  Model of arrhythmias in a cardiac cells network (Casaleggio et al. 2014)
" ... Here we explore the possible processes leading to the occasional onset and termination of the (usually) non-fatal arrhythmias widely observed in the heart. Using a computational model of a two-dimensional network of cardiac cells, we tested the hypothesis that an ischemia alters the properties of the gap junctions inside the ischemic area. ... In conclusion, our model strongly supports the hypothesis that non-fatal arrhythmias can develop from post-ischemic alteration of the electrical connectivity in a relatively small area of the cardiac cell network, and suggests experimentally testable predictions on their possible treatments."
468.  Model of repetitive firing in Grueneberg ganglion olfactory neurons (Liu et al., 2012)
This model is constructed based on properties of Na+ and K+ currents observed in whole-cell patch clamp recordings of mouse Grueneberg ganglion neurons in acute slices. Two distinct Na+ conductances representing the TTX-sensitive and TTX-resistant currents and one delayed rectifier K+ currrent are included. By modulating the maximal conductances of Na+ currents, one can reproduce the regular, phasic, and sporadic patterns of repetitive firing found in the patch clamp experiments.
469.  Model of SK current`s influence on precision in Globus Pallidus Neurons (Deister et al. 2009)
" ... In numerical simulations, the availability of both Na+ and A-type K+ channels during autonomous firing were reduced when SK channels were removed, and a nearly equal reduction in Na+ and K+ subthreshold-activated ion channel availability produced a large decrease in the neuron's slope conductance near threshold. This change made the neuron more sensitive to intrinsically generated noise. In vivo, this change would also enhance the sensitivity of GP (Globus Pallidus) neurons to small synaptic inputs."
470.  Model of Type 3 firing in neurons (Clay et al 2008)
An ionic model for Type 3 firing in neurons (Clay et al 2008) Some neurons fire only once in response to a sustained depolarizing current pulse, type 3 behavior. One example, surprisingly, is the squid giant axon. The Hodgkin-Huxley (HH) model of this preparation fires repetitively for these conditions – type 2, a result that is not observed experimentally as shown in the above paper. Changing one parameter of their model of IK is sufficient to mimic the result.
471.  Modeling conductivity profiles in the deep neocortical pyramidal neuron (Wang K et al. 2013)
"With the rapid increase in the number of technologies aimed at observing electric activity inside the brain, scientists have felt the urge to create proper links between intracellular- and extracellular-based experimental approaches. Biophysical models at both physical scales have been formalized under assumptions that impede the creation of such links. In this work, we address this issue by proposing amulticompartment model that allows the introduction of complex extracellular and intracellular resistivity profiles. This model accounts for the geometrical and electrotonic properties of any type of neuron through the combination of four devices: the integrator, the propagator, the 3D connector, and the collector. ..."
472.  Modeling dendritic spikes and plasticity (Bono and Clopath 2017)
Biophysical model and reduced neuron model with voltage-dependent plasticity.
473.  Modeling dentate granule cells heterosynaptic plasticity using STDP-BCM rule (Jedlicka et al. 2015)
... Here we study how key components of learning mechanisms in the brain, namely spike timing-dependent plasticity and metaplasticity, interact with spontaneous activity in the input pathways of the neuron. Using biologically realistic simulations we show that ongoing background activity is a key determinant of the degree of long-term potentiation and long-term depression of synaptic transmission between nerve cells in the hippocampus of freely moving animals. This work helps better understand the computational rules which drive synaptic plasticity in vivo. ...
474.  Modeling interactions in Aplysia neuron R15 (Yu et al 2004)
"The biophysical properties of neuron R15 in Aplysia endow it with the ability to express multiple modes of oscillatory electrical activity, such as beating and bursting. Previous modeling studies examined the ways in which membrane conductances contribute to the electrical activity of R15 and the ways in which extrinsic modulatory inputs alter the membrane conductances by biochemical cascades and influence the electrical activity. The goals of the present study were to examine the ways in which electrical activity influences the biochemical cascades and what dynamical properties emerge from the ongoing interactions between electrical activity and these cascades." See paper for more and details.
475.  Modeling temperature changes in AMPAR kinetics (Postlethwaite et al 2007)
This model was used to simulate glutamatergic, AMPA receptor mediated mEPSCs (miniature EPSCs, resulting from spontaneous vesicular transmitter release) at the calyx of Held synapse. It was used to assess the influence of temperature (physiological vs. subphysiological) on the amplitude and time course of mEPSCs. In the related paper, simulation results were directly compared to the experimental data, and it was concluded that an increase of temperature accelerates AMPA receptor kinetics.
476.  Modelling large scale electrodiffusion near morphologically detailed neurons (Solbra et al 2018)
" ... Here, we present the 3-D Kirchhoff-Nernst-Planck (KNP) framework, tailored to explore electrodiffusive effects on large spatiotemporal scales. By assuming electroneutrality, the KNP-framework circumvents charge-relaxation processes on the spatiotemporal scales of nanometers and nanoseconds, and makes it feasible to run simulations on the spatiotemporal scales of millimeters and seconds on a standard desktop computer. In the present work, we use the 3-D KNP framework to simulate the dynamics of ion concentrations and the electrical potential surrounding a morphologically detailed pyramidal cell. ..."
477.  Modelling platform of the cochlear nucleus and other auditory circuits (Manis & Compagnola 2018)
"Models of the auditory brainstem have been an invaluable tool for testing hypotheses about auditory information processing and for highlighting the most important gaps in the experimental literature. Due to the complexity of the auditory brainstem, and indeed most brain circuits, the dynamic behavior of the system may be difficult to predict without a detailed, biologically realistic computational model. Despite the sensitivity of models to their exact construction and parameters, most prior models of the cochlear nucleus have incorporated only a small subset of the known biological properties. This confounds the interpretation of modelling results and also limits the potential future uses of these models, which require a large effort to develop. To address these issues, we have developed a general purpose, bio-physically detailed model of the cochlear nucleus for use both in testing hypotheses about cochlear nucleus function and also as an input to models of downstream auditory nuclei. The model implements conductance-based Hodgkin-Huxley representations of cells using a Python-based interface to the NEURON simulator. ..."
478.  Modelling reduced excitability in aged CA1 neurons as a Ca-dependent process (Markaki et al. 2005)
"We use a multi-compartmental model of a CA1 pyramidal cell to study changes in hippocampal excitability that result from aging-induced alterations in calcium-dependent membrane mechanisms. The model incorporates N- and L-type calcium channels which are respectively coupled to fast and slow afterhyperpolarization potassium channels. Model parameters are calibrated using physiological data. Computer simulations reproduce the decreased excitability of aged CA1 cells, which results from increased internal calcium accumulation, subsequently larger postburst slow afterhyperpolarization, and enhanced spike frequency adaptation. We find that aging-induced alterations in CA1 excitability can be modelled with simple coupling mechanisms that selectively link specific types of calcium channels to specific calcium-dependent potassium channels."
479.  Models for cortical UP-DOWN states in a bistable inhibitory-stabilized network (Jercog et al 2017)
In the idling brain, neuronal circuits transition between periods of sustained firing (UP state) and quiescence (DOWN state), a pattern the mechanisms of which remain unclear. We analyzed spontaneous cortical population activity from anesthetized rats and found that UP and DOWN durations were highly variable and that population rates showed no significant decay during UP periods. We built a network rate model with excitatory (E) and inhibitory (I) populations exhibiting a novel bistable regime between a quiescent and an inhibition-stabilized state of arbitrarily low rate, where fluctuations triggered state transitions. In addition, we implemented these mechanisms in a more biophysically realistic spiking network, where DOWN-to-UP transitions are caused by synchronous high-amplitude events impinging onto the network.
480.  Models of Na channels from a paper on the PKC control of I Na,P (Baker 2005)
"The tetrodotoxin-resistant (TTX-r) persistent Na(+) current, attributed to Na(V)1.9, was recorded in small (< 25 mum apparent diameter) dorsal root ganglion (DRG) neurones cultured from P21 rats and from adult wild-type and Na(V)1.8 null mice. ... Numerical simulation of the up-regulation qualitatively reproduced changes in sensory neurone firing properties. ..." Note: models of NaV1.8 and NaV1.9 and also persistent and transient Na channels that collectively model Nav 1.1, 1.6, and 1.7 are present in this model.
481.  Modulation of septo-hippocampal theta activity by GABAA receptors (Hajos et al. 2004)
Theta frequency oscillation of the septo-hippocampal system has been considered as a prominent activity associated with cognitive function and affective processes. ... In the present experiments we applied a combination of computational and physiological techniques to explore the functional role of GABAA receptors in theta oscillation. ... In parallel to these experimental observations, a computational model has been constructed by implementing a septal GABA neuron model with a CA1 hippocampal model containing three types of neurons (including oriens and basket interneurons and pyramidal cells; latter modeled by multicompartmental techniques; for detailed model description with network parameters see online addendum: This connectivity made the network capable of simulating the responses of the septo-hippocampal circuitry to the modulation of GABAA transmission, and the presently described computational model proved suitable to reveal several aspects of pharmacological modulation of GABAA receptors. In addition, computational findings indicated different roles of distinctively located GABAA receptors in theta generation.
482.  Modulation of temporal integration window (Migliore, Shepherd 2002)
Model simulation file from the paper M.Migliore and Gordon M. Shepherd Emerging rules for distributions of active dendritic properties underlying specific neuronal functions. Nature Rev. Neurosci. 3, 362-370 (2002).
483.  Molecular layer interneurons in cerebellum encode valence in associative learning (Ma et al 2020)
We used two-photon microscopy to study the role of ensembles of cerebellar molecular layer interneurons (MLIs) in a go-no go task where mice obtain a sugar water reward. In order to begin understanding the circuit basis of our findings in changes in lick behavior with chemogenetics in the go-no go associative learning olfactory discrimination task we generated a simple computational model of MLI interaction with PCs.
484.  Morris-Lecar model of the barnacle giant muscle fiber (Morris, Lecar 1981)
... This paper presents an analysis of the possible modes of behavior available to a system of two noninactivating conductance mechanisms, and indicates a good correspondence to the types of behavior exhibited by barnacle fiber. The differential equations of a simple equivalent circuit for the fiber are dealt with by means of some of the mathematical techniques of nonlinear mechanics. General features of the system are (a) a propensity to produce damped or sustained oscillations over a rather broad parameter range, and (b) considerable latitude in the shape of the oscillatory potentials. It is concluded that for cells subject to changeable parameters (either from cell to cell or with time during cellular activity), a system dominated by two noninactivating conductances can exhibit varied oscillatory and bistable behavior. See paper for details.
485.  Motoneuron model of self-sustained firing after spinal cord injury (Kurian et al. 2011)
" ... During the acute-stage of spinal cord injury (SCI), the endogenous ability to generate plateaus is lost; however, during the chronic-stage of SCI, plateau potentials reappear with prolonged self-sustained firing that has been implicated in the development of spasticity. In this work, we extend previous modeling studies to systematically investigate the mechanisms underlying the generation of plateau potentials in motoneurons, including the influences of specific ionic currents, the morphological characteristics of the soma and dendrite, and the interactions between persistent inward currents and synaptic input. ..."
486.  Motoneuron pool input-output function (Powers & Heckman 2017)
"Although motoneurons have often been considered to be fairly linear transducers of synaptic input, recent evidence suggests that strong persistent inward currents (PICs) in motoneurons allow neuromodulatory and inhibitory synaptic inputs to induce large nonlinearities in the relation between the level of excitatory input and motor output. To try to estimate the possible extent of this nonlinearity, we developed a pool of model motoneurons designed to replicate the characteristics of motoneuron input-output properties measured in medial gastrocnemius motoneurons in the decerebrate cat with voltage- clamp and current-clamp techniques. We drove the model pool with a range of synaptic inputs consisting of various mixtures of excitation, inhibition, and neuromodulation. We then looked at the relation between excitatory drive and total pool output. Our results revealed that the PICs not only enhance gain but also induce a strong nonlinearity in the relation between the average firing rate of the motoneuron pool and the level of excitatory input. The relation between the total simulated force output and input was somewhat more linear because of higher force outputs in later-recruited units. ..."
487.  Multi-comp. CA1 O-LM interneuron model with varying dendritic Ih distributions (Sekulic et al 2015)
The model presented here was used to investigate possible dendritic distributions of the HCN channel-mediated current (Ih) in models of oriens-lacunosum/moleculare (O-LM) CA1 hippocampal interneurons. Physiological effects of varying the dendritic distributions consisted of examining back-propagating action potential speeds.
488.  Multi-timescale adaptive threshold model (Kobayashi et al 2009)
" ... In this study, we devised a simple, fast computational model that can be tailored to any cortical neuron not only for reproducing but also for predicting a variety of spike responses to greatly fluctuating currents. The key features of this model are a multi-timescale adaptive threshold predictor and a nonresetting leaky integrator. This model is capable of reproducing a rich variety of neuronal spike responses, including regular spiking, intrinsic bursting, fast spiking, and chattering, by adjusting only three adaptive threshold parameters. ..."
489.  Multi-timescale adaptive threshold model (Kobayashi et al 2009) (NEURON)
" ... In this study, we devised a simple, fast computational model that can be tailored to any cortical neuron not only for reproducing but also for predicting a variety of spike responses to greatly fluctuating currents. The key features of this model are a multi-timescale adaptive threshold predictor and a nonresetting leaky integrator. This model is capable of reproducing a rich variety of neuronal spike responses, including regular spiking, intrinsic bursting, fast spiking, and chattering, by adjusting only three adaptive threshold parameters. ..."
490.  Multicompartmental cerebellar granule cell model (Diwakar et al. 2009)
A detailed multicompartmental model was used to study neuronal electroresponsiveness of cerebellar granule cells in rats. Here we show that, in cerebellar granule cells, Na+ channels are enriched in the axon, especially in the hillock, but almost absent from soma and dendrites. Numerical simulations indicated that granule cells have a compact electrotonic structure allowing EPSPs to diffuse with little attenuation from dendrites to axon. The spike arose almost simultaneously along the whole axonal ascending branch and invaded the hillock, whose activation promoted spike back-propagation with marginal delay (<200 micros) and attenuation (<20 mV) into the somato-dendritic compartment. For details check the cited article.
491.  Multiple modes of a conditional neural oscillator (Epstein, Marder 1990)
We present a model for a conditional bursting neuron consisting of five conductances: Hodgkin-Huxley type time- and voltage-dependent Na+ and K+ conductances, a calcium activated voltage-dependent K+ conductance, a calcium-inhibited time- and voltage-dependent Ca++ conductance, and a leakage Cl- conductance. Different bursting and silent modes and transitions between them are analyzed in the model and compared to bursting modes in experiment. See the paper for details.
492.  Multiple modes of inner hair cell stimulation (Mountain, Cody 1999)
This model simulates the membrane potential of an inner hair cell for a sinusoidal stimulus to the hair bundle. It uses a 2-state Boltzmann model for the tension-gated conductance in the stereocilia and a linear model for the basolateral membrane. This model is based on the IHC model used in Mountain and Cody (1999).
493.  Multiplication by NMDA receptors in Direction Selective Ganglion cells (Poleg-Polsky & Diamond 2016)
The model demonstrates how signal amplification with NMDARs depends on the synaptic environment. When direction selectivity (DS) detection is mediated by DS inhibition, NMDARs multiply other synaptic conductances. In the case of DS tuned excitation, NMDARs contribute additively.
494.  Multiscale interactions between chemical and electric signaling in LTP (Bhalla 2011)
"Synaptic plasticity leads to long-term changes in excitability, whereas cellular homeostasis maintains excitability. Both these processes involve interactions between molecular events, electrical events, and network activity. Here I explore these intersections with a multilevel model that embeds molecular events following synaptic calcium influx into a multicompartmental electrical model of a CA1 hippocampal neuron. ..."
495.  Multiscale model of olfactory receptor neuron in mouse (Dougherty 2009)
Collection of XPP (.ode) files simulating the signal transduction (slow) and action potential (fast) currents in the olfactory receptor neuron of mouse. Collection contains model configured for dual odorant pulse delivery and model configured for prolonged odorant delivery. For those interested more in transduction processes, each whole cell recording model comes with a counter part file configured to show just the slow transduction current for ease of use and convenience. These transduction-only models typically run faster than the full multi-scale models but do not demonstrate action potentials.
496.  Multiscale simulation of the striatal medium spiny neuron (Mattioni & Le Novere 2013)
"… We present a new event-driven algorithm to synchronize different neuronal models, which decreases computational time and avoids superfluous synchronizations. The algorithm is implemented in the TimeScales framework. We demonstrate its use by simulating a new multiscale model of the Medium Spiny Neuron of the Neostriatum. The model comprises over a thousand dendritic spines, where the electrical model interacts with the respective instances of a biochemical model. Our results show that a multiscale model is able to exhibit changes of synaptic plasticity as a result of the interaction between electrical and biochemical signaling. …"
497.  MyFirstNEURON (Houweling, Sejnowski 1997)
MyFirstNEURON is a NEURON demo by Arthur Houweling and Terry Sejnowski. Perform experiments from the book 'Electrophysiology of the Neuron, A Companion to Shepherd's Neurobiology, An Interactive Tutorial' by John Huguenard & David McCormick, Oxford University Press 1997, or design your own one or two cell simulation.
498.  Na+ channel dependence of AP initiation in cortical pyramidal neuron (Kole et al. 2008)
In this simulation action potential initiation, action potential properties and the role of axon initial segment Na+ channels are investigated in a realistic model of a layer 5 pyramidal neuron axon initial segment. The main Na+ channel properties were constrained by experimental data and the axon initial segment was reconstructed. Model parameters were constrained by direct recordings at the axon initial segment.
499.  Nav1.6 sodium channel model in globus pallidus neurons (Mercer et al. 2007)
Model files for the paper Mercer JN, Chan CS, Tkatch T, Held J, Surmeier DJ. Nav1.6 sodium channels are critical to pacemaking and fast spiking in globus pallidus neurons.,J Neurosci. 2007 Dec 5;27(49):13552-66.
500.  Neocort. pyramidal cells subthreshold somatic voltage controls spike propagation (Munro Kopell 2012)
There is suggestive evidence that pyramidal cell axons in neocortex may be coupled by gap junctions into an ``axonal plexus" capable of generating Very Fast Oscillations (VFOs) with frequencies exceeding 80 Hz. It is not obvious, however, how a pyramidal cell in such a network could control its output when action potentials are free to propagate from the axons of other pyramidal cells into its own axon. We address this problem by means of simulations based on 3D reconstructions of pyramidal cells from rat somatosensory cortex. We show that somatic depolarization enables propagation via gap junctions into the initial segment and main axon, while somatic hyperpolarization disables it. We show further that somatic voltage cannot effectively control action potential propagation through gap junctions on minor collaterals; action potentials may therefore propagate freely from such collaterals regardless of somatic voltage. In previous work, VFOs are all but abolished during the hyperpolarization phase of slow-oscillations induced by anesthesia in vivo. This finding constrains the density of gap junctions on collaterals in our model and suggests that axonal sprouting due to cortical lesions may result in abnormally high gap junction density on collaterals, leading in turn to excessive VFO activity and hence to epilepsy via kindling.
501.  Neocortical pyramidal neuron: deep; effects of dopamine (Durstewitz et al 2000)
"... Simulated dopamine strongly enhanced high, delay-type activity but not low, spontaneous activity in the model network. Furthermore the strength of an afferent stimulation needed to disrupt delay-type activity increased with the magnitude of the dopamine-induced shifts in network parameters, making the currently active representation much more stable. Stability could be increased by dopamine-induced enhancements of the persistent Na(+) and N-methyl-D-aspartate (NMDA) conductances. Stability also was enhanced by a reduction in AMPA conductances. The increase in GABA(A) conductances that occurs after stimulation of dopaminergic D1 receptors was necessary in this context to prevent uncontrolled, spontaneous switches into high-activity states (i.e., spontaneous activation of task-irrelevant representations). In conclusion, the dopamine-induced changes in the biophysical properties of intrinsic ionic and synaptic conductances conjointly acted to highly increase stability of activated representations in PFC networks and at the same time retain control over network behavior and thus preserve its ability to adequately respond to task-related stimuli. ..." See paper and references for more and details.
502.  NETMORPH: creates NNs with realistic neuron morphologies (Koene et al. 2009, van Ooyen et al. 2014)
NETMORPH is a simulation tool for building synaptically connected networks with realistic neuron morphologies. Axonal and dendritic morphologies are created by using stochastic rules for the behavior of individual growth cones, the structures at the tip of outgrowing axons and dendrites that mediate elongation and branching. Axons and dendrites are not guided by any extracellular cues. Synapses are formed when crossing axonal and dendritic segments come sufficiently close to each other. See the README in the archive for more information.
503.  Neural Query System NQS Data-Mining From Within the NEURON Simulator (Lytton 2006)
NQS is a databasing program with a query command modeled loosely on the SQL select command. Please see the manual NQS.pdf for details of use. An NQS database must be populated with data to be used. This package includes MFP (model fingerprint) which provides an example of NQS use with the model provided in the modeldb folder (see readme for usage).
504.  NeuroMatic: software for acquisition, analysis and simulation of e-phys data (Rothman & Silver 2018)
"Acquisition, analysis and simulation of electrophysiological properties of the nervous system require multiple software packages. This makes it difficult to conserve experimental metadata and track the analysis performed. It also complicates certain experimental approaches such as online analysis. To address this, we developed NeuroMatic, an open-source software toolkit that performs data acquisition (episodic, continuous and triggered recordings), data analysis (spike rasters, spontaneous event detection, curve fitting, stationarity) and simulations (stochastic synaptic transmission, synaptic short-term plasticity, integrate-and-fire and Hodgkin-Huxley-like single-compartment models). The merging of a wide range of tools into a single package facilitates a more integrated style of research, from the development of online analysis functions during data acquisition, to the simulation of synaptic conductance trains during dynamic-clamp experiments. Moreover, NeuroMatic has the advantage of working within Igor Pro, a platform-independent environment that includes an extensive library of built-in functions, a history window for reviewing the user's workflow and the ability to produce publication-quality graphics. ..."
505.  Neuromuscular network model of gut motility (Barth et al 2017)
Here we develop an integrated neuromechanical model of the ENS and assess neurostimulation strategies for enhancing gut motility. The model includes a network of enteric neurons, smooth muscle fibers, and interstitial cells of Cajal, which regulate propulsion of a virtual pellet in a model of gut motility.
506.  Neuronal morphology goes digital ... (Parekh & Ascoli 2013)
An illustration of a NEURON model and why reconstructing morphologies is useful in this regard (i.e. investigating spatial/temporal aspect of how different currents and voltage propagate in dendrites).
507.  Neurophysiological impact of inactivation pathways in A-type K+ channels (Fineberg et al 2012)
These models predict the differential effects of varying pathways of inactivation (closed state inactivation, CSI, or open state inactivation, OSI). Specifically, Markov models of Kv4 potassium channels with CSI or CSI+OSI were inserted into the CA1 pyramidal neuron model from Migliore et al (1999; ModelDB accession #2796) to determine the neurophysiological impact of inactivation pathways. Furthermore, Markov models of Kv4.2 and Kv3.4 channels are used to illustrate a method by which to test what pathway of inactivation a channel uses.
508.  Nigral dopaminergic neurons: effects of ethanol on Ih (Migliore et al. 2008)
We use a realistic computational model of dopaminergic neurons in vivo to suggest that ethanol, through its effects on Ih, modifies the temporal structure of the spiking activity. The model predicts that the dopamine level may increase much more during bursting than pacemaking activity, especially in those brain regions with a slow dopamine clearance rate. The results suggest that a selective pharmacological remedy could thus be devised against the rewarding effects of ethanol that are postulated to mediate alcohol abuse and addiction, targeting the specific HCN genes expressed in dopaminergic neurons.
509.  NMDA spikes in basal dendrites of L5 pyramidal neurons (Polsky et al. 2009)
"... In apical dendrites of neocortical pyramidal neurons, calcium spikes are known to contribute to burst generation, but a comparable understanding of basal dendritic mechanisms is lacking. Here we show that NMDA spikes in basal dendrites mediate both detection and generation of bursts through a postsynaptic mechanism. High-frequency inputs to basal dendrites markedly facilitated NMDA spike initiation compared with low-frequency activation or single inputs. ..."
510.  NMDA subunit effects on Calcium and STDP (Evans et al. 2012)
Effect of NMDA subunit on spike timing dependent plasticity.
511.  NN activity impact on neocortical pyr. neurons integrative properties in vivo (Destexhe & Pare 1999)
"During wakefulness, neocortical neurons are subjected to an intense synaptic bombardment. To assess the consequences of this background activity for the integrative properties of pyramidal neurons, we constrained biophysical models with in vivo intracellular data obtained in anesthetized cats during periods of intense network activity similar to that observed in the waking state. In pyramidal cells of the parietal cortex (area 5–7), synaptic activity was responsible for an approximately fivefold decrease in input resistance (Rin), a more depolarized membrane potential (Vm), and a marked increase in the amplitude of Vm fluctuations, as determined by comparing the same cells before and after microperfusion of tetrodotoxin (TTX). ..."
512.  Nodose sensory neuron (Schild et al. 1994, Schild and Kunze 1997)
This is a simulink implementation of the model described in Schild et al. 1994, and Schild and Kunze 1997 papers on Nodose sensory neurons. These papers describe the sensitivity these models have to their parameters and the match of the models to experimental data.
513.  Non-Weak E-Fields Pyramidal Neurons (Reznik et. al.,2015)
Effect of Polarization Induced by Non-Weak Electric Fields on the Excitability of Elongated Neurons With Active Dendrite. In response to polarization, the active currents in the dendrites of pyramidal neurons play a pivotal role in the excitability of elongated neurons. Depending on a number of parameters either hyperpolarizing or depolarizing currents in the dendrite dominate as polarization is increased. Furthermore, the impact that these active dendrite channels (Ca, KAHP, etc) occur when only a small fraction of their channels are open.
514.  Nonlinear dendritic processing in barrel cortex spiny stellate neurons (Lavzin et al. 2012)
This is a multi-compartmental simulation of a spiny stellate neuron which is stimulated by a thalamocortical (TC) and cortico-cortical (CC) inputs. No other cells are explicitly modeled; the presynaptic network activation is represented by the number of active synapses. Preferred and non –preferred thalamic directions thus correspond to larder/smaller number of TC synapses. This simulation revealed that randomly activated synapses can cooperatively trigger global NMDA spikes, which involve participation of most of the dendritic tree. Surprisingly, we found that although the voltage profile of the cell was uniform, the calcium influx was restricted to ‘hot spots’ which correspond to synaptic clusters or large conductance synapses
515.  Nonlinear neuronal computation based on physiologically plausible inputs (McFarland et al. 2013)
"... Here we present an approach for modeling sensory processing, termed the Nonlinear Input Model (NIM), which is based on the hypothesis that the dominant nonlinearities imposed by physiological mechanisms arise from rectification of a neuron’s inputs. Incorporating such ‘upstream nonlinearities’ within the standard linear-nonlinear (LN) cascade modeling structure implicitly allows for the identification of multiple stimulus features driving a neuron’s response, which become directly interpretable as either excitatory or inhibitory. Because its form is analogous to an integrate-and-fire neuron receiving excitatory and inhibitory inputs, model fitting can be guided by prior knowledge about the inputs to a given neuron, and elements of the resulting model can often result in specific physiological predictions. Furthermore, by providing an explicit probabilistic model with a relatively simple nonlinear structure, its parameters can be efficiently optimized and appropriately regularized. ... ”
516.  Norns - Neural Network Studio (Visser & Van Gils 2014)
The Norns - Neural Network Studio is a software package for designing, simulation and analyzing networks of spiking neurons. It consists of three parts: 1. "Urd": a Matlab frontend with high-level functions for quickly defining networks 2. "Verdandi": an optimized C++ simulation environment which runs the simulation defined by Urd 3. "Skuld": an advanced Matlab graphical user interface (GUI) for visual inspection of simulated data.
517.  O-LM interneuron model (Lawrence et al. 2006)
Exploring the kinetics and distribution of the muscarinic potassium channel, IM, in 2 O-LM interneuron morphologies. Modulation of the ion channel by drugs such as XE991 (antagonist) and retigabine (agonist) are simulated in the models to examine the role of IM in spiking properties.
518.  Olfactory Computations in Mitral-Granule cell circuits (Migliore & McTavish 2013)
Model files for the entry "Olfactory Computations in Mitral-Granule Cell Circuits" of the Springer Encyclopedia of Computational Neuroscience by Michele Migliore and Tom Mctavish. The simulations illustrate two typical Mitral-Granule cell circuits in the olfactory bulb of vertebrates: distance-independent lateral inhibition and gating effects.
519.  Olfactory Mitral Cell (Bhalla, Bower 1993)
This is a conversion to NEURON of the mitral cell model described in Bhalla and Bower (1993). The original model was written in GENESIS and is available by joining BABEL, the GENESIS users' group here
520.  Olfactory Mitral Cell (Davison et al 2000)
A four-compartment model of a mammalian olfactory bulb mitral cell, reduced from the complex 286-compartment model described by Bhalla and Bower (1993). The compartments are soma/axon, secondary dendrites, primary dendrite shaft and primary dendrite tuft. The reduced model runs 75 or more times faster than the full model, making its use in large, realistic network models of the olfactory bulb practical.
521.  Olfactory Mitral cell: AP initiation modes (Chen et al 2002)
The mitral cell primary dendrite plays an important role in transmitting distal olfactory nerve input from olfactory glomerulus to the soma-axon initial segment. To understand how dendritic active properties are involved in this transmission, we have combined dual soma and dendritic patch recordings with computational modeling to analyze action-potential initiation and propagation in the primary dendrite.
522.  On stochastic diff. eq. models for ion channel noise in Hodgkin-Huxley neurons (Goldwyn et al. 2010)
" ... We analyze three SDE models that have been proposed as approximations to the Markov chain model: one that describes the states of the ion channels and two that describe the states of the ion channel subunits. We show that the former channel-based approach can capture the distribution of channel noise and its effect on spiking in a Hodgkin-Huxley neuron model to a degree not previously demonstrated, but the latter two subunit-based approaches cannot. ..."
523.  Online learning model of olfactory bulb external plexiform layer network (Imam & Cleland 2020)
This model illustrates the rapid online learning of odor representations, and their recognition despite high levels of interference (other competing odorants), in a model of the olfactory bulb external plexiform layer (EPL) network. The computational principles embedded in this model are based on the those developed in the biophysical models of Li and Cleland (2013, 2017). This is a standard Python version of a model written for Intel's Loihi neuromorphic hardware platform (The Loihi code is available at
524.  Opposing roles for Na+/Ca2+ exchange and Ca2+-activated K+ currents during STDP (O`Halloran 2020)
"Sodium Calcium exchanger (NCX) proteins utilize the electrochemical gradient of Na+ to generate Ca2+ efflux (forward mode) or influx (reverse mode). In mammals, there are three unique NCX encoding genes-NCX1, NCX2, and NCX3, that comprise the SLC8A family, and mRNA from all three exchangers is expressed in hippocampal pyramidal cells. Furthermore, mutant ncx2-/- and ncx3-/- mice have each been shown to exhibit altered long-term potentiation (LTP) in the hippocampal CA1 region due to delayed Ca2+ clearance after depolarization that alters synaptic transmission. In addition to the role of NCX at the synapse of hippocampal subfields required for LTP, the three NCX isoforms have also been shown to localize to the dendrite of hippocampal pyramidal cells. In the case of NCX1, it has been shown to localize throughout the basal and apical dendrite of CA1 neurons where it helps compartmentalize Ca2+ between dendritic shafts and spines. Given the role for NCX and calcium in synaptic plasticity, the capacity of NCX splice-forms to influence backpropagating action potentials has clear consequences for the induction of spike-timing dependent synaptic plasticity (STDP). To explore this, we examined the effect of NCX localization, density, and allosteric activation on forward and back propagating signals and, next employed a STDP paradigm to monitor the effect of NCX on plasticity using back propagating action potentials paired with EPSPs. From our simulation studies we identified a role for the sodium calcium exchange current in normalizing STDP, and demonstrate that NCX is required at the postsynaptic site for this response. We also screened other mechanisms in our model and identified a role for the Ca2+ activated K+ current at the postsynapse in producing STDP responses. Together, our data reveal opposing roles for the Na+/Ca2+ exchanger current and the Ca2+ activated K+ current in setting STDP."
525.  Optical stimulation of a channelrhodopsin-2 positive pyramidal neuron model (Foutz et al 2012)
A computational tool to explore the underlying principles of optogenetic neural stimulation. This "light-neuron" model consists of theoretical representations of the light dynamics generated by a fiber optic in brain tissue, coupled to a multicompartment cable model of a cortical pyramidal neuron (Hu et al. 2009, ModelDB #123897) embedded with channelrhodopsin-2 (ChR2) membrane dynamics. Simulations predict that the activation threshold is sensitive to many of the properties of ChR2 (density, conductivity, and kinetics), tissue medium (scattering and absorbance), and the fiber-optic light source (diameter and numerical aperture). This model system represents a scientific instrument to characterize the effects of optogenetic neuromodulation, as well as an engineering design tool to help guide future development of optogenetic technology.
526.  Optimal spatiotemporal spike pattern detection by STDP (Masquelier 2017)
We simulate a LIF neuron equipped with STDP. A pattern repeats in its inputs. The LIF progressively becomes selective to the repeating pattern, in an optimal manner.
527.  Oversampling method to extract excitatory and inhibitory conductances (Bedard et al. 2012)
" ... We present here a new method that allows extracting estimates of the full time course of excitatory and inhibitory conductances from single-trial Vm recordings. This method is based on oversampling of the Vm . We test the method numerically using models of increasing complexity. Finally, the method is evaluated using controlled conductance injection in cortical neurons in vitro using the dynamic-clamp technique. ..."
528.  Oxytocin and VIP involvement in prolactin secretion (Egli et al. 2004,2006, Bertram et al. 2006)
"Prolactin (PRL) is secreted from lactotrophs of the anterior pituitary gland of rats in a unique pattern in response to uterine cervical stimulation (CS) during mating. Surges of PRL secretion occur in response to relief from hypothalamic dopaminergic inhibition and stimulation by hypothalamic releasing neurohormones. In this study, we characterized the role of oxytocin (OT) in this system and the involvement of vasoactive intestinal polypeptide (VIP) from the suprachiasmatic nucleus (SCN) in controlling OT and PRL secretion of CS rats. ... OT measurements of serial blood samples obtained from ovariectomized (OVX) CS rats displayed a prominent increase at the time of the afternoon PRL peak. The injection of VIP antisense oligonucleotides into the SCN abolished the afternoon increase of OT and PRL in CS-OVX animals. These findings suggest that VIP from the SCN contributes to the regulation of OT and PRL secretion in CS rats. We propose that in CS rats the regulatory mechanism(s) for PRL secretion comprise coordinated action of neuroendocrine dopaminergic and OT cells, both governed by the daily rhythm of VIP-ergic output from the SCN. This hypothesis is illustrated with a mathematical model."
529.  Pacemaker neurons and respiratory rhythm generation (Purvis et al 2007)
"The pre-Botzinger complex (pBC) is a vital subcircuit of the respiratory central pattern generator. Although the existence of neurons with pacemaker-like bursting properties in this network is not questioned, their role in network rhythmogenesis is unresolved. ... We modeled the parameter variability of experimental data from pBC bursting pacemaker and nonpacemaker neurons using a modified version of our previously developed pBC neuron and network models. ... " The paper contains network modeling results that are not represented in this model entry. Only the neuron models are included in this modeldb entry.
530.  Paired turbulence and light effect on calcium increase in Hermissenda (Blackwell 2004)
The sea slug Hermissenda learns to associate light and hair cell stimulation, but not when the stimuli are temporally uncorrelated...These issues were addressed using a multi-compartmental computer model of phototransduction, calcium dynamics, and ionic currents of the Hermissenda photoreceptor...simulations show that a potassium leak channel, which closes with an increase in calcium, is required to produce both the untrained LLD and the enhanced LLD due to the decrease in voltage dependent potassium currents.
531.  Pallidostriatal projections promote beta oscillations (Corbit, Whalen, et al 2016)
This model consists of an inhibitory loop combining the projections from GPe neurons back to the striatum (shown experimentally to predominantly affect fast spiking interneurons, FSIs), together with the coupling from FSIs to medium spiny neurons (MSNs) in the striatum, along with the projections from MSNs to GPe. All models are in the Hodgkin-Huxley formalism, adapted from previously published models for each cell type. The connected circuit produces irregular activity under control conditions, but increasing FSI-to-MSN connectivity as observed experimentally under dopamine depletion yields exaggerated beta oscillations and synchrony. Additional mechanistic aspects are also explored.
532.  Pancreatic Beta Cell signalling pathways (Fridlyand & Philipson 2016) (MATLAB)
This is a 3rd party implementation of Fridlyand & Philipson 2016 who's abstract begins "Insulin secretory in pancreatic beta-cells responses to nutrient stimuli and hormonal modulators include multiple messengers and signaling pathways with complex interdependencies. Here we present a computational model that incorporates recent data on glucose metabolism, plasma membrane potential, G-protein-coupled-receptors (GPCR), cytoplasmic and endoplasmic reticulum calcium dynamics, cAMP and phospholipase C pathways that regulate interactions between second messengers in pancreatic beta-cells. The values of key model parameters were inferred from published experimental data. The model gives a reasonable fit to important aspects of experimentally measured metabolic and second messenger concentrations and provides a framework for analyzing the role of metabolic, hormones and neurotransmitters changes on insulin secretion. Our analysis of the dynamic data provides support for the hypothesis that activation of Ca2+-dependent adenylyl cyclases play a critical role in modulating the effects of glucagon-like peptide 1 (GLP-1), glucose-dependent insulinotropic polypeptide (GIP) and catecholamines. ..."
533.  Paradoxical GABA-mediated excitation (Lewin et al. 2012)
"GABA is the key inhibitory neurotransmitter in the adult central nervous system, but in some circumstances can lead to a paradoxical excitation that has been causally implicated in diverse pathologies from endocrine stress responses to diseases of excitability including neuropathic pain and temporal lobe epilepsy. We undertook a computational modeling approach to determine plausible ionic mechanisms of GABAA-dependent excitation in isolated post-synaptic CA1 hippocampal neurons because it may constitute a trigger for pathological synchronous epileptiform discharge. In particular, the interplay intracellular chloride accumulation via the GABAA receptor and extracellular potassium accumulation via the K/Cl co-transporter KCC2 in promoting GABAA-mediated excitation is complex. ..."
534.  Parameter estimation for Hodgkin-Huxley based models of cortical neurons (Lepora et al. 2011)
Simulation and fitting of two-compartment (active soma, passive dendrite) for different classes of cortical neurons. The fitting technique indirectly matches neuronal currents derived from somatic membrane potential data rather than fitting the voltage traces directly. The method uses an analytic solution for the somatic ion channel maximal conductances given approximate models of the channel kinetics, membrane dynamics and dendrite. This approach is tested on model-derived data for various cortical neurons.
535.  Parameter optimization using CMA-ES (Jedrzejewski-Szmek et al 2018)
"Computational models in neuroscience can be used to predict causal relationships between biological mechanisms in neurons and networks, such as the effect of blocking an ion channel or synaptic connection on neuron activity. Since developing a biophysically realistic, single neuron model is exceedingly difficult, software has been developed for automatically adjusting parameters of computational neuronal models. The ideal optimization software should work with commonly used neural simulation software; thus, we present software which works with models specified in declarative format for the MOOSE simulator. Experimental data can be specified using one of two different file formats. The fitness function is customizable as a weighted combination of feature differences. The optimization itself uses the covariance matrix adaptation-evolutionary strategy, because it is robust in the face of local fluctuations of the fitness function, and deals well with a high-dimensional and discontinuous fitness landscape. We demonstrate the versatility of the software by creating several model examples of each of four types of neurons (two subtypes of spiny projection neurons and two subtypes of globus pallidus neurons) by tuning to current clamp data. ..."
536.  Periodicity in Na channel properties alters model neuron excitability (Majumdar and Sikdar 2007)
"... We have shown earlier that the duration and amplitude of a prolonged depolarization alter all the steady state and kinetic parameters of rNav1.2a voltage gated Na channel in a pseudo-oscillatory fashion. In the present study, we show that the Hodgkin–Huxley voltage and time dependent rate constants of activation (am and bm) and fast inactivation (ah and bh), obtained from the analyses of Na currents and steady state activation and inactivation plots, following application of prepulses in both slow (1–100 s) and fast (100–1000 ms) ranges, vary with the duration of a prepulse in a pseudo-oscillatory manner. ..."
537.  Persistent Spiking in ACC Neurons (Ratte et al 2018)
"Neurons use action potentials, or spikes, to encode information. Some neurons can store information for short periods (seconds to minutes) by continuing to spike after a stimulus ends, thus enabling working memory. This so-called “persistent” spiking occurs in many brain areas and has been linked to activation of canonical transient receptor potential (TRPC) channels. However, TRPC activation alone is insufficient to explain many aspects of persistent spiking such as resumption of spiking after periods of imposed quiescence. Using experiments and simulations, we show that calcium influx caused by spiking is necessary and sufficient to activate TRPC channels and that the ensuing positive feedback interaction between intracellular calcium and TRPC channel activation can account for many hitherto unexplained aspects of persistent spiking."
538.  Phase locking in leaky integrate-and-fire model (Brette 2004)
"This shows the phase-locking structure of a LIF driven by a sinusoidal current. When the current crosses the threshold (a<3), the model almost always phase locks (in a measure-theoretical sense)."
539.  Phase plane reveals two slow variables in midbrain dopamine neuron bursts (Yu and Canavier, 2015)
"Midbrain dopamine neurons exhibit a novel type of bursting that we call “inverted square wave bursting” when exposed to Ca2+-activated small conductance (SK) K+ channel blockers in vitro. This type of bursting has three phases: hyperpolarized silence, spiking, and depolarization block. We find that two slow variables are required for this type of bursting, and we show that the three-dimensional bifurcation diagram for inverted square wave bursting is a folded surface with upper (depolarized) and lower (hyperpolarized) branches. ..."
540.  Phase response curve of a globus pallidal neuron (Fujita et al. 2011)
We investigated how changes in ionic conductances alter the phase response curve (PRC) of a globus pallidal (GP) neuron and stability of a synchronous activity of a GP network, using a single-compartmental conductance-based neuron model. The results showed the PRC and the stability were influenced by changes in the persistent sodium current, the Kv3 potassium, the M-type potassium and the calcium-dependent potassium current.
541.  Phase response curves firing rate dependency of rat purkinje neurons in vitro (Couto et al 2015)
NEURON implementation of stochastic gating in the Khaliq-Raman Purkinje cell model. NEURON implementation of the De Schutter and Bower model of a Purkinje Cell. Matlab scripts to compute the Phase Response Curve (PRC). LCG configuration files to experimentally determine the PRC. Integrate and Fire models (leaky and non-leaky) implemented in BRIAN to see the influence of the PRC in a network of unconnected neurons receiving sparse common input.
542.  Phase-locking analysis with transcranial magneto-acoustical stimulation (Yuan et al 2017)
"Transcranial magneto-acoustical stimulation (TMAS) uses ultrasonic waves and a static magnetic field to generate electric current in nerve tissues for the purpose of modulating neuronal activities. It has the advantage of high spatial resolution and penetration depth. Neuronal firing rhythms carry and transmit nerve information in neural systems. In this study, we investigated the phase-locking characteristics of neuronal firing rhythms with TMAS based on the Hodgkin-Huxley neuron model. The simulation results indicate that the modulation frequency of ultrasound can affect the phase-locking behaviors. The results of this study may help us to explain the potential firing mechanism of TMAS."
543.  Place and grid cells in a loop (Rennó-Costa & Tort 2017)
This model implements a loop circuit between place and grid cells. The model was used to explain place cell remapping and grid cell realignment. Grid cell model as a continuous attractor network. Place cells have recurrent attractor network. Rate models implemented with E%-MAX winner-take-all network dynamics, with gamma cycle time-step.
544.  Pleiotropic effects of SCZ-associated genes (Mäki-Marttunen et al. 2017)
Python and MATLAB scripts for studying the dual effects of SCZ-related genes on layer 5 pyramidal cell firing and sinoatrial node cell pacemaking properties. The study is based on two L5PC models (Hay et al. 2011, Almog & Korngreen 2014) and SANC models (Kharche et al. 2011, Severi et al. 2012).
545.  Point process framework for modeling electrical stimulation of auditory nerve (Goldwyn et al. 2012)
A point process model of the auditory nerve that provides a compact and accurate description of neural responses to electric stimulation. Inspired by the framework of generalized linear models, the model consists of a cascade of linear and nonlinear stages. A semi-analytical procedure uniquely determines each parameter in the model on the basis of fundamental statistics from recordings of single fiber responses to electric stimulation, including threshold, relative spread, jitter, and chronaxie. The model also accounts for refractory and summation effects that influence the responses of auditory nerve fibers to high pulse rate stimulation.
546.  PreBotzinger Complex inspiratory neuron with NaP and CAN currents (Park and Rubin 2013)
We have built on earlier models to develop a single-compartment Hodgkin-Huxley type model incorporating NaP and CAN currents, both of which can play important roles in bursting of inspiratory neurons in the PreBotzinger Complex of the mammalian respiratory brain stem. The model tracks the evolution of membrane potential, related (in)activation variables, calcium concentration, and available fraction of IP3 channels. The model can produce several types of bursting, presented and analyzed from a dynamical systems perspective in our paper.
547.  Preserving axosomatic spiking features despite diverse dendritic morphology (Hay et al., 2013)
The authors found that linearly scaling the ion channel conductance densities of a reference model with the conductance load in 28 3D reconstructed layer 5 thick-tufted pyramidal cells was necessary to match the experimental statistics of these cells electrical firing properties.
548.  Principles governing the operation of synaptic inhibition in dendrites (Gidon & Segev 2012)
A simple result of Gidon & Segev 2012 was provided where distal (off-path) inhibition is demonstrated to be more effective than proximal (on-path) inhibition in a ball and stick neuron.
549.  Principles of Computational Modelling in Neuroscience (Book) (Sterratt et al. 2011)
"... This book provides a step-by-step account of how to model the neuron and neural circuitry to understand the nervous system at all levels, from ion channels to networks. Starting with a simple model of the neuron as an electrical circuit, gradually more details are added to include the effects of neuronal morphology, synapses, ion channels and intracellular signaling. The principle of abstraction is explained through chapters on simplifying models, and how simplified models can be used in networks. This theme is continued in a final chapter on modeling the development of the nervous system. Requiring an elementary background in neuroscience and some high school mathematics, this textbook is an ideal basis for a course on computational neuroscience."
550.  Properties of aconitine-induced block of KDR current in NG108-15 neurons (Lin et al. 2008)
"The effects of aconitine (ACO), a highly toxic alkaloid, on ion currents in differentiated NG108-15 neuronal cells were investigated in this study. ACO (0.3-30 microM) suppressed the amplitude of delayed rectifier K+ current (IK(DR)) in a concentration-dependent manner with an IC50 value of 3.1 microM. The presence of ACO enhanced the rate and extent of IK(DR) inactivation, although it had no effect on the initial activation phase of IK(DR). ... A modeled cell was designed to duplicate its inhibitory effect on spontaneous pacemaking. ... Taken together, the experimental data and simulations show that ACO can block delayed rectifier K+ channels of neurons in a concentration- and state-dependent manner. Changes in action potentials induced by ACO in neurons in vivo can be explained mainly by its blocking actions on IK(DR) and INa."
551.  Proximal inhibition of Renshaw cells (Bui et al 2005)
Inhibitory synaptic inputs to Renshaw cells are concentrated on the soma and the juxtasomatic dendrites. In the present study, we investigated whether this proximal bias leads to more effective inhibition under different neuronal operating conditions. Using compartmental models based on detailed anatomical measurements of intracellularly stained Renshaw cells, we compared the inhibition produced by GABAA synapses when distributed with a proximal bias to the inhibition produced when the same synapses were distributed uniformly. See paper for more and details.
552.  Purkinje neuron network (Zang et al. 2020)
Both spike rate and timing can transmit information in the brain. Phase response curves (PRCs) quantify how a neuron transforms input to output by spike timing. PRCs exhibit strong firing-rate adaptation, but its mechanism and relevance for network output are poorly understood. Using our Purkinje cell (PC) model we demonstrate that the rate adaptation is caused by rate-dependent subthreshold membrane potentials efficiently regulating the activation of Na+ channels. Then we use a realistic PC network model to examine how rate-dependent responses synchronize spikes in the scenario of reciprocal inhibition-caused high-frequency oscillations. The changes in PRC cause oscillations and spike correlations only at high firing rates. The causal role of the PRC is confirmed using a simpler coupled oscillator network model. This mechanism enables transient oscillations between fast-spiking neurons that thereby form PC assemblies. Our work demonstrates that rate adaptation of PRCs can spatio-temporally organize the PC input to cerebellar nuclei.
553.  PyMUS: A Python based Motor Unit Simulator (Kim & Kim 2018)
PyMUS is a simulation software that allows for integrative investigations on the input-output processing of the motor unit system in a hierarchical manner from a single channel to the entire system behavior. Using PyMUS, a single motoneuron, muscle unit and motor unit can be separately simulated under a wide range of experimental input protocols.
554.  Pyramidal neuron coincidence detection tuned by dendritic branching pattern (Schaefer et al 2003)
"... We examined the relationship between dendritic arborization and the coupling between somatic and dendritic action potential (AP) initiation sites in layer 5 (L5) neocortical pyramidal neurons. Coupling was defined as the relative reduction in threshold for initiation of a dendritic calcium AP due to a coincident back-propagating AP. Simulations based on reconstructions of biocytin-filled cells showed that addition of oblique branches of the main apical dendrite in close proximity to the soma (d < 140 um) increases the coupling between the apical and axosomatic AP initiation zones, whereas incorporation of distal branches decreases coupling. ... We conclude that variation in dendritic arborization may be a key determinant of variability in coupling (49+-17%; range 19-83%; n = 37) and is likely to outweigh the contribution made by variations in active membrane properties. Thus coincidence detection of inputs arriving from different cortical layers is strongly regulated by differences in dendritic arborization."
555.  Pyramidal neuron conductances state and STDP (Delgado et al. 2010)
Neocortical neurons in vivo process each of their individual inputs in the context of ongoing synaptic background activity, produced by the thousands of presynaptic partners a typical neuron has. That background activity affects multiple aspects of neuronal and network function. However, its effect on the induction of spike-timing dependent plasticity (STDP) is not clear. Using the present biophysically-detailed computational model, it is not only able to replicate the conductance-dependent shunting of dendritic potentials (Delgado et al,2010), but show that synaptic background can truncate calcium dynamics within dendritic spines, in a way that affects potentiation more strongly than depression. This program uses a simplified layer 2/3 pyramidal neuron constructed in NEURON. It was similar to the model of Traub et al., J Neurophysiol. (2003), and consisted of a soma, an apical shaft, distal dendrites, five basal dendrites, an axon, and a single spine. The spine’s location was variable along the apical shaft (initial 50 &#956;m) and apical. The axon contained an axon hillock region, an initial segment, segments with myelin, and nodes of Ranvier, in order to have realistic action potential generation. For more information about the model see supplemental material, Delgado et al 2010.
556.  Pyramidal Neuron Deep: attenuation in dendrites (Stuart, Spruston 1998)
Stuart, G. and Spruston, N. Determinants of voltage attenuation in neocortical pyramidal neuron dendrites. Journal of Neuroscience 18:3501-3510, 1998.
557.  Pyramidal Neuron Deep: Constrained by experiment (Dyhrfjeld-Johnsen et al. 2005)
"... As a practical demonstration of the use of CoCoDat we constructed a detailed computer model of an intrinsically bursting (IB) layer V pyramidal neuron from the rat barrel cortex supplementing experimental data (Schubert et al., 2001) with information extracted from the database. The pyramidal neuron morphology (Fig. 10B) was reconstructed from histological sections of a biocytin-stained IB neuron using the NeuroLucida software package..."
558.  Pyramidal neuron, fast, regular, and irregular spiking interneurons (Konstantoudaki et al 2014)
This is a model network of prefrontal cortical microcircuit based primarily on rodent data. It includes 16 pyramidal model neurons, 2 fast spiking interneuron models, 1 regular spiking interneuron model and 1 irregular spiking interneuron model. The goal of the paper was to use this model network to determine the role of specific interneuron subtypes in persistent activity
559.  Pyramidal Neuron: Deep, Thalamic Relay and Reticular, Interneuron (Destexhe et al 1998, 2001)
This package shows single-compartment models of different classes of cortical neurons, such as the "regular-spiking", "fast-spiking" and "bursting" (LTS) neurons. The mechanisms included are the Na+ and K+ currents for generating action potentials (INa, IKd), the T-type calcium current (ICaT), and a slow voltage-dependent K+ current (IM). See
560.  Pyramidal neurons switch from integrators to resonators (Prescott et al. 2008)
During wakefulness, pyramidal neurons in the intact brain are bombarded by synaptic input that causes tonic depolarization, increased membrane conductance (i.e. shunting), and noisy fluctuations in membrane potential; by comparison, pyramidal neurons in acute slices typically experience little background input. Such differences in operating conditions can compromise extrapolation of in vitro data to explain neuronal operation in vivo. ... in slice experiments, we show that CA1 hippocampal pyramidal cells switch from integrators to resonators, i.e. from class 1 to class 2 excitability. The switch is explained by increased outward current contributed by the M-type potassium current IM ... Thus, even so-called “intrinsic” properties may differ qualitatively between in vitro and in vivo conditions.
561.  Pyramidal neurons with mutated SCN2A gene (Nav1.2) (Ben-Shalom et al 2017)
Model of pyramidal neurons that either hyper or hypo excitable due to SCN2A mutations. Mutations are taken from patients with ASD or Epilepsy
562.  PyRhO: A multiscale optogenetics simulation platform (Evans et al 2016)
"... we present an integrated suite of open-source, multi-scale computational tools called PyRhO. The purpose of developing PyRhO is three-fold: (i) to characterize new (and existing) opsins by automatically fitting a minimal set of experimental data to three-, four-, or six-state kinetic models, (ii) to simulate these models at the channel, neuron and network levels, and (iii) provide functional insights through model selection and virtual experiments in silico. The module is written in Python with an additional IPython/Jupyter notebook based GUI, allowing models to be fit, simulations to be run and results to be shared through simply interacting with a webpage. The seamless integration of model fitting algorithms with simulation environments (including NEURON and Brian2) for these virtual opsins will enable neuroscientists to gain a comprehensive understanding of their behavior and rapidly identify the most suitable variant for application in a particular biological system. ..."
563.  Rat LGN Thalamocortical Neuron (Connelly et al 2015, 2016)
" ... Here, combining data from fluorescence-targeted dendritic recordings and Ca2+ imaging from low-threshold spiking cells in rat brain slices with computational modeling, the cellular mechanism responsible for LTS (Low Threshold Spike) generation is established. ..." " ... Using dendritic recording, 2-photon glutamate uncaging, and computational modeling, we investigated how rat dorsal lateral geniculate nucleus thalamocortical neurons integrate excitatory corticothalamic feedback. ..."
564.  Rat phrenic motor neuron (Amini et al 2004)
We have developed a model for the rat phrenic motor neuron (PMN) that robustly replicates many experimentally observed behaviors of PMNs in response to pharmacological, ionic, and electrical perturbations using a single set of parameters.
565.  Rat subthalamic projection neuron (Gillies and Willshaw 2006)
A computational model of the rat subthalamic nucleus projection neuron is constructed using electrophysiological and morphological data and a restricted set of channel specifications. The model cell exhibits a wide range of electrophysiological behaviors characteristic of rat subthalamic neurons. It reveals that a key set of three channels play a primary role in distinguishing behaviors: a high-voltage-activated calcium channel (Cav 1.2.-1.3), a low-voltage-activated calcium channel (Cav 3.-), and a small current calcium-activated potassium channel (KCa 2.1-2.3). See paper for more and details.
566.  Reciprocal regulation of rod and cone synapse by NO (Kourennyi et al 2004)
We constructed models of rod and cone photoreceptors using NEURON software to predict how changes in Ca channels would affect the light response in these cells and in postsynaptic horizontal cells.
567.  Reconstructing cerebellar granule layer evoked LFP using convolution (ReConv) (Diwakar et al. 2011)
The model allows reconstruction of evoked local field potentials as seen in the cerebellar granular layer. The approach uses a detailed model of cerebellar granule neuron to generate data traces and then uses a "ReConv" or jittered repetitive convolution technique to reproduce post-synaptic local field potentials in the granular layer. The algorithm was used to generate both in vitro and in vivo evoked LFP and reflected the changes seen during LTP and LTD, when such changes were induced in the underlying neurons by modulating release probability of synapses and sodium channel regulated intrinsic excitability of the cells.
568.  Recording from rod bipolar axon terminals in situ (Oltedal et al 2007)
"... Whole cell recordings from axon terminals and cell bodies were used to investigate the passive membrane properties of rod bipolar cells and analyzed with a two-compartment equivalent electrical circuit model developed by Mennerick et al. For both terminal- and soma-end recordings, capacitive current decays were well fitted by biexponential functions. Computer simulations of simplified models of rod bipolar cells demonstrated that estimates of the capacitance of the axon terminal compartment can depend critically on the recording location, with terminal-end recordings giving the best estimates. Computer simulations and whole cell recordings demonstrated that terminal-end recordings can yield more accurate estimates of the peak amplitude and kinetic properties of postsynaptic currents generated at the axon terminals due to increased electrotonic filtering of these currents when recorded at the soma. ..." See paper for more and details.
569.  Recurrent discharge in a reduced model of cat spinal motoneuron (Balbi et al, 2013)
Following a distal stimulation of a motor fibre, only a fraction of spinal motoneurons are able to produce a re-excitation of the initial segment leading to an orthodromically conducted action potential, known as recurrent discharge. In order to show the reciprocal interplay of the axonal initial segment and the soma leading to recurrent discharge in detail, a reduced model of a cat spinal motoneuron was developed.
570.  Reduced leech heart interneuron (Channell et al. 2009)
"Spiking and bursting patterns of neurons are characterized by a high degree of variability. A single neuron can demonstrate endogenously various bursting patterns, changing in response to external disturbances due to synapses, or to intrinsic factors such as channel noise. We argue that in a model of the leech heart interneuron existing variations of bursting patterns are significantly enhanced by a small noise. In the absence of noise this model shows periodic bursting with fixed numbers of interspikes for most parameter values. ..."
571.  Reflected SDE Hodgkin-Huxley Model (Dangerfield et al. 2012)
Matlab code for simulating channel noise using the original Hodgkin-Huxley equations and a variant of the Hodkgin-Huxley model from (Bruce, Annals Bio Eng, Vol 36, pp 824-838, 2009). Methods used in simulation are SSA, SDE method and RSDE method.
572.  Region-specific atrophy in dendrites (Narayanan, Narayan, Chattarji, 2005) this study, we develop an algorithm that uses statistics from precise morphometric analyses to systematically remodel neuronal reconstructions. We use the distribution function of the ratio of two normal distributed random variables to specify the probabilities of remodeling along various regions of the dendritic arborization. We then use these probabilities to drive an iterative algorithm for manipulating the dendritic tree in a region-specific manner. As a test, we apply this framework to a well characterized example of dendritic remodeling: stress-induced dendritic atrophy in hippocampal CA3 pyramidal cells. We show that our pruning algorithm is capable of eliciting atrophy that matches biological data from rodent models of chronic stress. <br>
573.  Regulation of firing frequency in a midbrain dopaminergic neuron model (Kuznetsova et al. 2010)
A dopaminergic (DA) neuron model with a morphologicaly realistic dendritic architecture. The model captures several salient features of DA neurons under different pharmacological manipulations and exhibits depolarization block for sufficiently high current pulses applied to the soma.
574.  Regulation of KCNQ2/KCNQ3 current by G protein cycling (Suh et al 2004)
Receptor-mediated modulation of KCNQ channels regulates neuronal excitability. This study concerns the kinetics and mechanism of M1 muscarinic receptor-mediated regulation of the cloned neuronal M channel, KCNQ2/KCNQ3 (Kv7.2/Kv7.3). ... observations were successfully described by a kinetic model representing biochemical steps of the signaling cascade using published rate constants where available. The model supports the following sequence of events for this Gq-coupled signaling: A classical G-protein cycle, including competition for nucleotide-free G-protein by all nucleotide forms and an activation step requiring Mg2, followed by G-protein-stimulated phospholipase C and hydrolysis of PIP2, and finally PIP2 dissociation from binding sites for inositol lipid on the channels so that KCNQ current was suppressed. See paper for details and more.
575.  Regulation of motoneuron excitability by KCNQ/Kv7 modulators (Lombardo & Harrington 2016)
" ... Computer simulations confirmed that pharmacological enhancement of KCNQ/Kv7 channel (M current) activity decreases excitability and also suggested that the effects of inhibition of KCNQ/Kv7 channels on the excitability of spinal MNs do not depend on a direct effect in these neurons but likely on spinal cord synaptic partners. These results indicate that KCNQ/Kv7 channels have a fundamental role in the modulation of the excitability of spinal MNs acting both in these neurons and in their local presynaptic partners. ..."
576.  Regulation of the firing pattern in dopamine neurons (Komendantov et al 2004)
Midbrain dopaminergic (DA) neurons in vivo exhibit two major firing patterns: single-spike firing and burst firing. The firing pattern expressed is dependent on both the intrinsic properties of the neurons and their excitatory and inhibitory synaptic inputs. Experimental data suggest that the activation of NMDA and GABAA receptors is crucial contributor to the initiation and suppression of burst firing, respectively, and that blocking calcium-activated potassium channels can facilitate burst firing. This multi-compartmental model of a DA neuron with a branching structure was developed and calibrated based on in vitro experimental data to explore the effects of different levels of activation of NMDA and GABAA receptors as well as the modulation of the SK current on the firing activity.
577.  Rejuvenation model of dopamine neuron (Chan et al. 2007)
Model files for the paper C. Savio Chan, et al. 'Rejuvenation' protects neurons in mouse models of Parkinson's disease, Nature 447, 1081-1086(28 June 2007).
578.  Reliability of Morris-Lecar neurons with added T, h, and AHP currents (Zeldenrust et al. 2013)
We investigated the reliability of the timing of spikes in a spike train in a Morris-Lecar model with several extensions. A frozen Gaussian noise current, superimposed on a DC current, was injected. The neuron responded with spike trains that showed trial-to-trial variability. The reliability depends on the shape (steepness) of the current input versus spike frequency output curve. The model also allowed to study the contribution of three relevant ionic membrane currents to reliability: a T-type calcium current, a cation selective h-current and a calcium dependent potassium current in order to allow bursting, investigate the consequences of a more complex current-frequency relation and produce realistic firing rates.
579.  Reliability of spike timing is a general property of spiking model neurons (Brette & Guigon 2003)
"... Here we show, through simulations and theoretical considerations, that for a general class of spiking neuron models, which includes, in particular, the leaky integrate-and-fire model as well as nonlinear spiking models, aperiodic currents, contrary to periodic currents, induce reproducible responses, which are stable under noise, change in initial conditions and deterministic perturbations of the input. We provide a theoretical explanation for aperiodic currents that cross the threshold."
580.  Reproducing infra-slow oscillations with dopaminergic modulation (Kobayashi et al 2017)
" ... In this paper, to reproduce ISO (Infra-Slow Oscillations) in neural networks, we show that dopaminergic modulation of STDP is essential. More specifically, we discovered a close relationship between two dopaminergic effects: modulation of the STDP function and generation of ISO. We therefore, numerically investigated the relationship in detail and proposed a possible mechanism by which ISO is generated."
581.  Resonance properties through Chirp stimulus responses (Narayanan Johnston 2007, 2008)
...we constructed a simple, single-compartment model with Ih as the only active current... we found that both resonance frequency and resonance strength increased monotonically with the increase in the h conductance, supporting the notion of a direct, graded relationship between h conductance and resonance properties... (Narayanan and Johnston, 2007). ...we show that the h channels introduce an apparent negative delay in the local voltage response of these neurons with respect to the injected current within the theta frequency range... we found that the total inductive phase increased monotonically with the h conductance, whereas it had a bell-shaped dependence on both the membrane voltage and the half-maximal activation voltage for the h conductance. (Narayanan and Johnston, 2008).
582.  Resource competition in growing neurites (Hjorth et al 2014)
Computer model of neurite outgrowth in a simplified neuron. A growth limiting resource is produced in the soma, transported through the neurites and consumed at the growth cones.
583.  Respiratory central pattern generator (mammalian brainstem) (Rubin & Smith 2019)
This model includes a conditional respiratory pacemaker unit (representing the pre-Botzinger Complex), which can be tuned across oscillatory and non-oscillatory dynamic regimes in isolation, embedded into a full respiratory network. The work shows that under this embedding, the pacemaker unit's dynamics become masked: the network exhibits similar dynamical properties regardless of the conditional pacemaker node's tuning, and that node's outputs are dominated by network influences.
584.  Respiratory control model with brainstem CPG and sensory feedback (Diekman, Thomas, and Wilson 2017)
This is a closed-loop respiratory control model incorporating a central pattern generator (CPG), the Butera-Rinzel-Smith (BRS) model, together with lung mechanics, oxygen handling, and chemosensory components. The closed-loop system exhibits bistability of bursting and tonic spiking. Bursting corresponds to coexistence of eupnea-like breathing, with normal minute ventilation and blood oxygen level. Tonic spiking corresponds to a tachypnea-like state, with pathologically reduced minute ventilation and critically low blood oxygen. In our paper, we use the closed-loop system to demonstrate robustness to changes in metabolic demand, spontaneous autoresuscitation in response to hypoxia, and the distinct mechanisms that underlie rhythmogenesis in the intact control circuit vs. the isolated, open-loop CPG.
585.  Response properties of an integrate and fire model (Zhang and Carney 2005)
"A computational technique is described for calculation of the interspike interval and poststimulus time histograms for the responses of an integrate-and-fire model to arbitrary inputs. ... For stationary inputs, the regularity of the output was studied in detail for various model parameters. For nonstationary inputs, the effects of the model parameters on the output synchronization index were explored. ... these response properties have been reported for some cells in the ventral cochlear nucleus in the auditory brainstem. "
586.  Response to correlated synaptic input for HH/IF point neuron vs with dendrite (Górski et al 2018)
" ... Here, we study computational models of neurons to investigate the functional effects of dendritic spikes. In agreement with previous studies, we found that point neurons or neurons with passive dendrites increase their somatic firing rate in response to the correlation of synaptic bombardment for a wide range of input conditions, i.e. input firing rates, synaptic conductances, or refractory periods. However, neurons with active dendrites show the opposite behavior: for a wide range of conditions the firing rate decreases as a function of correlation. We found this property in three types of models of dendritic excitability: a Hodgkin-Huxley model of dendritic spikes, a model with integrate and fire dendrites, and a discrete-state dendritic model. We conclude that fast dendritic spikes confer much broader computational properties to neurons, sometimes opposite to that of point neurons."
587.  Resurgent Na+ current offers noise modulation in bursting neurons (Venugopal et al 2019)
"Neurons utilize bursts of action potentials as an efficient and reliable way to encode information. It is likely that the intrinsic membrane properties of neurons involved in burst generation may also participate in preserving its temporal features. Here we examined the contribution of the persistent and resurgent components of voltage-gated Na+ currents in modulating the burst discharge in sensory neurons. Using mathematical modeling, theory and dynamic-clamp electrophysiology, we show that, distinct from the persistent Na+ component which is important for membrane resonance and burst generation, the resurgent Na+ can help stabilize burst timing features including the duration and intervals. ..."
588.  Rhesus Monkey Layer 3 Pyramidal Neurons: V1 vs PFC (Amatrudo, Weaver et al. 2012)
Whole-cell patch-clamp recordings and high-resolution 3D morphometric analyses of layer 3 pyramidal neurons in in vitro slices of monkey primary visual cortex (V1) and dorsolateral granular prefrontal cortex (dlPFC) revealed that neurons in these two brain areas possess highly distinctive structural and functional properties. ... Three-dimensional reconstructions of V1 and dlPFC neurons were incorporated into computational models containing Hodgkin-Huxley and AMPA- and GABAA-receptor gated channels. Morphology alone largely accounted for observed passive physiological properties, but led to AP firing rates that differed more than observed empirically, and to synaptic responses that opposed empirical results. Accordingly, modeling predicts that active channel conductances differ between V1 and dlPFC neurons. The unique features of V1 and dlPFC neurons are likely fundamental determinants of area-specific network behavior. The compact electrotonic arbor and increased excitability of V1 neurons support the rapid signal integration required for early processing of visual information. The greater connectivity and dendritic complexity of dlPFC neurons likely support higher level cognitive functions including working memory and planning.
589.  Rhesus Monkey Layer 3 Pyramidal Neurons: Young vs aged PFC (Coskren et al. 2015)
Layer 3 (L3) pyramidal neurons in the lateral prefrontal cortex (LPFC) of rhesus monkeys exhibit dendritic regression, spine loss and increased action potential (AP) firing rates during normal aging. The relationship between these structural and functional alterations, if any, is unknown. Computational models using the digital reconstructions with Hodgkin-Huxley and AMPA channels allowed us to assess relationships between demonstrated age-related changes and to predict physiological changes that have not yet been tested empirically. Tuning passive parameters for each model predicted significantly higher membrane resistance (Rm) in aged versus young neurons. This Rm increase alone did not account for the empirically observed fI-curves, but coupling these Rm values with subtle differences in morphology and membrane capacitance Cm did. The predicted differences in passive parameters (or other parameters with similar effects) are mathematically plausible, but must be tested empirically.
590.  Rhesus Monkey Young and Aged L3 PFC Pyramidal Neurons (Rumbell et al. 2016)
A stereotypical pyramidal neuron morphology with ion channel parameter combinations that reproduce firing patterns of one young and one aged rhesus monkey L3 PFC pyramidal neurons. Parameters were found through an automated optimization method.
591.  Robust and tunable bursting requires slow positive feedback (Franci et al 2018)
"We highlight that the robustness and tunability of a bursting model critically rely on currents that provide slow positive feedback to the membrane potential. Such currents have the ability to make the total conductance of the circuit negative in a timescale that is termed “slow” because it is intermediate between the fast timescale of the spike upstroke and the ultraslow timescale of even slower adaptation currents. We discuss how such currents can be assessed either in voltage-clamp experiments or in computational models. We show that, while frequent in the literature, mathematical and computational models of bursting that lack the slow negative conductance are fragile and rigid. Our results suggest that modeling the slow negative conductance of cellular models is important when studying the neuromodulation of rhythmic circuits at any broader scale."
592.  Robust modulation of integrate-and-fire models (Van Pottelbergh et al 2018)
"By controlling the state of neuronal populations, neuromodulators ultimately affect behavior. A key neuromodulation mechanism is the alteration of neuronal excitability via the modulation of ion channel expression. This type of neuromodulation is normally studied with conductance-based models, but those models are computationally challenging for large-scale network simulations needed in population studies. This article studies the modulation properties of the multiquadratic integrate-and-fire model, a generalization of the classical quadratic integrate-and-fire model. The model is shown to combine the computational economy of integrate-and-fire modeling and the physiological interpretability of conductance-based modeling. It is therefore a good candidate for affordable computational studies of neuromodulation in large networks."
593.  Robust transmission in the inhibitory Purkinje Cell to Cerebellar Nuclei pathway (Abbasi et al 2017)
594.  Rod photoreceptor (Barnes and Hille 1989, Publio et al. 2006, Kourennyi and Liu et al. 2004)
This a conductance-based model of a rod photoreceptor cell based on other modeling works (Barnes and Hille 1989 and Publio et al. 2006 and Kourennyi and Liu et al. 2004 ). In this model four types of ionic channels identified in the inner segment of the rod: nonselective cation channel (h), delayed rectifying potassium channel (Kv), noninactivating potassium channel (Kx) and calcium channel (Ca) was used. The model accurately reproduces the rod response when stimulated with a simulated photocurrent signal. We can show the effect of nonselective cation channel. The absence of this channel cause increasing the peak amplitude and the time to reach the peak of voltage response and absence of transient mode in this response.
595.  Role of active dendrites in rhythmically-firing neurons (Goldberg et al 2006)
"The responsiveness of rhythmically-firing neurons to synaptic inputs is characterized by their phase response curve (PRC), which relates how weak somatic perturbations affect the timing of the next action potential. The shape of the somatic PRC is an important determinant of collective network dynamics. Here we study theoretically and experimentally the impact of distally-located synapses and dendritic nonlinearities on the synchronization properties of rhythmically firing neurons. Combining the theories of quasi-active cables and phase-coupled oscillators we derive an approximation for the dendritic responsiveness, captured by the neuron's dendritic PRC (dPRC). This closed-form expression indicates that the dPRCs are linearly-filtered versions of the somatic PRC, and that the filter characteristics are determined by the passive and active properties of the dendrite. ... collective dynamics can be qualitatively different depending on the location of the synapse, the neuronal firing rates and the dendritic nonlinearities." See paper for more and details.
596.  Role of afferent-hair cell connectivity in determining spike train regularity (Holmes et al 2017)
"Vestibular bouton afferent terminals in turtle utricle can be categorized into four types depending on their location and terminal arbor structure: lateral extrastriolar (LES), striolar, juxtastriolar, and medial extrastriolar (MES). The terminal arbors of these afferents differ in surface area, total length, collecting area, number of boutons, number of bouton contacts per hair cell, and axon diameter (Huwe JA, Logan CJ, Williams B, Rowe MH, Peterson EH. J Neurophysiol 113: 2420 –2433, 2015). To understand how differences in terminal morphology and the resulting hair cell inputs might affect afferent response properties, we modeled representative afferents from each region, using reconstructed bouton afferents. ..."
597.  Role of Ih in firing patterns of cold thermoreceptors (Orio et al., 2012)
" ... Here we investigated the role of Ih in cold-sensitive (CS) nerve endings, where cold sensory transduction actually takes place. Corneal CS nerve endings in mice show a rhythmic spiking activity at neutral skin temperature that switches to bursting mode when the temperature is lowered. ... Mathematical modeling shows that the firing phenotype of CS nerve endings from HCN1-/- mice can be reproduced by replacing HCN1 channels with the slower HCN2 channels rather than by abolishing Ih. We propose that Ih carried by HCN1 channels helps tune the frequency of the oscillation and the length of bursts underlying regular spiking in cold thermoreceptors, having important implications for neural coding of cold sensation. "
598.  Role of the AIS in the control of spontaneous frequency of dopaminergic neurons (Meza et al 2017)
Computational modeling showed that the size of the Axon Initial Segment (AIS), but not its position within the somatodendritic domain, is the major causal determinant of the tonic firing rate in the intact model, by virtue of the higher intrinsic frequency of the isolated AIS. Further mechanistic analysis of the relationship between neuronal morphology and firing rate showed that dopaminergic neurons function as a coupled oscillator whose frequency of discharge results from a compromise between AIS and somatodendritic oscillators.
599.  Roles of I(A) and morphology in AP prop. in CA1 pyramidal cell dendrites (Acker and White 2007)
" ...Using conductance-based models of CA1 pyramidal cells, we show that underlying “traveling wave attractors” control action potential propagation in the apical dendrites. By computing these attractors, we dissect and quantify the effects of IA channels and dendritic morphology on bAP amplitudes. We find that non-uniform activation properties of IA can lead to backpropagation failure similar to that observed experimentally in these cells. ... "
600.  Salamander retinal ganglian cells: morphology influences firing (Sheasby, Fohlmeister 1999)
Nerve impulse entrainment and other excitation and passive phenomena are analyzed for a morphologically diverse and exhaustive data set (n=57) of realistic (3-dimensional computer traced) soma-dendritic tree structures of ganglion cells in the tiger salamander (Ambystoma tigrinum) retina.
601.  Salamander retinal ganglion cell: ion channels (Fohlmeister, Miller 1997)
A realistic five (5) channel spiking model reproduces the bursting behavior of tiger salamander ganglion cells in the retina. Please see the readme for more information.
602.  Schiz.-linked gene effects on intrinsic single-neuron excitability (Maki-Marttunen et al. 2016)
Python scripts for running NEURON simulations that model a layer V pyramidal cell with certain genetic variants implemented. The genes included are obtained from genome-wide association studies of schizophrenia.
603.  SCZ-associated variant effects on L5 pyr cell NN activity and delta osc. (Maki-Marttunen et al 2018)
" … Here, using computational modeling, we show that a common biomarker of schizophrenia, namely, an increase in delta-oscillation power, may be a direct consequence of altered expression or kinetics of voltage-gated ion channels or calcium transporters. Our model of a circuit of layer V pyramidal cells highlights multiple types of schizophrenia-related variants that contribute to altered dynamics in the delta frequency band. Moreover, our model predicts that the same membrane mechanisms that increase the layer V pyramidal cell network gain and response to delta-frequency oscillations may also cause a decit in a single-cell correlate of the prepulse inhibition, which is a behavioral biomarker highly associated with schizophrenia."
604.  Selective control of cortical axonal spikes by a slowly inactivating K+ current (Shu et al. 2007)
We discovered a low-threshold, slowly inactivating K+ current, containing Kv1.2 alpha subunits, in axon initial segment, playing a key role in the modulation of spike threshold and spike duration as well as the spike timing in prefrontal cortex layer V pyramidal cell of ferrets and rats. A kd.mod file implements this D current and put it in the axonal model: Neuron_Dcurrent.hoc. Run the model to see the gradual modulation effect over seconds on spike shape.
605.  Self-influencing synaptic plasticity (Tamosiunaite et al. 2007)
"... Similar to a previous study (Saudargiene et al., 2004) we employ a differential Hebbian learning rule to emulate spike-timing dependent plasticity and investigate how the interaction of dendritic and back-propagating spikes, as the post-synaptic signals, could influence plasticity. ..."
606.  Sensory-evoked responses of L5 pyramidal tract neurons (Egger et al 2020)
This is the L5 pyramidal tract neuron (L5PT) model from Egger, Narayanan et al., Neuron 2020. It allows investigating how synaptic inputs evoked by different sensory stimuli are integrated by the complex intrinsic properties of L5PTs. The model is constrained by anatomical measurements of the subcellular synaptic input patterns to L5PT neurons, in vivo measurements of sensory-evoked responses of different populations of neurons providing these synaptic inputs, and in vitro measurements constraining the biophysical properties of the soma, dendrites and axon (note: the biophysical model is based on the work by Hay et al., Plos Comp Biol 2011). The model files provided here allow performing simulations and analyses presented in Figures 3, 4 and 5.
607.  Serotonergic modulation of Aplysia sensory neurons (Baxter et al 1999)
The present study investigated how the modulation of these currents altered the spike duration and excitability of sensory neurons and examined the relative contributions of PKA- and PKC-mediated effects to the actions of 5-HT. A Hodgkin-Huxley type model was developed that described the ionic conductances in the somata of sensory neurons. The descriptions of these currents and their modulation were based largely on voltageclamp data from sensory neurons. Simulations were preformed with the program SNNAP (Simulator for Neural Networks and Action Potentials). The model was sufficient to replicate empirical data that describes the membrane currents, action potential waveform and excitability as well as their modulation by application of 5-HT, increased levels of adenosine cyclic monophosphate or application of active phorbol esters. The results provide several predictions that warrant additional experimental investigation and illustrate the importance of considering indirect as well as direct effects of modulatory agents on the modulation of membrane currents. See paper for more details.
608.  Shaping NMDA spikes by timed synaptic inhibition on L5PC (Doron et al. 2017)
This work (published in "Timed synaptic inhibition shapes NMDA spikes, influencing local dendritic processing and global I/O properties of cortical neurons", Doron et al, Cell Reports, 2017), examines the effect of timed inhibition over dendritic NMDA spikes on L5PC (Based on Hay et al., 2011) and CA1 cell (Based on Grunditz et al. 2008 and Golding et al. 2001).
609.  Shaping of action potentials by different types of BK channels (Jaffe et al., 2011)
Dentate gyrus granule cells highly express the beta4 accessory subunit which confer BK channels with type II properties. The properties of heterologously-expressed BK channels (with and without the beta4 subunit) were used to construct channel models. These were then used to study how they affect single action potentials and trains of spikes in a model dentate gyrus granule cells (based on Aradi and Holmes, 1999).
610.  Sharpness of spike initiation in neurons explained by compartmentalization (Brette 2013)
"Spike initiation determines how the combined inputs to a neuron are converted to an output. Since the pioneering work of Hodgkin and Huxley, it is known that spikes are generated by the opening of sodium channels with depolarization. According to this standard theory, these channels should open gradually when the membrane potential increases, but spikes measured at the soma appear to suddenly rise from rest. This apparent contradiction has triggered a controversy about the origin of spike “sharpness.” This study shows with biophysical modelling that if sodium channels are placed in the axon rather than in the soma, they open all at once when the somatic membrane potential exceeds a critical value. This work explains the sharpness of spike initiation and provides another demonstration that morphology plays a critical role in neural function."
611.  Signal fidelity in the rostral nucleus of the solitary tract (Boxwell et al 2018)
"Neurons in the rostral nucleus of the solitary tract (rNST) convey taste information to both local circuits and pathways destined for forebrain structures. This nucleus is more than a simple relay, however, because rNST neurons differ in response rates and tuning curves relative to primary afferent fibers. To systematically study the impact of convergence and inhibition on firing frequency and breadth of tuning (BOT) in rNST, we constructed a mathematical model of its two major cell types: projection neurons and inhibitory neurons. First, we fit a conductance-based neuronal model to data derived from whole cell patch-clamp recordings of inhibitory and noninhibitory neurons in a mouse expressing Venus under the control of the VGAT promoter. We then used in vivo chorda tympani (CT) taste responses as afferent input to modeled neurons and assessed how the degree and type of convergence influenced model cell output frequency and BOT for comparison with in vivo gustatory responses from the rNST. Finally, we assessed how presynaptic and postsynaptic inhibition impacted model cell output. ..."
612.  Signal integration in a CA1 pyramidal cell (Graham 2001)
This model investigates signal integration in the dendritic tree of a hippocampal CA1 pyramidal cell when different combinations of active channels are present in the tree (Graham, 2001)
613.  Signal integration in LGN cells (Briska et al 2003)
Computer models were used to investigate passive properties of lateral geniculate nucleus thalamocortical cells and thalamic interneurons based on in vitro whole-cell study. Two neurons of each type were characterized physiologically and morphologically. Differences in the attenuation of propagated signals depend on both cell morphology and signal frequency. See the paper for details.
614.  Simple and accurate Diffusion Approximation algor. for stochastic ion channels (Orio & Soudry 2012)
" ... We derived the (Stochastic Differential Equations) SDE explicitly for any given ion channel kinetic scheme. The resulting generic equations were surprisingly simple and interpretable – allowing an easy, transparent and efficient (Diffusion Approximation) DA implementation, avoiding unnecessary approximations. The algorithm was tested in a voltage clamp simulation and in two different current clamp simulations, yielding the same results as (Markov Chains) MC modeling. Also, the simulation efficiency of this DA method demonstrated considerable superiority over MC methods, except when short time steps or low channel numbers were used."
615.  Simple model of barrel-specific segregation in cortex (Lu et al 2006)
Mice with a loss-of-function mutation of calcium/calmodulin-activated adenylyl cyclase I (AC1) - barrelless mice - have strikingly abherrent cortical development: the thalamic afferents into the barrel cortex do not segregate into whisker-specific barrels. Our paper investigates the link between this mutation and the "barrelless" phenotype, and demonstrates that the loss-of-function mutation leads to deficits in presynaptic mechanisms at the thalamocortical synapse. How might presynaptic deficits disrupt whisker-specific segregation in the barrel cortex? We used a model to demonstrate one possibility: decrease in the release probability at the thalamocortical synapse (which is observed in the barrelless mutant) can influence the balance between LTP and LTD (in favor of LTD), which can disrupt whisker segregaton. Though how this occurs is easily explained with a conceptual model (described succinctly in the associated paper), we also produced a computational simulation of this phenomenon.
616.  Simulated light response in rod photoreceptors (Liu and Kourennyi 2004)
We developed a complete computer model of the rod, which accurately reproduced the main features of the light response and allowed us to demonstrate that it was suppression of Kx channels that was essential for slowing SLR and increasing excitability of rods. The results reported in this work further establish the importance of Kx channels in rod photoreceptor function.
617.  Simulating ion channel noise in an auditory brainstem neuron model (Schmerl & McDonnell 2013)
" ... Here we demonstrate that biophysical models of channel noise can give rise to two kinds of recently discovered stochastic facilitation effects in a Hodgkin-Huxley-like model of auditory brainstem neurons. The first, known as slope-based stochastic resonance (SBSR), enables phasic neurons to emit action potentials that can encode the slope of inputs that vary slowly relative to key time constants in the model. The second, known as inverse stochastic resonance (ISR), occurs in tonically firing neurons when small levels of noise inhibit tonic firing and replace it with burstlike dynamics. ..." Preprint available at
618.  Simulation studies on mechanisms of levetiracetam-mediated inhibition of IK(DR) (Huang et al. 2009)
Levetiracetam (LEV) is an S-enantiomer pyrrolidone derivative with established antiepileptic efficacy in generalized epilepsy and partial epilepsy. However, its effects on ion currents and membrane potential remain largely unclear. In this study, we investigated the effect of LEV on differentiated NG108-15 neurons. ... Simulation studies in a modified Hodgkin-Huxley neuron and network unraveled that the reduction of slowly inactivating IK(DR) resulted in membrane depolarization accompanied by termination of the firing of action potentials in a stochastic manner. Therefore, the inhibitory effects on slowly inactivating IK(DR) (Kv3.1-encoded current) may constitute one of the underlying mechanisms through which LEV affects neuronal activity in vivo.
619.  Simulation study of Andersen-Tawil syndrome (Sung et al 2006)
Patients with Andersen-Tawil syndrome (ATS) mostly have mutations on the KCNJ2 gene producing loss of function or dominant-negative suppression of the inward rectifier K(+) channel Kir2.1. However, clinical manifestations of ATS including dysmorphic features, periodic paralysis (hypo-, hyper-, or normokalemic), long QT, and ventricular arrhythmias (VA) are considerably variable. Using a modified dynamic Luo-Rudy simulation model of cardiac ventricular myocyte, we elucidate the mechanisms of VA in ATS. We adopted a kinetic model of KCNJ2 in which channel block by Mg(+2) and spermine was incorporated. In this study, we attempt to examine the effects of KCNJ2 mutations on the ventricular action potential (AP), single-channel Markovian models were reformulated and incorporated into the dynamic Luo-Rudy model for rapidly and slowly delayed rectifying K(+) currents and KCNJ2 channel. During pacing at 1.0 Hz with [K(+)]o at 5.4 mM, a stepwise 10% reduction of Kir2.1 channel conductance progressively prolonged the terminal repolarization phase of AP along with gradual depolarization of the resting membrane potential (RMP). At 90% reduction, early after- depolarizations (EADs) became inducible and RMP was depolarized to -55.0 mV (control: -90.1 mV) followed by emergence of spontaneous action potentials (SAP). Both EADs and SAP were facilitated by a decrease in [K(+)]o and suppressed by increase in [K(+)]o. beta-adrenergic stimulation enhanced delayed after-depolarizations (DADs) and could also facilitate EADs as well as SAP in the setting of low [K(+)]o and reduced Kir2.1 channel conductance. In conclusion, the spectrum of VA in ATS includes (1) triggered activity mediated by EADs and/or DADs, and (2) abnormal automaticity manifested as SAP. These VA can be aggravated by a decrease in [K(+)]o and beta-adrenergic stimulation, and may potentially induce torsades de pointes and cause sudden death. In patients with ATS, the hypokalemic form of periodic paralysis should have the highest propensity to VA especially during physical activities.
620.  Simulations of motor unit discharge patterns (Powers et al. 2011)
" ... To estimate the potential contributions of PIC (Persistent Inward Current) activation and synaptic input patterns to motor unit discharge patterns, we examined the responses of a set of cable motoneuron models to different patterns of excitatory and inhibitory inputs. The models were first tuned to approximate the current- and voltage-clamp responses of low- and medium-threshold spinal motoneurons studied in decerebrate cats and then driven with different patterns of excitatory and inhibitory inputs. The responses of the models to excitatory inputs reproduced a number of features of human motor unit discharge. However, the pattern of rate modulation was strongly influenced by the temporal and spatial pattern of concurrent inhibitory inputs. Thus, even though PIC activation is likely to exert a strong influence on firing rate modulation, PIC activation in combination with different patterns of excitatory and inhibitory synaptic inputs can produce a wide variety of motor unit discharge patterns."
621.  Single cell model with variable ion concentrations and Na+/K+ ATPase (Krishnan et al. 2015)
This is a single cell model with variable intra and extra cellular ion concentrations for Na+, K+ and Cl- ions. This model also incorporates both the electrogenic and ionic concentration effects of the Na+/K+ pump. The program in this archive will run under CONTENT software to generate various bifurcation plots.
622.  Single compartment Dorsal Lateral Medium Spiny Neuron w/ NMDA and AMPA (Biddell and Johnson 2013)
A biophysical single compartment model of the dorsal lateral striatum medium spiny neuron is presented here. The model is an implementation then adaptation of a previously described model (Mahon et al. 2002). The model has been adapted to include NMDA and AMPA receptor models that have been fit to dorsal lateral striatal neurons. The receptor models allow for excitation by other neuron models.
623.  Single excitatory axons form clustered synapses onto CA1 pyramidal cell dendrites (Bloss et al 2018)
" ... Here we show that single presynaptic axons form multiple, spatially clustered inputs onto the distal, but not proximal, dendrites of CA1 pyramidal neurons. These compound connections exhibit ultrastructural features indicative of strong synapses and occur much more commonly in entorhinal than in thalamic afferents. Computational simulations revealed that compound connections depolarize dendrites in a biophysically efficient manner, owing to their inherent spatiotemporal clustering. ..."
624.  Single neuron with dynamic ion concentrations (Cressman et al. 2009)
These are the full and reduced models of a generic single neuron with dynamic ion concentrations as described in Cressman et al., Journal of Computational Neuroscience (2009) 26:159–170.
625.  Single neuron with ion concentrations to model anoxic depolarization (Zandt et al. 2011)
A minimal single neuron model, including changing ion concentrations and homeostasis mechanisms. It shows the sudden depolarization that occurs after prolonged anoxia/ischemia.
626.  Site of impulse initiation in a neuron (Moore et al 1983)
Examines the effect of temperature, the taper of the axon hillock, and HH channel density on antidromic spike invasion into the soma and spike initiation under dendritic stimulation.
627.  Sloppy morphological tuning in identified neurons of the crustacean STG (Otopalik et al 2017)
" ...Theoretical studies suggest that morphology is tightly tuned to minimize wiring and conduction delay of synaptic events. We utilize high-resolution confocal microscopy and custom computational tools to characterize the morphologies of four neuron types in the stomatogastric ganglion (STG) of the crab Cancer borealis. Macroscopic branching patterns and fine cable properties are variable within and across neuron types. We compare these neuronal structures to synthetic minimal spanning neurite trees constrained by a wiring cost equation and find that STG neurons do not adhere to prevailing hypotheses regarding wiring optimization principles. In this highly-modulated and oscillating circuit, neuronal structures appear to be governed by a space-filling mechanism that outweighs the cost of inefficient wiring."
628.  Small world networks of Type I and Type II Excitable Neurons (Bogaard et al. 2009)
Implemented with NEURON 5.9, four model neurons with varying excitability properties affect the spatiotemporal patterning of small world networks of homogeneous and heterogeneous cell population.
629.  Sodium channel mutations causing generalized epilepsy with febrile seizures + (Barela et al. 2006)
A novel mutation, R859C, in the Nav1.1 sodium channel was identified in a 4-generation, 33-member Caucasian family with a clinical presentation consistent with GEFS+. The mutation neutralizes a positively charged arginine in the domain 2 S4 voltage sensor of the Nav1.1 channel Ą subunit. When the mutation was placed in the rat Nav1.1 channel and expressed in Xenopus oocytes, the mutant channel displayed a positive shift in the voltage-dependence of sodium channel activation, slower recovery from slow inactivation, and lower levels of current compared to the wild-type channel. Computational analysis suggests that neurons expressing the mutant channel have higher thresholds for firing a single action potential and for firing multiple action potentials, along with decreased repetitive firing. Therefore, this mutation should lead to decreased neuronal excitability, in contrast to most previous GEFS+ sodium channel mutations that have changes predicted to increase neuronal firing.
630.  Software for teaching the Hodgkin-Huxley model (Hernandez & Zurek 2013) (SENB written in NEURON hoc)
" ... The SENB software offers several advantages for teaching and learning electrophysiology. First, SENB offers ease and flexibility in determining the number of stimuli. Second, SENB allows immediate and simultaneous visualization, in the same window and time frame, of the evolution of the electrophysiological variables. Third, SENB calculates parameters such as time and space constants, stimuli frequency, cellular area and volume, sodium and potassium equilibrium potentials, and propagation velocity of the action potentials. ..."
631.  Space clamp problems in neurons with voltage-gated conductances (Bar-Yehuda and Korngreen 2008)
" ... using numerical simulations, we show that the distortions of voltage-gated K+ and Ca2+ currents are substantial even in neurons with short dendrites. The simulations also demonstrate that passive cable theory cannot be used to justify voltage-clamping of neurons, due to significant shunting to the reversal potential of the voltage-gated conductance during channel activation. ... "
632.  Spatial gridding and temporal accuracy in NEURON (Hines and Carnevale 2001)
A heuristic for compartmentalization based on the space constant at 100 Hz is proposed. The paper also discusses spatio/temporal accuracy and the use of CVODE.
633.  Spatial summation of excitatory and inhibitory inputs in pyramidal neurons (Hao et al. 2010)
"... Based on realistic modeling and experiments in rat hippocampal slices, we derived a simple arithmetic rule for spatial summation of concurrent excitatory glutamatergic inputs (E) and inhibitory GABAergic inputs (I). The somatic response can be well approximated as the sum of the excitatory postsynaptic potential (EPSP), the inhibitory postsynaptic potential (IPSP), and a nonlinear term proportional to their product (k*EPSP*IPSP), where the coefficient k reflects the strength of shunting effect. ..."
634.  Species-specific wiring for direction selectivity in the mammalian retina (Ding et al 2016)
" ... Here we present a detailed connectomic reconstruction of SAC circuitry in mouse retina and describe two previously unknown features of synapse distributions along SAC dendrites: input and output synapses are segregated, with inputs restricted to proximal dendrites; and the distribution of inhibitory inputs is fundamentally different from that observed in rabbit retina. An anatomically constrained SAC network model suggests that SAC–SAC wiring differences between mouse and rabbit retina underlie distinct contributions of synaptic inhibition to velocity and contrast tuning and receptive field structure. In particular, the model indicates that mouse connectivity enables SACs to encode lower linear velocities that account for smaller eye diameter, thereby conserving angular velocity tuning. These predictions are confirmed with calcium imaging of mouse SAC dendrites responding to directional stimuli. ..."
635.  Spectral method and high-order finite differences for nonlinear cable (Omurtag and Lytton 2010)
We use high-order approximation schemes for the space derivatives in the nonlinear cable equation and investigate the behavior of numerical solution errors by using exact solutions, where available, and grid convergence. The space derivatives are numerically approximated by means of differentiation matrices. A flexible form for the injected current is used that can be adjusted smoothly from a very broad to a narrow peak, which leads, for the passive cable, to a simple, exact solution. We provide comparisons with exact solutions in an unbranched passive cable, the convergence of solutions with progressive refinement of the grid in an active cable, and the simulation of spike initiation in a biophysically realistic single-neuron model.
636.  Spike burst-pause dynamics of Purkinje cells regulate sensorimotor adaptation (Luque et al 2019)
"Cerebellar Purkinje cells mediate accurate eye movement coordination. However, it remains unclear how oculomotor adaptation depends on the interplay between the characteristic Purkinje cell response patterns, namely tonic, bursting, and spike pauses. Here, a spiking cerebellar model assesses the role of Purkinje cell firing patterns in vestibular ocular reflex (VOR) adaptation. The model captures the cerebellar microcircuit properties and it incorporates spike-based synaptic plasticity at multiple cerebellar sites. ..."
637.  Spike frequency adaptation in spinal sensory neurones (Melnick et al 2004)
Using tight-seal recordings from rat spinal cord slices, intracellular labelling and computer simulation, we analysed the mechanisms of spike frequency adaptation in substantia gelatinosa (SG) neurones. Adapting-firing neurones (AFNs) generated short bursts of spikes during sustained depolarization and were mostly found in lateral SG. ... Ca2 + -dependent conductances do not contribute to adapting firing. Transient KA current was small and completely inactivated at resting potential suggesting that adapting firing was mainly generated by voltage-gated Na+ and delayed-rectifier K+ (KDR ) currents. ... Computer simulation has further revealed that down-regulation of Na+ conductance represents an effective mechanism for the induction of firing adaptation. It is suggested that the cell-specific regulation of Na+ channel expression can be an important factor underlying the diversity of firing patterns in SG neurones. See paper for more and details.
638.  Spike frequency adaptation in the LGMD (Peron and Gabbiani 2009)
This model is used in the referenced paper to demonstrate that a model of an SK-like calcium-sensitive potassium (KCa) conductance can replicate the spike frequency adaptation (SFA) of the locust lobula giant movement detector (LGMD) neuron. The model simulates current injection experiments with and without KCa block in the LGMD, as well as visual stimulation experiments with and without KCa block.
639.  Spike Initiation in Neocortical Pyramidal Neurons (Mainen et al 1995)
This model reproduces figure 3A from the paper Mainen ZF, Joerges J, Huguenard JR, Sejnowski TJ (1995). Please see the paper for detail whose full text is available at Email Zach Mainen for questions:
640.  Spike repolarization in axon collaterals (Foust et al. 2011)
Voltage sensing dye experiments and simulations characterize the location and re-polarizing function of Kv1 channels in cortical neurons. "... (the papers) results indicate that action potential-induced synaptic transmission may operate through a mix of analog–digital transmission owing to the properties of Kv1 channels in axon collaterals and presynaptic boutons."
641.  Spike Response Model simulator (Jolivet et al. 2004, 2006, 2008)
The Spike Response Model (SRM) optimized on the experimental data in the Single-Neuron modelling Competition ( ) for edition 2007 and edition 2008. The Spike Response Model is a simplified model of neuronal excitability where current linearly integrates to an artificial threshold. After the spike, the threshold is augmented and the voltage follows a voltage kernel that is the average voltage trace during and after a spike. The parameters were chosen to best fit the observed spike times with a method outlined in Jolivet et al. (2006).
642.  Spike-timing dependent inhibitory plasticity for gating bAPs (Wilmes et al 2017)
"Inhibition is known to influence the forward-directed flow of information within neurons. However, also regulation of backward-directed signals, such as backpropagating action potentials (bAPs), can enrich the functional repertoire of local circuits. Inhibitory control of bAP spread, for example, can provide a switch for the plasticity of excitatory synapses. Although such a mechanism is possible, it requires a precise timing of inhibition to annihilate bAPs without impairment of forward-directed excitatory information flow. Here, we propose a specific learning rule for inhibitory synapses to automatically generate the correct timing to gate bAPs in pyramidal cells when embedded in a local circuit of feedforward inhibition. Based on computational modeling of multi-compartmental neurons with physiological properties, we demonstrate that a learning rule with anti-Hebbian shape can establish the required temporal precision. ..."
643.  Spikelet generation and AP initiation in a L5 neocortical pyr neuron (Michalikova et al. 2017) Fig 1
The article by Michalikova et al. (2017) explores the generation of spikelets in cortical pyramidal neurons. The model cell, adapted from Hu et al. (2009), is a layer V pyramidal neuron. The cell is stimulated by fluctuating synaptic inputs and generates somatic APs and spikelets in response. The spikelets are initiated as APs at the AIS that do not activate the soma.
644.  Spikelet generation and AP initiation in a simplified pyr neuron (Michalikova et al. 2017) Fig 3
The article by Michalikova et al. (2017) explores the generation of spikelets in cortical pyramidal neurons. This package contains code for simulating the model with simplified morphology shown in Figs 3 and S2.
645.  Spiking GridPlaceMap model (Pilly & Grossberg, PLoS One, 2013)
Development of spiking grid cells and place cells in the entorhinal-hippocampal system to represent positions in large spaces
646.  Spinal Motor Neuron (Dodge, Cooley 1973)
"The excitability of various regions of the spinal motorneuron can be specified by solving the partial differential equation of a nerve fiber whose diameter and membrane properties vary with distance. For our model geometrical factors for the myelinated axon, initial segment and cell body were derived from anatomical measurements, the dendritic tree was represented by its equivalent cylinder, and the current-voltage relations of the membrane were described by a modification of the Hodgkin-Huxley model that fits voltage-clamp data from the motorneuron. ..."
647.  Spine head calcium in a CA1 pyramidal cell model (Graham et al. 2014)
"We use a computational model of a hippocampal CA1 pyramidal cell to demonstrate that spine head calcium provides an instantaneous readout at each synapse of the postsynaptic weighted sum of all presynaptic activity impinging on the cell. The form of the readout is equivalent to the functions of weighted, summed inputs used in neural network learning rules. Within a dendritic layer, peak spine head calcium levels are either a linear or sigmoidal function of the number of coactive synapses, with nonlinearity depending on the ability of voltage spread in the dendrites to reach calcium spike threshold. ..."
648.  Spiny neuron model with dopamine-induced bistability (Gruber et al 2003)
These files implement a model of dopaminergic modulation of voltage-gated currents (called kir2 and caL in the original paper). See spinycell.html for details of usage and implementation. For questions about this implementation, contact Ted Carnevale (
649.  Spreading depression model for FHM3 with Nav1.1 mutation (Dahlem et al. 2014)
Familial hemiplegic migraine (FHM) is a rare subtype of migraine with aura. A mutation causing FHM type 3 (FHM3) has been identified in SCN1A encoding the Nav1.1 Na+ channel. This genetic defect affects the inactivation gate. The code describes an extended Hodgkin-Huxley framework with dynamic ion concentrations in a wilde-type and mutant form.
650.  State and location dependence of action potential metabolic cost (Hallermann et al., 2012)
With this model of a layer 5 pyramidal neuron the state and location dependence of the ATP usage and the metabolic efficiency of action potentials can be analyzed. Model parameters were constrained by direct subcellular recordings at dendritic, somatic and axonal compartments.
651.  State dependent drug binding to sodium channels in the dentate gyrus (Thomas & Petrou 2013)
A Markov model of sodium channels was developed that includes drug binding to fast inactivated states. This was incorporated into a model of the dentate gyrus to investigate the effects of anti-epileptic drugs on neuron and network properties.
652.  STD-dependent and independent encoding of Input irregularity as spike rate (Luthman et al. 2011)
"... We use a conductance-based model of a CN neuron to study the effect of the regularity of Purkinje cell spiking on CN neuron activity. We find that increasing the irregularity of Purkinje cell activity accelerates the CN neuron spike rate and that the mechanism of this recoding of input irregularity as output spike rate depends on the number of Purkinje cells converging onto a CN neuron. ..."
653.  STDP and BDNF in CA1 spines (Solinas et al. 2019)
Storing memory traces in the brain is essential for learning and memory formation. Memory traces are created by joint electrical activity in neurons that are interconnected by synapses and allow transferring electrical activity from a sending (presynaptic) to a receiving (postsynaptic) neuron. During learning, neurons that are co-active can tune synapses to become more effective. This process is called synaptic plasticity or long-term potentiation (LTP). Timing-dependent LTP (t-LTP) is a physiologically relevant type of synaptic plasticity that results from repeated sequential firing of action potentials (APs) in pre- and postsynaptic neurons. T-LTP is observed during learning in vivo and is a cellular correlate of memory formation. T-LTP can be elicited by different rhythms of synaptic activity that recruit distinct synaptic growth processes underlying t-LTP. The protein brain-derived neurotrophic factor (BDNF) is released at synapses and mediates synaptic growth in response to specific rhythms of t-LTP stimulation, while other rhythms mediate BDNF-independent t-LTP. Here, we developed a realistic computational model that accounts for our previously published experimental results of BDNF-independent 1:1 t-LTP (pairing of 1 presynaptic with 1 postsynaptic AP) and BDNF-dependent 1:4 t-LTP (pairing of 1 presynaptic with 4 postsynaptic APs). The model explains the magnitude and time course of both t-LTP forms and allows predicting t-LTP properties that result from altered BDNF turnover. Since BDNF levels are decreased in demented patients, understanding the function of BDNF in memory processes is of utmost importance to counteract Alzheimer’s disease.
654.  STDP and oscillations produce phase-locking (Muller et al. 2011)
"... In this note, we investigate a simple mechanism for learning precise LFP-to-spike coupling in feed-forward networks – the reliable, periodic modulation of presynaptic firing rates during oscillations, coupled with spike-timing dependent plasticity. When oscillations are within the biological range (2–150 Hz), firing rates of the inputs change on a timescale highly relevant to spike-timing dependent plasticity (STDP). Through analytic and computational methods, we find points of stable phase-locking for a neuron with plastic input synapses. These points correspond to precise phase-locking behavior in the feed-forward network. The location of these points depends on the oscillation frequency of the inputs, the STDP time constants, and the balance of potentiation and de-potentiation in the STDP rule. ..."
655.  STDP depends on dendritic synapse location (Letzkus et al. 2006)
This model was published in Letzkus, Kampa & Stuart (2006) J Neurosci 26(41):10420-9. The simulation creates several plots showing voltage and NMDA current and conductance changes at different apical dendritic locations in layer 5 pyramidal neurons during STDP induction protocols. Created by B. Kampa (2006).
656.  Stochastic 3D model of neonatal rat spinal motoneuron (Ostroumov 2007)
" ... Although existing models of motoneurons have indicated the distributed role of certain conductances in regulating firing, it is unclear how the spatial distribution of certain currents is ultimately shaping motoneuron output. Thus, it would be helpful to build a bridge between histological and electrophysiological data. The present report is based on the construction of a 3D motoneuron model based on available parameters applicable to the neonatal spinal cord. ..."
657.  Stochastic calcium mechanisms cause dendritic calcium spike variability (Anwar et al. 2013)
" ... In single Purkinje cells, spontaneous and synaptically evoked dendritic calcium bursts come in a variety of shapes with a variable number of spikes. The mechanisms causing this variability have never been investigated thoroughly. In this study, a detailed computational model employing novel simulation routines is applied to identify the roles that stochastic ion channels, spatial arrangements of ion channels and stochastic intracellular calcium have towards producing calcium burst variability. … Our findings suggest that stochastic intracellular calcium mechanisms play a crucial role in dendritic calcium spike generation and are, therefore, an essential consideration in studies of neuronal excitability and plasticity."
658.  Stochastic Hodgkin-Huxley Model: 14x28D Langevin Simulation (Pu and Thomas, 2020).
This model provides a natural 14-dimensional Langevin dynamics for the Hodgkin Huxley system in which each directed edge in the ion channel state transition graph acts as an independent noise source, leading to a 14 dimensional state space (1 dimension for voltage, 5 for potassium and 8 for sodium) and 14 × 28 noise coefficient matrix S. In [Pu and Thomas (2020) Neural Computation] we show that this 14 x 28 dimensional model is pathwise equivalent to the 14 x 11 dimensional Langevin model proposed in [Fox and Lu (1994) Phys Rev E], as well as an 14 x 14 model described in [Orio and Soudry (2012) PLoS One]. Unlike Fox and Lu's model, our construction does not require a matrix root extraction step, and runs significantly faster. Unlike Orio and Soudry's model, each directed edge acts as an independent noise source, which facilitates the application of stochastic shielding methods for even greater simulation speed. For comparison, we provide implementations of the following models: 1. Discrete-state Markov chain model (slow, but provides the "gold standard" model), adapted from [Goldwyn and Shea-Brown (2011) PLoS Comp. Biol.] 2. 14 x 11 Langevin model from [Fox and Lu (1994) Phys. Rev. E]. (We implement versions with three different boundary conditions: open boundaries, reflecting boundaries, and resampling/rejection at the boundaries.) 3. 4 x 3 Langevin model from [Fox (1997) Biophys. J.] 4. 14 x 13 Langevin model from [Goldwyn and Shea (2011) PLoS Comp. Biol.] 5. 14 x 14 Langevin model from [Dangerfield et al (2012) Phys. Rev. E] 6. 14 x 14 Langevin model from [Orio and Soudry (2012) PLoS One] 7. 14 x 28 Langevin model from [Pu and Thomas (2020) Neural Computation] implemented both with and without stochastic shielding 8. 14 x 0 deterministic HH model (also from [Pu and Thomas (2020) Neural Computation], with the full 14 dimensional state space but no noise) The file provides more detailed simulations. To cite the code: Pu, Shusen, and Peter J. Thomas. "Fast and Accurate Langevin Simulations of Stochastic Hodgkin-Huxley Dynamics." Neural Computation 32, 1775–1835 (2020)
659.  Stochastic Ih and Na-channels in pyramidal neuron dendrites (Kole et al 2006)
The hyperpolarization-activated cation current (Ih) plays an important role in regulating neuronal excitability, yet its native single-channel properties in the brain are essentially unknown. Here we use variance-mean analysis to study the properties of single Ih channels in the apical dendrites of cortical layer 5 pyramidal neurons in vitro. ... In contrast to the uniformly distributed single-channel conductance, Ih channel number increases exponentially with distance, reaching densities as high as approximately 550 channels/microm2 at distal dendritic sites. These high channel densities generate significant membrane voltage noise. By incorporating a stochastic model of Ih single-channel gating into a morphologically realistic model of a layer 5 neuron, we show that this channel noise is higher in distal dendritic compartments and increased threefold with a 10-fold increased single-channel conductance (6.8 pS) but constant Ih current density. ... These data suggest that, in the face of high current densities, the small single-channel conductance of Ih is critical for maintaining the fidelity of action potential output. See paper for more and details.
660.  Stochastic ion channels and neuronal morphology (Cannon et al. 2010)
"... We introduce and validate new computational tools that enable efficient generation and simulation of models containing stochastic ion channels distributed across dendritic and axonal membranes. Comparison of five morphologically distinct neuronal cell types reveals that when all simulated neurons contain identical densities of stochastic ion channels, the amplitude of stochastic membrane potential fluctuations differs between cell types and depends on sub-cellular location. ..." The code is downloadable and more information is available at <a href=""></a>
661.  Stochastic layer V pyramidal neuron: interpulse interval coding and noise (Singh & Levy 2017)
Layer V pyramidal neuron with stochastic Na channels. Supports evidence for interpulse interval coding and has very detailed AIS with Nav1.2 and Nav1.6 channels.
662.  Stochastic versions of the Hodgkin-Huxley equations (Goldwyn, Shea-Brown 2011)
A Matlab gui for simulating different channel noise models using the Hodgkin-Huxley equations. Methods provided and reviewed in Goldwyn and Shea-Brown (2011) are: current noise, subunit noise, conductance noise, and Markov chain, as well as the standard deterministic Hodgkin-Huxley model.
663.  Stochastic versions of the Hodgkin-Huxley equations (Goldwyn, Shea-Brown 2011) (pylab)
A pylab version from Alan Leggitt for simulating different channel noise models using the Hodgkin-Huxley equations. Methods provided and reviewed in Goldwyn and Shea-Brown (2011) are: current noise, subunit noise, conductance noise, and Markov chain, as well as the standard deterministic Hodgkin-Huxley model.
664.  Striatal D1R medium spiny neuron, including a subcellular DA cascade (Lindroos et al 2018)
We are investigating how dopaminergic modulation of single channels can be combined to make the D1R possitive MSN more excitable. We also connect multiple channels to substrates of a dopamine induced subcellular cascade to highlight that the classical pathway is too slow to explain DA induced kinetics in the subsecond range (Howe and Dombeck, 2016. doi: 10.1038/nature18942)
665.  Striatal FSI and SPN oscillation model (Chartove et al. 2020)
Our model consists of three interconnected populations of single or double compartment Hodgkin-Huxley neurons: a feedforward network of FSIs, and two networks of SPNs (the D1 receptor-expressing "direct pathway" subnetwork and the D2 receptor-expressing "indirect pathway" subnetwork).
666.  Striatal GABAergic microcircuit, dopamine-modulated cell assemblies (Humphries et al. 2009)
To begin identifying potential dynamically-defined computational elements within the striatum, we constructed a new three-dimensional model of the striatal microcircuit's connectivity, and instantiated this with our dopamine-modulated neuron models of the MSNs and FSIs. A new model of gap junctions between the FSIs was introduced and tuned to experimental data. We introduced a novel multiple spike-train analysis method, and apply this to the outputs of the model to find groups of synchronised neurons at multiple time-scales. We found that, with realistic in vivo background input, small assemblies of synchronised MSNs spontaneously appeared, consistent with experimental observations, and that the number of assemblies and the time-scale of synchronisation was strongly dependent on the simulated concentration of dopamine. We also showed that feed-forward inhibition from the FSIs counter-intuitively increases the firing rate of the MSNs.
667.  Striatal GABAergic microcircuit, spatial scales of dynamics (Humphries et al, 2010)
The main thrust of this paper was the development of the 3D anatomical network of the striatum's GABAergic microcircuit. We grew dendrite and axon models for the MSNs and FSIs and extracted probabilities for the presence of these neurites as a function of distance from the soma. From these, we found the probabilities of intersection between the neurites of two neurons given their inter-somatic distance, and used these to construct three-dimensional striatal networks. These networks were examined for their predictions for the distributions of the numbers and distances of connections for all the connections in the microcircuit. We then combined the neuron models from a previous model (Humphries et al, 2009; ModelDB ID: 128874) with the new anatomical model. We used this new complete striatal model to examine the impact of the anatomical network on the firing properties of the MSN and FSI populations, and to study the influence of all the inputs to one MSN within the network.
668.  Striatal NN model of MSNs and FSIs investigated effects of dopamine depletion (Damodaran et al 2015)
This study investigates the mechanisms that are affected in the striatal network after dopamine depletion and identifies potential therapeutic targets to restore normal activity.
669.  Striatal Output Neuron (Mahon, Deniau, Charpier, Delord 2000)
Striatal output neurons (SONs) integrate glutamatergic synaptic inputs originating from the cerebral cortex. In vivo electrophysiological data have shown that a prior depolarization of SONs induced a short-term (1 sec)increase in their membrane excitability, which facilitated the ability of corticostriatal synaptic potentials to induce firing. Here we propose, using a computational model of SONs, that the use-dependent, short-term increase in the responsiveness of SONs mainly results from the slow kinetics of a voltage-dependent, slowly inactivating potassium A-current. This mechanism confers on SONs a form of intrinsic short-term memory that optimizes the synaptic input–output relationship as a function of their past activation.
670.  Striatal Spiny Projection Neuron, inhibition enhances spatial specificity (Dorman et al 2018)
We use a computational model of a striatal spiny projection neuron to investigate dendritic spine calcium dynamics in response to spatiotemporal patterns of synaptic inputs. We show that spine calcium elevation is stimulus-specific, with supralinear calcium elevation in cooperatively stimulated spines. Intermediate calcium elevation occurs in neighboring non-stimulated dendritic spines, predicting heterosynaptic effects. Inhibitory synaptic inputs enhance the difference between peak calcium in stimulated spines, and peak calcium in non-stimulated spines, thereby enhancing stimulus specificity.
671.  Superior paraolivary nucleus neuron (Kopp-Scheinpflug et al. 2011)
This is a model of neurons in the brainstem superior paraolivary nucleus (SPN), which produce very salient offset firing during sound stimulation. Rebound offset firing is triggered by IPSPs coming from the medial nucleus of the trapezoid body (MNTB). This model shows that AP firing can emerge from inhibition through integration of large IPSPs, driven by an extremely negative chloride reversal potential, combined with a large hyperpolarization- activated non-specific cationic current (IH), with a secondary contribution from a T-type calcium conductance (ITCa). As a result, tiny gaps in sound stimuli of just 3-4ms can elicit reliable APs that signal such brief offsets.
672.  Sympathetic neuron (Wheeler et al 2004)
This study shows how synaptic convergence and plasticity can interact to generate synaptic gain in autonomic ganglia and thereby enhance homeostatic control. Using a conductance-based computational model of an idealized sympathetic neuron, we simulated the postganglionic response to noisy patterns of presynaptic activity and found that a threefold amplification in postsynaptic spike output can arise in ganglia, depending on the number and strength of nicotinic synapses, the presynaptic firing rate, the extent of presynaptic facilitation, and the expression of muscarinic and peptidergic excitation. See references for details.
673.  Sympathetic Preganglionic Neurone (Briant et al. 2014)
A model of a sympathetic preganglionic neurone of muscle vasoconstrictor-type.
674.  Synaptic gating at axonal branches, and sharp-wave ripples with replay (Vladimirov et al. 2013)
The computational model of in vivo sharp-wave ripples with place cell replay. Excitatory post-synaptic potentials at dendrites gate antidromic spikes arriving from the axonal collateral, and thus determine when the soma and the main axon fire. The model allows synchronous replay of pyramidal cells during sharp-wave ripple event, and the replay is possible in both forward and reverse directions.
675.  Synaptic integration by MEC neurons (Justus et al. 2017)
Pyramidal cells, stellate cells and fast-spiking interneurons receive running speed dependent glutamatergic input from septo-entorhinal projections. These models simulate the integration of this input by the different MEC celltypes.
676.  Synaptic integration in a model of granule cells (Gabbiani et al 1994)
We have developed a compartmental model of a turtle cerebellar granule cell consisting of 13 compartments that represent the soma and 4 dendrites. We used this model to investigate the synaptic integration of mossy fiber inputs in granule cells. See reference or abstract at PubMed link below for more information.
677.  Synaptic integration in tuft dendrites of layer 5 pyramidal neurons (Larkum et al. 2009)
Simulations used in the paper. Voltage responses to current injections in different tuft locations; NMDA and calcium spike generation. Summation of multiple input distribution.
678.  Synchronization by D4 dopamine receptor-mediated phospholipid methylation (Kuznetsova, Deth 2008)
"We describe a new molecular mechanism of dopamine-induced membrane protein modulation that can tune neuronal oscillation frequency to attention related gamma rhythm. This mechanism is based on the unique ability of D4 dopamine receptors (D4R) to carry out phospholipid methylation (PLM) that may affect the kinetics of ion channels. We show that by deceasing the inertia of the delayed rectifier potassium channel, a transition to 40 Hz oscillations can be achieved. ..."
679.  Synchronized oscillations of clock gene expression in the choroid plexus (Myung et al 2018)
Our model simulates synchronized rhythms in the clock gene expression found in the choroid plexus. These synchronized oscillations, primarily mediated by gap junctions, showed interesting relationships between their amplitude, oscillation frequency, and coupling strength (gap junction density) in our experimental data. The model is based on coupled Poincaré oscillators and replicates this phenomenon via a non-zero "twist" in each cell.
680.  Synchrony by synapse location (McTavish et al. 2012)
This model considers synchrony between mitral cells induced via shared granule cell interneurons while taking into account the spatial constraints of the system. In particular, since inhibitory inputs decay passively along the lateral dendrites, this model demonstrates that an optimal arrangement of the inhibitory synapses will be near the cell bodies of the relevant mitral cells.
681.  Synergistic inhibitory action of oxcarbazepine on INa and IK (Huang et al. 2008)
"Oxcarbazepine (OXC), one of the newer anti-epileptic drugs, has been demonstrating its efficacy on wide-spectrum neuropsychiatric disorders. ... With the aid of patch-clamp technology, we first investigated the effects of OXC on ion currents in NG108-15 neuronal cells differentiated with cyclic AMP. We found OXC ... caused a reversible reduction in the amplitude of voltage-gated Na+ current (INa) ... and produce(d) a significant prolongation in the recovery of INa inactivation. ... Moreover, OXC could suppress the amplitude of delayed rectifier K+ current (IK(DR)), with no effect on M-type K+ current (IK(M)). ... Furthermore, the simulations, based on hippocampal pyramidal neurons (Pinsky-Rinzel model) and a network of the Hodgkin-Huxley model, were analysed to investigate the effect of OXC on action potentials. Taken together, our results suggest that the synergistic blocking effects on INa and IK(DR) may contribute to the underlying mechanisms through which OXC affects neuronal function in vivo."
682.  Synthesis of spatial tuning functions from theta cell spike trains (Welday et al., 2011)
A single compartment model reproduces the firing rate maps of place, grid, and boundary cells by receiving inhibitory inputs from theta cells. The theta cell spike trains are modulated by the rat's movement velocity in such a way that phase interference among their burst pattern creates spatial envelope function which simulate the firing rate maps.
683.  T-type Ca current in thalamic neurons (Wang et al 1991)
A model of the transient, low-threshold voltage-dependent (T-type) Ca2+ current is constructed using whole-cell voltage-clamp data from enzymatically isolated rat thalamocortical relay neurons. The T-type Ca2+ current is described according to the Hodgkin-Huxley scheme, using the m3h format, with rate constants determined from the experimental data.
684.  Tag Trigger Consolidation (Clopath and Ziegler et al. 2008)
This model simulates different phases of LTP/D, i.e. the induction or early phase, the setting of synaptic tags, a trigger process for protein synthesis, and a slow transition leading to synaptic consolidation namely the late phase of synaptic plasticity. The model explains a large body of experimental data on synaptic tagging and capture, cross-tagging, and the late phases of LTP and LTD. Moreover, the model accounts for the dependence of LTP and LTD induction on voltage and presynaptic stimulation frequency.
685.  Temperature-Dependent Pyloric Pacemaker Kernel (Caplan JS et al., 2014)
"... Here we demonstrate that biophysical models of channel noise can give rise to two kinds of recently discovered stochastic facilitation effects in a Hodgkin-Huxley-like model of auditory brainstem neurons. The first, known as slope-based stochastic resonance (SBSR), enables phasic neurons to emit action potentials that can encode the slope of inputs that vary slowly relative to key time constants in the model. The second, known as inverse stochastic resonance (ISR), occurs in tonically firing neurons when small levels of noise inhibit tonic firing and replace it with burstlike dynamics. ... our results show that possible associated computational benefits may occur due to channel noise in neurons of the auditory brainstem. ... "
686.  Temporal decorrelation by intrinsic cellular dynamics (Wang et al 2003)
"... Recent investigations in primary visual (V1) cortical neurons have demonstrated that adaptation to prolonged changes in stimulus contrast is mediated in part through intrinsic ionic currents, a Ca2+ activated K+ current (IKCa) and especially a Na+ activated K+ current (IKNa). The present study was designed to test the hypothesis that the activation of adaptation ionic currents may provide a cellular mechanism for temporal decorrelation in V1. A conductance-based neuron model was simulated, which included an IKCa and an IKNa. We show that the model neuron reproduces the adaptive behavior of V1 neurons in response to high contrast inputs. ...". See paper for details and more.
687.  Thalamic interneuron multicompartment model (Zhu et al. 1999)
This is an attempt to recreate a set of simulations originally performed in 1994 under NEURON version 3 and last tested in 1999. When I ran it now it did not behave exactly the same as previously which I suspect is due to some minor mod file changes on my side rather than due to any differences among versions. After playing around with the parameters a little bit I was able to get something that looks generally like a physiological trace in J Neurophysiol, 81:702--711, 1999, fig. 8b top trace. This sad preface is simply offered in order to encourage anyone who is interested in this model to make and post fixes. I'm happy to help out. Simulation by JJ Zhu To run nrnivmodl nrngui.hoc
688.  Thalamic neuron, zebra finch DLM: Integration of pallidal and cortical inputs (Goldberg et al. 2012)
This is a single-compartment model of a zebra finch thalamic relay neuron from nucleus DLM. It is used to explore the interaction between cortex-like glutamatergic input and pallidum-like GABAergic input as they control the spiking output of these neurons.
689.  Thalamic neuron: Modeling rhythmic neuronal activity (Meuth et al. 2005)
The authors use an in vitro cell model of a single acutely isolated thalamic neuron in the NEURON simulation environment to address and discuss questions in an undergraduate course. Topics covered include passive electrical properties, composition of action potentials, trains of action potentials, multicompartment modeling, and research topics. The paper includes detailed instructions on how to run the simulations in the appendix.
690.  Thalamic reticular neurons: the role of Ca currents (Destexhe et al 1996)
The experiments and modeling reported in this paper show how intrinsic bursting properties of RE cells may be explained by dendritic calcium currents.
691.  Thalamic transformation of pallidal input (Hadipour-Niktarash 2006)
"In Parkinson’s disease, neurons of the internal segment of the globus pallidus (GPi) display the low-frequency tremor-related oscillations. These oscillatory activities are transmitted to the thalamic relay nuclei. Computer models of the interacting thalamocortical (TC) and thalamic reticular (RE) neurons were used to explore how the TC-RE network processes the low-frequency oscillations of the GPi neurons. ..."
692.  Thalamocortical loop with delay for investigation of absence epilepsy (Liu et al 2019)
Conductance based network model of one thalamic reticular neuron, one thalamic pyramidal neuron and one cortical pyramidal neuron. Used to show that large delay in the corticothalamic connection can lead to multistability.
693.  Thalamocortical Relay cell under current clamp in high-conductance state (Zeldenrust et al 2018)
Mammalian thalamocortical relay (TCR) neurons switch their firing activity between a tonic spiking and a bursting regime. In a combined experimental and computational study, we investigated the features in the input signal that single spikes and bursts in the output spike train represent and how this code is influenced by the membrane voltage state of the neuron. Identical frozen Gaussian noise current traces were injected into TCR neurons in rat brain slices to adjust, fine-tune and validate a three-compartment TCR model cell (Destexhe et al. 1998, accession number 279). Three currents were added: an h-current (Destexhe et al. 1993,1996, accession number 3343), a high-threshold calcium current and a calcium- activated potassium current (Huguenard & McCormick 1994, accession number 3808). The information content carried by the various types of events in the signal as well as by the whole signal was calculated. Bursts phase-lock to and transfer information at lower frequencies than single spikes. On depolarization the neuron transits smoothly from the predominantly bursting regime to a spiking regime, in which it is more sensitive to high-frequency fluctuations. Finally, the model was used to in the more realistic “high-conductance state” (Destexhe et al. 2001, accession number 8115), while being stimulated with a Poisson input (Brette et al. 2007, Vogels & Abbott 2005, accession number 83319), where fluctuations are caused by (synaptic) conductance changes instead of current injection. Under “standard” conditions bursts are difficult to initiate, given the high degree of inactivation of the T-type calcium current. Strong and/or precisely timed inhibitory currents were able to remove this inactivation.
694.  Thalamocortical relay neuron models constrained by experiment and optimization (Iavarone et al 2019)
695.  The APP in C-terminal domain alters CA1 neuron firing (Pousinha et al 2019)
"The amyloid precursor protein (APP) is central to AD pathogenesis and we recently showed that its intracellular domain (AICD) could modify synaptic signal integration. We now hypothezise that AICD modifies neuron firing activity, thus contributing to the disruption of memory processes. Using cellular, electrophysiological and behavioural techniques, we showed that pathological AICD levels weakens CA1 neuron firing activity through a gene transcription-dependent mechanism. Furthermore, increased AICD production in hippocampal neurons modifies oscillatory activity, specifically in the gamma frequency range, and disrupts spatial memory task. Collectively, our data suggest that AICD pathological levels, observed in AD mouse models and in human patients, might contribute to progressive neuron homeostatic failure, driving the shift from normal ageing to AD."
696.  The basis of sharp spike onset in standard biophysical models (Telenczuk et al 2017)
"In most vertebrate neurons, spikes initiate in the axonal initial segment (AIS). When recorded in the soma, they have a surprisingly sharp onset, as if sodium (Na) channels opened abruptly. The main view stipulates that spikes initiate in a conventional manner at the distal end of the AIS, then progressively sharpen as they backpropagate to the soma. We examined the biophysical models used to substantiate this view, and we found that spikes do not initiate through a local axonal current loop that propagates along the axon, but through a global current loop encompassing the AIS and soma, which forms an electrical dipole. Therefore, the phenomenon is not adequately modeled as the backpropagation of an electrical wave along the axon, since the wavelength would be as large as the entire system. Instead, in these models, we found that spike initiation rather follows the critical resistive coupling model proposed recently, where the Na current entering the AIS is matched by the axial resistive current flowing to the soma. ..."
697.  The cannula artifact (Chandler & Hodgkin 1965)
Chandler and Hodgkin 1965 describes how using a high impedance electrode can lead to squid axon recordings that appear to overshoot the sodium reversal potential, thus resolving controversial recordings at the time.
698.  The dynamics underlying pseudo-plateau bursting in a pituitary cell model (Teka et al. 2011)
" ... pseudo-plateau bursts, are unlike bursts studied mathematically in neurons (plateau bursting) and the standard fast-slow analysis used for plateau bursting is of limited use. Using an alternative fast-slow analysis, with one fast and two slow variables, we show that pseudo-plateau bursting is a canard-induced mixed mode oscillation. ..." See paper for other results.
699.  The neocortical microcircuit collaboration portal (Markram et al. 2015)
"This portal provides an online public resource of the Blue Brain Project's first release of a digital reconstruction of the microcircuitry of juvenile Rat somatosensory cortex, access to experimental data sets used in the reconstruction, and the resulting models."
700.  The relationship between two fast/slow analysis techniques for bursting oscill. (Teka et al. 2012)
"Bursting oscillations in excitable systems reflect multi-timescale dynamics. These oscillations have often been studied in mathematical models by splitting the equations into fast and slow subsystems. Typically, one treats the slow variables as parameters of the fast subsystem and studies the bifurcation structure of this subsystem. This has key features such as a z-curve (stationary branch) and a Hopf bifurcation that gives rise to a branch of periodic spiking solutions. In models of bursting in pituitary cells, we have recently used a different approach that focuses on the dynamics of the slow subsystem. Characteristic features of this approach are folded node singularities and a critical manifold. … We find that the z-curve and Hopf bifurcation of the twofast/ one-slow decomposition are closely related to the voltage nullcline and folded node singularity of the one-fast/two-slow decomposition, respectively. They become identical in the double singular limit in which voltage is infinitely fast and calcium is infinitely slow."
701.  The role of ATP-sensitive potassium channels in a hippocampal neuron (Huang et al. 2007)
"Hyperglycemia-related neuronal excitability and epileptic seizures are not uncommon in clinical practice. However, their underlying mechanism remains elusive. ATP-sensitive K(+) (K(ATP)) channels are found in many excitable cells, including cardiac myocytes, pancreatic beta cells, and neurons. These channels provide a link between the electrical activity of cell membranes and cellular metabolism. We investigated the effects of higher extracellular glucose on hippocampal K(ATP) channel activities and neuronal excitability. The cell-attached patch-clamp configuration on cultured hippocampal cells and a novel multielectrode recording system on hippocampal slices were employed. In addition, a simulation modeling hippocampal CA3 pyramidal neurons (Pinsky-Rinzel model) was analyzed to investigate the role of K(ATP) channels in the firing of simulated action potentials. ..."
702.  The role of glutamate in neuronal ion homeostasis: spreading depolarization (Hubel et al 2017)
This model includes ion concentration dynamics (sodium, potassium, chloride) inside and outside the neuron, the exchange of ions with glia and blood vessels, volume dynamics of neuron, glia, and extracellular space, glutamate homeostasis involving release by neuron and uptake by both neuron and glia. Spreading depolarization is used as a case study.
703.  The subcellular distribution of T-type Ca2+ channels in LGN interneurons (Allken et al. 2014)
" ...To study the relationship between the (Ca2+ channel) T-distribution and several (LGN interneuron) IN response properties, we here run a series of simulations where we vary the T-distribution in a multicompartmental IN model with a realistic morphology. We find that the somatic response to somatic current injection is facilitated by a high T-channel density in the soma-region. Conversely, a high T-channel density in the distal dendritic region is found to facilitate dendritic signalling in both the outward direction (increases the response in distal dendrites to somatic input) and the inward direction (the soma responds stronger to distal synaptic input). ..."
704.  The ventricular AP and effects of the isoproterenol-induced cardiac hypertrophy (Sengul et al 2020)
This model reproduces Action Potential (AP) of Rat Ventricular Myocytes according to the experimental AP and Voltage Clamp recordings.
705.  Theoretical principles of DBS induced synaptic suppression (Farokhniaee & McIntyre 2019)
"Deep brain stimulation (DBS) is a successful clinical therapy for a wide range of neurological disorders; however, the physiological mechanisms of DBS remain unresolved. While many different hypotheses currently exist, our analyses suggest that high frequency (~100?Hz) stimulation-induced synaptic suppression represents the most basic concept that can be directly reconciled with experimental recordings of spiking activity in neurons that are being driven by DBS inputs. Objective The goal of this project was to develop a simple model system to characterize the excitatory post-synaptic currents (EPSCs) and action potential signaling generated in a neuron that is strongly connected to pre-synaptic glutamatergic inputs that are being directly activated by DBS. Methods We used the Tsodyks-Markram (TM) phenomenological synapse model to represent depressing, facilitating, and pseudo-linear synapses driven by DBS over a wide range of stimulation frequencies. The EPSCs were then used as inputs to a leaky integrate-and-fire neuron model and we measured the DBS-triggered post-synaptic spiking activity. Results Synaptic suppression was a robust feature of high frequency stimulation, independent of the synapse type. As such, the TM equations were used to define alternative DBS pulsing strategies that maximized synaptic suppression with the minimum number of stimuli. ..."
706.  Theoretical reconstrucion of field potentials and dendrodendritic synaptic...(Rall & Shepherd 1968)
This was the first application of compartmental modeling using the Rall approach to brain neurons. It combined multicompartmental representation of a mitral cell and a granule cell with the first Hodgkin-Huxley-like action potential to model antidromic activation of the mitral cell, followed by synaptic excitation of the granule cell and synaptic inhibition of the mitral cell. Combined with reconstruction of the field potentials generated around these neurons, and detailed comparisons with single cell recordings, it led to prediction of dendrodendritic interactions mediating self and lateral inhibition of the mitral cells by the granule cells. It has been regarded as the first computational model of a brain microcircuit (see also Shepherd and Brayton, 1979). Recreation of the model is pending.
707.  Theory of arachnid prey localization (Sturzl et al. 2000)
"Sand scorpions and many other arachnids locate their prey through highly sensitive slit sensilla at the tips (tarsi) of their eight legs. This sensor array responds to vibrations with stimulus-locked action potentials encoding the target direction. We present a neuronal model to account for stimulus angle determination using a population of second-order neurons, each receiving excitatory input from one tarsus and inhibition from a triad opposite to it. ..."
708.  Theory of sequence memory in neocortex (Hawkins & Ahmad 2016)
"... First we show that a neuron with several thousand synapses segregated on active dendrites can recognize hundreds of independent patterns of cellular activity even in the presence of large amounts of noise and pattern variation. We then propose a neuron model where patterns detected on proximal dendrites lead to action potentials, defining the classic receptive field of the neuron, and patterns detected on basal and apical dendrites act as predictions by slightly depolarizing the neuron without generating an action potential. By this mechanism, a neuron can predict its activation in hundreds of independent contexts. We then present a network model based on neurons with these properties that learns time-based sequences. ..."
709.  Theta phase precession in a model CA3 place cell (Baker and Olds 2007)
"... The present study concerns a neurobiologically based computational model of the emergence of theta phase precession in which the responses of a single model CA3 pyramidal cell are examined in the context of stimulation by realistic afferent spike trains including those of place cells in entorhinal cortex, dentate gyrus, and other CA3 pyramidal cells. Spike-timing dependent plasticity in the model CA3 pyramidal cell leads to a spatially correlated associational synaptic drive that subsequently creates a spatially asymmetric expansion of the model cell’s place field. ... Through selective manipulations of the model it is possible to decompose theta phase precession in CA3 into the separate contributing factors of inheritance from upstream afferents in the dentate gyrus and entorhinal cortex, the interaction of synaptically controlled increasing afferent drive with phasic inhibition, and the theta phase difference between dentate gyrus granule cell and CA3 pyramidal cell activity."
710.  Tonic activation of extrasynaptic NMDA-R promotes bistability (Gall & Dupont 2020)
Our theoretical model provides a simple description of neuronal electrical activity that takes into account the tonic activity of extrasynaptic NMDA receptors and a cytosolic calcium compartment. We show that calcium influx mediated by the tonic activity of NMDA-R can be coupled directly to the activation of calcium-activated potassium channels, resulting in an overall inhibitory effect on neuronal excitability. Furthermore, the presence of tonic NMDA-R activity promotes bistability in electrical activity by dramatically increasing the stimulus interval where both a stable steady state and repetitive firing can coexist. These results could provide an intrinsic mechanism for the constitution of memory traces in neuronal circuits.
711.  Tonic firing in substantia gelatinosa neurons (Melnick et al 2004)
Ionic conductances underlying excitability in tonically firing neurons (TFNs) from substantia gelatinosa (SG) were studied by the patch-clamp method in rat spinal cord slices. ... Suppression of Ca2+ and KCA currents ... did not abolish the basic pattern of tonic firing, indicating that it was generated by voltage-gated Na+ and K+ currents. ... on the basis of present data, we created a model of TFN and showed that Na+ and KDR currents are sufficient to generate a basic pattern of tonic firing. It is concluded that the balanced contribution of all ionic conductances described here is important for generation and modulation of tonic firing in SG neurons. See paper for more and details.
712.  Tonic neuron in spinal lamina I: prolongation of subthreshold depol. (Prescott and De Koninck 2005)
Model demonstrates mechanism whereby two kinetically distinct inward currents act synergistically to prolong subthreshold depolarization. The important currents are a persistent Na current (with fast kinetics) and a persistent Ca current (with slower kinetics). Model also includes a slow K current and transient Ca current, in addition to standard HH currents. Model parameters are set to values used in Fig. 8A. Simulation shows prolonged depolarizations in response to two brief stimuli.
713.  Touch Sensory Cells (T Cells) of the Leech (Cataldo et al. 2004) (Scuri et al. 2007)
Bursts of spikes in leech T cells produce an AHP, which results from activation of a Na+/K+ pump and a Ca2+-dependent K+ current. Activity-dependent increases in the AHP are believed to induce conduction block of spikes in several regions of the neuron, which in turn, may decrease presynaptic invasion of spikes and thereby decrease transmitter release. To explore this possibility, we used the neurosimulator SNNAP to develop a multi-compartmental model of the T cell. Each compartment was modeled as an equivalent electrical circuit, in which some currents were regulated by intracellular Ca2+ and Na+. The membrane model consisted of a membrane capacitance (Cm), for which we used the value 1 uF/cm2, in parallel with two inward currents (Na+ and Ca2+), two K+ currents, a leak current and pump current. The model incorporated empirical data that describe the geometry of the cell and activity-dependent changes of the AHP (see paper for details). Simulations indicated that at some branching points, activity-dependent increases of the AHP reduced the number of spikes transmitted from the minor receptive field to the soma and beyond. These results suggest that the AHP can regulate spike conduction within the presynaptic arborizations of the cell and could in principle contribute to the synaptic depression that is correlated with increases in the AHP.
714.  Transfer properties of Neuronal Dendrites (Korogod et al 1998)
The somatopetal current transfer was studied in mathematical models of a reconstructed brainstem motoneuron with tonically activated excitatory synaptic inputs uniformly distributed over the dendritic arborization. See paper and below readme.txt for more information.
715.  TRPM8-dependent dynamic response in cold thermoreceptors (Olivares et al. 2015)
This model reproduces the dynamic response of cold thermoreceptors, transiently changing the firing rate upon heating or cooling. It also displays the 'static' or adapted firing patterns observed in these receptors.
716.  TTX-R Na+ current effect on cell response (Herzog et al 2001)
"Small dorsal root ganglion (DRG) neurons, which include nociceptors, express multiple voltage-gated sodium currents. In addition to a classical fast inactivating tetrodotoxin-sensitive (TTX-S) sodium current, many of these cells express a TTX-resistant (TTX-R) sodium current that activates near -70 mV and is persistent at negative potentials. To investigate the possible contributions of this TTX-R persistent (TTX-RP) current to neuronal excitability, we carried out computer simulations using the Neuron program with TTX-S and -RP currents, fit by the Hodgkin-Huxley model, that closely matched the currents recorded from small DRG neurons. ..." See paper for more and details.
717.  TTX-R Na+ current effect on cell response (Herzog et al 2001) (MATLAB)
"Small dorsal root ganglion (DRG) neurons, which include nociceptors, express multiple voltage-gated sodium currents. In addition to a classical fast inactivating tetrodotoxin-sensitive (TTX-S) sodium current, many of these cells express a TTX-resistant (TTX-R) sodium current that activates near -70 mV and is persistent at negative potentials. To investigate the possible contributions of this TTX-R persistent (TTX-RP) current to neuronal excitability, we carried out computer simulations using the Neuron program with TTX-S and -RP currents, fit by the Hodgkin-Huxley model, that closely matched the currents recorded from small DRG neurons. ..." See paper for more and details.
718.  Two forms of synaptic depression by neuromodulation of presynaptic Ca2+ channels (Burke et al 2018)
"... To determine whether the different biophysical mechanisms of CaV modulation predicted by OFA (Optical Fluctuation Analysis) are sufficient to explain the differing effects of D1Rs and GABA B Rs on STP, we developed a reduced synaptic model..."
719.  Understanding how fast activating K+ channels promote bursting in pituitary cells (Vo et al 2014)
"... Experimental observations have shown ... that fast-activating voltage- and calcium-dependent potassium (BK) current tends to promote bursting in pituitary cells. This burst promoting effect requires fast activation of the BK current, otherwise it is inhibitory to bursting. In this work, we analyze a pituitary cell model in order to answer the question of why the BK activation must be fast to promote bursting. ..."
720.  Using Strahler`s analysis to reduce realistic models (Marasco et al, 2013)
Building on our previous work (Marasco et al., (2012)), we present a general reduction method based on Strahler's analysis of neuron morphologies. We show that, without any fitting or tuning procedures, it is possible to map any morphologically and biophysically accurate neuron model into an equivalent reduced version. Using this method for Purkinje cells, we demonstrate how run times can be reduced up to 200-fold, while accurately taking into account the effects of arbitrarily located and activated synaptic inputs.
721.  Ventricular cell model (Guinea-pig-type) (Luo, Rudy 1991, +11 other papers!) (C++)
A mathematical model of the membrane action potential of the mammalian ventricular cell is introduced. The model is based, whenever possible, on recent single-cell and single-channel data and incorporates the possibility of changing extracellular potassium concentration [K]o. ... The results are consistent with recent experimental observations, and the model simulations relate these phenomena to the underlying ionic channel kinetics. See paper for more and details.
722.  Ventricular cell model (Luo Rudy dynamic model) (Luo Rudy 1994) used in (Wang et al 2006) (XPP)
A mathematical model of the membrane action potential of the mammalian ventricular cell introduced in Luo, Rudy 1991 and used in Wang et al 2006 is made available here in XPP. The model is based, whenever possible, on recent single-cell and single-channel data and incorporates the possibility of changing extracellular potassium concentration [K]o. ... The results are consistent with recent experimental observations, and the model simulations relate these phenomena to the underlying ionic channel kinetics. See papers for more and details.
723.  Visual Cortex Neurons: Dendritic computations (Archie, Mel 2000)
Neuron and C program files from Archie, K.A. and Mel, B.W. A model of intradendritic computation of binocular disparity. Nature Neuroscience 3:54-63, 2000 The original files for this model are located at the web site
724.  Visual Cortex Neurons: Dendritic study (Anderson et al 1999)
Neuron mod and hoc files for the paper: Anderson, J.C. Binzegger, T., Kahana, O., Segev, I., and Martin, K.A.C Dendritic asymmetry cannot account for directional responses in visual cortex. Nature Neuroscience 2:820:824, 1999
725.  Visual physiology of the layer 4 cortical circuit in silico (Arkhipov et al 2018)
"Despite advances in experimental techniques and accumulation of large datasets concerning the composition and properties of the cortex, quantitative modeling of cortical circuits under in-vivo-like conditions remains challenging. Here we report and publicly release a biophysically detailed circuit model of layer 4 in the mouse primary visual cortex, receiving thalamo- cortical visual inputs. The 45,000-neuron model was subjected to a battery of visual stimuli, and results were compared to published work and new in vivo experiments. ..."
726.  Voltage and light-sensitive Channelrhodopsin-2 model (ChR2) (Williams et al. 2013)
" ... Focusing on one of the most widely used ChR2 mutants (H134R) with enhanced current, we collected a comprehensive experimental data set of the response of this ion channel to different irradiances and voltages, and used these data to develop a model of ChR2 with empirically-derived voltage- and irradiance- dependence, where parameters were fine-tuned via simulated annealing optimization. This ChR2 model offers: 1) accurate inward rectification in the current-voltage response across irradiances; 2) empirically-derived voltage- and light-dependent kinetics (activation, deactivation and recovery from inactivation); and 3) accurate amplitude and morphology of the response across voltage and irradiance settings. Temperature-scaling factors (Q10) were derived and model kinetics was adjusted to physiological temperatures. ... "
727.  Voltage attenuation in CA1 pyramidal neuron dendrites (Golding et al 2005)
Voltage attenuation in the apical dendritic field of CA1 pyramidal neurons is particularly strong for epsps spreading toward the soma. High cytoplasmic resistivity and high membrane (leak) conductance appear to be the major determinants of voltage attenuation over most of the apical field, but H current may be responsible for as much as half of the attenuation of distal apical epsps.
728.  Voltage imaging calibration in tuft dendrites of mitral cells (Djurisic et al 2004)
A detailed morphology of a tuft is provided in a reconstruction of a mitral cell that was used to place simulated estimates on for the calibration of EPSPs as recorded in voltage imaging in the real cells (estimated to be within +12% to -18% of the actual amplitude).
729.  Voltage- and Branch-specific Climbing Fiber Responses in Purkinje Cells (Zang et al 2018)
"Climbing fibers (CFs) provide instructive signals driving cerebellar learning, but mechanisms causing the variable CF responses in Purkinje cells (PCs) are not fully understood. Using a new experimentally validated PC model, we unveil the ionic mechanisms underlying CF-evoked distinct spike waveforms on different parts of the PC. We demonstrate that voltage can gate both the amplitude and the spatial range of CF-evoked Ca2+ influx by the availability of K+ currents. ... The voltage- and branch-specific CF responses can increase dendritic computational capacity and enable PCs to actively integrate CF signals."
730.  Vomeronasal sensory neuron (Shimazaki et al 2006)
NEURON model files from the papers: Shimazaki et al, Chem. Senses, epub ahead of print (2006) Electrophysiological properties and modeling of murine vomeronasal sensory neurons in acute slice preparations. The model reproduces quantitatively the experimentally observed firing rates of these neurons under a wide range of input currents.
731.  VTA dopamine neuron (Tarfa, Evans, and Khaliq 2017)
In our model of a midbrain VTA dopamine neuron, we show that the decay kinetics of the A-type potassium current can control the timing of rebound action potentials.
732.  VTA neurons: Morphofunctional alterations in acute opiates withdrawal (Enrico et al. 2016)
" ... Here we present a biophysical model of a DA VTA neuron based on 3D morphological reconstruction and electrophysiological data, showing how opiates withdrawal-driven morphological and electrophysiological changes could affect the firing rate and discharge pattern...."
733.  Wang-Buzsaki Interneuron (Talathi et al., 2010)
The submitted code provides the relevant C++ files, matlabfiles and the data files essential to reproduce the figures in the JCNS paper titled Control of neural synchrony using channelrhodopsin-2: A computational study.
734.  Zonisamide-induced inhibition of the firing of APs in hippocampal neurons (Huang et al. 2007)
Zonisamide (ZNS), a synthetic benzisoxazole derivative, has been used as an alternative choice in the treatment of epilepsy with a better efficacy and safety profile. However, little is known regarding the mechanism of ZNS actions on ion currents in neurons. We thus investigated its effect on ion currents in differentiated hippocampal 19-7 cells. The ZNS (30 uM) reversibly increased the amplitude of K+ outward currents and paxilline (1 uM) was effective in suppressing ZNS-induced increase of K+ outward currents. In inside-out configuration, ZNS (30 uM) applied to the intracellular face of the membrane did not alter single-channel conductance; however, it did enhance the activity of large-conductance Ca2+-activated K+ (BKCa) channels primarily by decreasing mean closed time. The EC50 value for ZNS-stimulated BKCa channels was 34 uM. This drug caused a left shift in the activation curve of BKCa channels with no change in the gating charge of these channels. ZNS at a concentration greater than 100 uM also reduced the amplitude of A-type K+ current in these cells. A simulation modeling based on hippocampal CA3 pyramidal neurons (Pinsky-Rinzel model) was also analyzed to investigate the inhibitory effect of ZNS on the firing of simulated action potentials. Taken together, this study suggests that in hippocampal neurons, during the exposure to ZNS, the ZNS-mediated effects on BKCa channels and IA could be one of the ionic mechanisms through which it affects neuronal excitability.

Re-display model names without descriptions