Models that contain the Neurotransmitter : Amino Acids

Re-display model names without descriptions
    Models   Description
1.  3D olfactory bulb: operators (Migliore et al, 2015)
"... Using a 3D model of mitral and granule cell interactions supported by experimental findings, combined with a matrix-based representation of glomerular operations, we identify the mechanisms for forming one or more glomerular units in response to a given odor, how and to what extent the glomerular units interfere or interact with each other during learning, their computational role within the olfactory bulb microcircuit, and how their actions can be formalized into a theoretical framework in which the olfactory bulb can be considered to contain "odor operators" unique to each individual. ..."
2.  A computational model of systems memory consolidation and reconsolidation (Helfer & Shultz 2019)
A neural-network framework for modeling systems memory consolidation and reconsolidation.
3.  A cortico-cerebello-thalamo-cortical loop model under essential tremor (Zhang & Santaniello 2019)
We investigated the origins of oscillations under essential tremor (ET) by building a computational model of the cortico-cerebello-thalamo-cortical loop. It showed that an alteration of amplitudes and decay times of the GABAergic currents to the dentate nucleus can facilitate sustained oscillatory activity at tremor frequency throughout the network as well as a robust bursting activity in the thalamus, which is consistent with observations of thalamic tremor cells in ET patients. Tremor-related oscillations initiated in small neural populations and spread to a larger network as the synaptic dysfunction increased, while thalamic high-frequency stimulation suppressed tremor-related activity in thalamus but increased the oscillation frequency in the olivocerebellar loop.
4.  A fast model of voltage-dependent NMDA Receptors (Moradi et al. 2013)
These are two or triple-exponential models of the voltage-dependent NMDA receptors. Conductance of these receptors increase voltage-dependently with a "Hodgkin and Huxley-type" gating style that is also depending on glutamate-binding. Time course of the gating of these receptors in response to glutamate are also changing voltage-dependently. Temperature sensitivity and desensitization of these receptor are also taken into account. Three previous kinetic models that are able to simulate the voltage-dependence of the NMDARs are also imported to the NMODL. These models are not temperature sensitive. These models are compatible with the "event delivery system" of NEURON. Parameters that are reported in our paper are applicable to CA1 pyramidal cell dendrites.
5.  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.
6.  A kinetic model unifying presynaptic short-term facilitation and depression (Lee et al. 2009)
"... Here, we propose a unified theory of synaptic short-term plasticity based on realistic yet tractable and testable model descriptions of the underlying intracellular biochemical processes. Analysis of the model equations leads to a closed-form solution of the resonance frequency, a function of several critical biophysical parameters, as the single key indicator of the propensity for synaptic facilitation or depression under repetitive stimuli. This integrative model is supported by a broad range of transient and frequency response experimental data including those from facilitating, depressing or mixed-mode synapses. ... the model provides the reasons behind the switching behavior between facilitation and depression observed in experiments. ..."
7.  A Layer V CCS type pyramidal cell, inhibitory synapse current conduction (Kubota Y et al., 2015)
A layer V crossed-corticostriatal (CCS) ‘slender untufted’ pyramidal cell model of rat frontal cortex was built using Neurolucida tracing as well as 3D reconstructed dendrites of serial electron micrographs to give the model as authentic morphology as possible.
8.  A mathematical model of a neurovascular unit (Dormanns et al 2015, 2016) (Farrs & David 2011)
Here a lumped parameter numerical model of a neurovascular unit is presented, representing an intercellular communication system based on ion exchange through pumps and channels between neurons, astrocytes, smooth muscle cells, endothelial cells, and the spaces between these cells: the synaptic cleft between the neuron and astrocyte, the perivascular space between the astrocyte and SMC, and the extracellular space surrounding the cells. The model contains various cellular and chemical pathways such as potassium, astrocytic calcium, and nitric oxide. The model is able to simulate neurovascular coupling, the process characterised by an increase in neuronal activity followed by a rapid dilation of local blood vessels and hence increased blood supply providing oxygen and glucose to cells in need.
9.  A Method for Prediction of Receptor Activation in the Simulation of Synapses (Montes et al. 2013)
A machine-learning based method that can accurately predict relevant aspects of the behavior of synapses, such as the activation of synaptic receptors, at very low computational cost. The method is designed to learn patterns and general principles from previous Monte Carlo simulations and to predict synapse behavior from them. The resulting procedure is accurate, automatic and can predict synapse behavior under experimental conditions that are different to the ones used during the learning phase. Since our method efficiently reduces the computational costs, it is suitable for the simulation of the vast number of synapses that occur in the mammalian brain.
10.  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.
11.  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.
12.  A model of neurovascular coupling and the BOLD response (Mathias et al 2017, Kenny et al 2018)
Here a lumped parameter numerical model of a neurovascular unit is presented, representing an intercellular communication system based on ion exchange through pumps and channels between neurons, astrocytes, smooth muscle cells, endothelial cells, and the spaces between these cells: the synaptic cleft between the neuron and astrocyte, the perivascular space between the astrocyte and SMC, and the extracellular space surrounding the cells. The model contains various cellular and chemical pathways such as potassium, astrocytic calcium, and nitric oxide. The model is able to simulate neurovascular coupling, the process characterised by an increase in neuronal activity followed by a rapid dilation of local blood vessels and hence increased blood supply providing oxygen and glucose to cells in need. The model also incorporates the BOLD response.
13.  A model of unitary responses from A/C and PP synapses in CA3 pyramidal cells (Baker et al. 2010)
The model was used to reproduce experimentally determined mean synaptic response characteristics of unitary AMPA and NMDA synaptic stimulations in CA3 pyramidal cells with the objective of inferring the most likely response properties of the corresponding types of synapses. The model is primarily concerned with passive cells, but models of active dendrites are included.
14.  A Moth MGC Model-A HH network with quantitative rate reduction (Buckley & Nowotny 2011)
We provide the model used in Buckley & Nowotny (2011). It consists of a network of Hodgkin Huxley neurons coupled by slow GABA_B synapses which is run alongside a quantitative reduction described in the associated paper.
15.  A multilayer cortical model to study seizure propagation across microdomains (Basu et al. 2015)
A realistic neural network was used to simulate a region of neocortex to obtain extracellular LFPs from ‘virtual micro-electrodes’ and produce test data for comparison with multisite microelectrode recordings. A model was implemented in the GENESIS neurosimulator. A simulated region of cortex was represented by layers 2/3, 5/6 (interneurons and pyramidal cells) and layer 4 stelate cells, spaced at 25 µm in each horizontal direction. Pyramidal cells received AMPA and NMDA inputs from neighboring cells at the basal and apical dendrites. The LFP data was generated by simulating 16-site electrode array with the help of ‘efield’ objects arranged at the predetermined positions with respect to the surface of the simulated network. The LFP for the model is derived from a weighted average of the current sources summed over all cellular compartments. Cell models were taken from from Traub et al. (2005) J Neurophysiol 93(4):2194-232.
16.  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."
17.  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."
18.  A network model of the vertebrate retina (Publio et al. 2009)
In this work, we use a minimal conductance-based model of the ON rod pathways in the vertebrate retina to study the effects of electrical synaptic coupling via gap junctions among rods and among AII amacrine cells on the dynamic range of the retina. The model is also used to study the effects of the maximum conductance of rod hyperpolarization activated current Ih on the dynamic range of the retina, allowing a study of the interrelations between this intrinsic membrane parameter with those two retina connectivity characteristics.
19.  A neural mass model for critical assessment of brain connectivity (Ursino et al 2020)
We use a neural mass model of interconnected regions of interest to simulate reliable neuroelectrical signals in the cortex. In particular, signals simulating mean field potentials were generated assuming two, three or four ROIs, connected via excitatory or by-synaptic inhibitory links. Then we investigated whether bivariate Transfer Entropy (TE) can be used to detect a statistically significant connection from data (as in binary 0/1 networks), and even if connection strength can be quantified (i.e., the occurrence of a linear relationship between TE and connection strength). Results suggest that TE can reliably estimate the strength of connectivity if neural populations work in their linear regions. However, nonlinear phenomena dramatically affect the assessment of connectivity, since they may significantly reduce TE estimation. Software included here allows the simulation of neural mass models with a variable number of ROIs and connections, the estimation of TE using the free package Trentool, and the realization of figures to compare true connectivity with estimated values.
20.  A NN with synaptic depression for testing the effects of connectivity on dynamics (Jacob et al 2019)
Here we used a 10,000 neuron model. The neurons are a mixture of excitatory and inhibitory integrate-and-fire neurons connected with synapses that exhibit synaptic depression. Three different connectivity paradigms were tested to look for spontaneous transition between interictal spiking and seizure: uniform, small-world network, and scale-free. All three model types are included here.
21.  A synapse model for developing somatosensory cortex (Manninen et al 2020)
We developed a model for an L4-L2/3 synapse in somatosensory cortex to study the role of astrocytes in modulation of t-LTD. Our model includes the one-compartmental presynaptic L4 spiny stellate cell, two-compartmental (soma and dendrite) postsynaptic L2/3 pyramidal cell, and one-compartmental fine astrocyte process.
22.  ACnet23 primary auditory cortex model (Beeman et al 2019)
These scripts were used to model a patch of layer 2/3 primary auditory cortex, making use of the the improvements to PGENESIS by Crone, et al. (2019). This single layer model contains a 48 x 48 grid of pyramidal cells (PCs) and a 24 x 24 grid of basket cells (BCs). The reduced PC models have 17 compartments with dimensions and passive properties that were fit to human cortical PC reconstructions. This parallel version of the simulation was used by Beeman, et al. (2019) to understand the effects of inhibition of PCs by BCs on auditory evoked potentials.
23.  Active zone model of Ca2+ secretion coupling (Keller et al. 2015)
"... At a large model synapse, the calyx of Held, transmitter release is controlled by several Ca2+ channels in a "domain overlap" mode, at least in young animals. To study the geometrical constraints of Ca2+ channel placement in domain overlap control of release, we used stochastic MCell modelling, at active zones for which the position of docked vesicles was derived from electron microscopy (EM). ..."
24.  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.
25.  An attractor network model of grid cells and theta-nested gamma oscillations (Pastoll et al 2013)
A two population spiking continuous attractor model of grid cells. This model combines the attractor dynamics with theta-nested gamma oscillatory activity. It reproduces the behavioural response of grid cells (grid fields) in medial entorhinal cortex, while at the same time allowing for nested gamma oscillations of post-synaptic currents.
26.  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.
27.  Analytical modelling of temperature effects on an AMPA-type synapse (Kufel & Wojcik 2018)
This code was used in the construction of the model developed in the paper. It is a modified version of the simulation developed by Postlethwaite et al. 2007 - for details of modifications refer to the main body of Kufel & Wojcik (2018).
28.  Application of a common kinetic formalism for synaptic models (Destexhe et al 1994)
Application to AMPA, NMDA, GABAA, and GABAB receptors is given in a book chapter. The reference paper synthesizes a comprehensive general description of synaptic transmission with Markov kinetic models. This framework is applicable to modeling ion channels, synaptic release, and all receptors. Please see the references for more details. A simple introduction to this method is given in a seperate paper Destexhe et al Neural Comput 6:14-18 , 1994). More information and papers at http://cns.iaf.cnrs-gif.fr/Main.html and through email: Destexhe@iaf.cnrs-gif.fr
29.  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).
30.  Basal ganglia network model of subthalamic deep brain stimulation (Hahn and McIntyre 2010)
Basal ganglia network model of parkinsonian activity and subthalamic deep brain stimulation in non-human primates from the article Instructions are provided in the README.txt file. Contact hahnp@ccf.org if you have any questions about the implementation of the model. Please include "ModelDB - BGnet" in the subject heading.
31.  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.
32.  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. ..."
33.  Biochemically detailed model of LTP and LTD in a cortical spine (Maki-Marttunen et al 2020)
"Signalling pathways leading to post-synaptic plasticity have been examined in many types of experimental studies, but a unified picture on how multiple biochemical pathways collectively shape neocortical plasticity is missing. We built a biochemically detailed model of post-synaptic plasticity describing CaMKII, PKA, and PKC pathways and their contribution to synaptic potentiation or depression. We developed a statistical AMPA-receptor-tetramer model, which permits the estimation of the AMPA-receptor-mediated maximal synaptic conductance based on numbers of GluR1s and GluR2s predicted by the biochemical signalling model. We show that our model reproduces neuromodulator-gated spike-timing-dependent plasticity as observed in the visual cortex and can be fit to data from many cortical areas, uncovering the biochemical contributions of the pathways pinpointed by the underlying experimental studies. Our model explains the dependence of different forms of plasticity on the availability of different proteins and can be used for the study of mental disorder-associated impairments of cortical plasticity."
34.  Biologically Constrained Basal Ganglia model (BCBG model) (Lienard, Girard 2014)
We studied the physiology and function of the basal ganglia through the design of mean-field models of the whole basal ganglia. The parameterizations are optimized with multi-objective evolutionary algorithm to respect best a collection of numerous anatomical data and electrophysiological data. The main outcomes of our study are: • The strength of the GPe to GPi/SNr connection does not support opposed activities in the GPe and GPi/SNr. • STN and MSN target more the GPe than the GPi/SNr. • Selection arises from the structure of the basal ganglia, without properly segregated direct and indirect pathways and without specific inputs from pyramidal tract neurons of the cortex. Selection is enhanced when the projection from GPe to GPi/SNr has a diffuse pattern.
35.  Biophysical modeling of pathological brain states (Sudhakar et al 2019)
"Traumatic brain injuries (TBI) lead to dramatic changes in the surviving brain tissue. Altered ion concentrations, coupled with changes in the expression of membrane-spanning proteins, create a post-TBI brain state that can lead to further neuronal loss caused by secondary excitotoxicity. Several GABA receptor agonists have been tested in the search for neuroprotection immediately after an injury, with paradoxical results. These drugs not only fail to offer neuroprotection, but can also slow down functional recovery after TBI. Here, using computational modeling, we provide a biophysical hypothesis to explain these observations. We show that the accumulation of intracellular chloride ions caused by a transient upregulation of Na+-K+-2Cl- (NKCC1) co-transporters as observed following TBI, causes GABA receptor agonists to lead to excitation and depolarization block, rather than the expected hyperpolarization. The likelihood of prolonged, excitotoxic depolarization block is further exacerbated by the extremely high levels of extracellular potassium seen after TBI. Our modeling results predict that the neuroprotective efficacy of GABA receptor agonists can be substantially enhanced when they are combined with NKCC1 co-transporter inhibitors. This suggests a rational, biophysically principled method for identifying drug combinations for neuroprotection after TBI."
36.  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. ..."
37.  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. ..."
38.  CA1 pyramidal neuron dendritic spine with plasticity (O`Donnell et al. 2011)
Biophysical model of a dendritic spine and adjacent dendrite with synapse. Model parameters adjusted to fit CA3-CA1 Shaffer collateral synapse data from literature. Model includes both electrical and Ca2+ dynamics, including AMPARs, NMDARs, 4 types of CaV channel, and leak conductance. Spine and synapse are plastic according to Ca2+ dependent rule. The aim of the model is to explore the effects of dendritic spine structural plasticity on the rules of synaptic plasticity.
39.  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.
40.  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.
41.  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.
42.  CA1 pyramidal neuron: dendritic spike initiation (Gasparini et al 2004)
NEURON mod files from the paper: Sonia Gasparini, Michele Migliore, and Jeffrey C. Magee On the initiation and propagation of dendritic spikes in CA1 pyramidal neurons, J. Neurosci., J. Neurosci. 24:11046-11056 (2004).
43.  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.
44.  CA1 pyramidal neuron: integration of subthreshold inputs from PP and SC (Migliore 2003)
The model shows how the experimentally observed increase in the dendritic density of Ih and IA could have a major role in constraining the temporal integration window for the main CA1 synaptic inputs.
45.  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.
46.  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.
47.  CA1 pyramidal neuron: rebound spiking (Ascoli et al.2010)
The model demonstrates that CA1 pyramidal neurons support rebound spikes mediated by hyperpolarization-activated inward current (Ih), and normally masked by A-type potassium channels (KA). Partial KA reduction confined to one or few branches of the apical tuft may be sufficient to elicit a local spike following a train of synaptic inhibition. These data suggest that the plastic regulation of KA can provide a dynamic switch to unmask post-inhibitory spiking in CA1 pyramidal neurons, further increasing the signal processing power of the CA1 synaptic microcircuitry.
48.  CA1 pyramidal neuron: schizophrenic behavior (Migliore et al. 2011)
NEURON files from the paper: A modeling study suggesting how a reduction in the context-dependent input on CA1 pyramidal neurons could generate schizophrenic behavior. by M. Migliore, I. De Blasi, D. Tegolo, R. Migliore, Neural Networks,(2011), doi:10.1016/j.neunet.2011.01.001. Starting from the experimentally supported assumption on hippocampal neurons we explore an experimentally testable prediction at the single neuron level. The model shows how and to what extent a pathological hypofunction of a contextdependent distal input on a CA1 neuron can generate hallucinations by altering the normal recall of objects on which the neuron has been previously tuned. The results suggest that a change in the context during the recall phase may cause an occasional but very significant change in the set of active dendrites used for features recognition, leading to a distorted perception of objects.
49.  CA1 pyramidal neuron: signal propagation in oblique dendrites (Migliore et al 2005)
NEURON mod files from the paper: M. Migliore, M. Ferrante, GA Ascoli (2005). The model shows how the back- and forward propagation of action potentials in the oblique dendrites of CA1 neurons could be modulated by local properties such as morphology or active conductances.
50.  CA1 pyramidal neurons: effect of external electric field from power lines (Cavarretta et al. 2014)
The paper discusses the effects induced by an electric field at power lines frequency.
51.  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.
52.  CA3 pyramidal neuron (Safiulina et al. 2010)
In this review some of the recent work carried out in our laboratory concerning the functional role of GABAergic signalling at immature mossy fibres (MF)-CA3 principal cell synapses has been highlighted. To compare the relative strength of CA3 pyramidal cell output in relation to their MF glutamatergic or GABAergic inputs in postnatal development, a realistic model was constructed taking into account the different biophysical properties of these synapses.
53.  Ca3 pyramidal neuron: membrane response near rest (Hemond et al. 2009)
In this paper, the model was used to show how the temporal summation of excitatory inputs in CA3 pyramidal neurons was affected by the presence of Ih in the dendrites in a frequency- and distance-dependent fashion.
54.  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
55.  Calyx of Held, short term plasticity (Yang Z et al. 2009)
This model investigates mechanisms contributing to short term plasticity at the calyx of Held, a giant glutamatergic synapse in the mammalian brainstem auditory system. It is a stochastic version of the model described in: Hennig, M., Postlethwaite, M., Forsythe, I.D. and Graham, B.P. (2007). A biophysical model of short-term plasticity at the calyx of Held. Neurocomputing, 70:1626-1629. This version introduces stochastic vesicle recycling and release. It has been used to investigate the information transmission properties of this synapse, as detailed in: Yang, Z., Hennig, M., Postlethwaite, M., Forsythe, I.D. and Graham, B.P. (2008). Wide-band information transmission at the calyx of Held. Neural Computation, 21(4):991-1018.
56.  Cerebellar granular layer (Maex and De Schutter 1998)
Circuit model of the granular layer representing a one-dimensional array of single-compartmental granule cells (grcs) and Golgi cells (Gocs). This paper examines the effects of feedback inhibition (grc -> Goc -> grc) versus feedforward inhibition (mossy fibre -> Goc -> grc) on synchronization and oscillatory behaviour.
57.  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. ..."
58.  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.
59.  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.
60.  Circadian rhythmicity shapes astrocyte morphology and neuronal function in CA1 (McCauley et al 2020)
Most animal species operate according to a 24-hour period set by the suprachiasmatic nucleus (SCN) of the hypothalamus. The rhythmic activity of the SCN modulates hippocampal-dependent memory, but the molecular and cellular mechanisms that account for this effect remain largely unknown. In McCauley et al. 2020 [1], we identify cell-type specific structural and functional changes that occur with circadian rhythmicity in neurons and astrocytes in hippocampal area CA1. Pyramidal neurons change the surface expression of NMDA receptors. Astrocytes change their proximity clustered excitatory synaptic inputs, ultimately shaping hippocampal-dependent learning in vivo. We identify to synapses. Together, these phenomena alter glutamate clearance, receptor activation and integration of temporally corticosterone as a key contributor to changes in synaptic strength. These findings highlight important mechanisms through which neurons and astrocytes modify the molecular composition and structure of the synaptic environment, contribute to the local storage of information in the hippocampus and alter the temporal dynamics of cognitive processing. [1] "Circadian modulation of neurons and astrocytes controls synaptic plasticity in hippocampal area CA1" by J.P. McCauley, M.A. Petroccione, L.Y. D’Brant, G.C. Todd, N. Affinnih, J.J. Wisnoski, S. Zahid, S. Shree, A.A. Sousa, R.M. De Guzman, R. Migliore, A. Brazhe, R.D. Leapman, A. Khmaladze, A. Semyanov, D.G. Zuloaga, M. Migliore and A. Scimemi. Cell Reports (2020), https://doi.org/10.1016/j.celrep.2020.108255
61.  Coding of stimulus frequency by latency in thalamic networks (Golomb et al 2005)
The paper presents models of the rat vibrissa processing system including the posterior medial (POm) thalamus, ventroposterior medial (VPm) thalamus, and GABAB- mediated feedback inhibition from the reticular thalamic (Rt) nucleus. A clear match between the experimentally measured spike-rates and the numerically calculated rates for the full model occurs when VPm thalamus receives stronger brainstem input and weaker GABAB-mediated inhibition than POm thalamus.
62.  Coincident signals in Olfactory Bulb Granule Cell spines (Aghvami et al 2019)
"In the mammalian olfactory bulb, the inhibitory axonless granule cells (GCs) feature reciprocal synapses that interconnect them with the principal neurons of the bulb, mitral, and tufted cells. These synapses are located within large excitable spines that can generate local action potentials (APs) upon synaptic input (“spine spike”). Moreover, GCs can fire global APs that propagate throughout the dendrite. Strikingly, local postsynaptic Ca2+ entry summates mostly linearly with Ca2+ entry due to coincident global APs generated by glomerular stimulation, although some underlying conductances should be inactivated. We investigated this phenomenon by constructing a compartmental GC model to simulate the pairing of local and global signals as a function of their temporal separation ?t. These simulations yield strongly sublinear summation of spine Ca2+ entry for the case of perfect coincidence ?t = 0 ms. ..."
63.  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.
64.  Competition model of pheromone ratio detection (Zavada et al. 2011)
For some closely related sympatric moth species, recognizing a specific pheromone component concentration ratio is essential for mating success. We propose and test a minimalist competition-based feed-forward neuronal model capable of detecting a certain ratio of pheromone components independently of overall concentration. This model represents an elementary recognition unit for binary mixtures which we propose is entirely contained in the macroglomerular complex (MGC) of the male moth. A set of such units, along with projection neurons (PNs), can provide the input to higher brain centres. We found that (1) accuracy is mainly achieved by maintaining a certain ratio of connection strengths between olfactory receptor neurons (ORN) and local neurons (LN), much less by properties of the interconnections between the competing LNs proper. (2) successful ratio recognition is achieved using latency-to-first-spike in the LN populations which. (3) longer durations of the competition process between LNs did not result in higher recognition accuracy.
65.  Composite spiking network/neural field model of Parkinsons (Kerr et al 2013)
This code implements a composite model of Parkinson's disease (PD). The composite model consists of a leaky integrate-and-fire spiking neuronal network model being driven by output from a neural field model (instead of the more usual white noise drive). Three different sets of parameters were used for the field model: one with basal ganglia parameters based on data from healthy individuals, one based on data from individuals with PD, and one purely thalamocortical model. The aim of this model is to explore how the different dynamical patterns in each each of these field models affects the activity in the network model.
66.  Computational Modelling of TNFalpha Pathway in Parkinson's Disease (Sasidharakurup et al 2019)
"The paper aims developing a computational framework of signaling using the principles of biochemical systems theory as a model for Parkinson’s disease. Several molecular interactions aided by TNFalpha, a proinflammatory cytokine play key roles in mediating glutamate excitotoxicity and neuroinflammation, resulting in neuronal cell death. In this paper, initial concentrations and rate constants were extracted from literature and simulations developed were based on systems of ordinary differential equations following first-order kinetics. In control or healthy conditions, a decrease in TNFalpha and neuronal cell death was predicted in simulations matching data from experiments, whereas in diseased condition, a drastic increase in levels of TNFalpha, glutamate, TNFR1 and ROS were observed similar to experimental data correlating diseased condition to augmented neuronal cell death. The study suggests toxic effects induced by TNFalpha in the substantia nigra may be attributed to Parkinson’s disease conditions."
67.  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)
68.  Computer model of clonazepam`s effect in thalamic slice (Lytton 1997)
Demonstration of the effect of a minor pharmacological synaptic change at the network level. Clonazepam, a benzodiazepine, enhances inhibition but is paradoxically useful for certain types of seizures. This simulation shows how inhibition of inhibitory cells (the RE cells) produces this counter-intuitive effect.
69.  Conductance-based model of Layer-4 in the barrel cortex (Argaman et Golomb 2017)
Layer 4 in the mouse barrel cortex includes hundreds of inhibitory PV neurons and thousands of excitatory neurons. Despite this fact, its dynamical state is similar to a balanced state of large neuronal circuits.
70.  Cortex-Basal Ganglia-Thalamus network model (Kumaravelu et al. 2016)
" ... We developed a biophysical network model comprising of the closed loop cortical-basal ganglia-thalamus circuit representing the healthy and parkinsonian rat brain. The network properties of the model were validated by comparing responses evoked in basal ganglia (BG) nuclei by cortical (CTX) stimulation to published experimental results. A key emergent property of the model was generation of low-frequency network oscillations. Consistent with their putative pathological role, low-frequency oscillations in model BG neurons were exaggerated in the parkinsonian state compared to the healthy condition. ..."
71.  Cortical Basal Ganglia Network Model during Closed-loop DBS (Fleming et al 2020)
We developed a computational model of the cortical basal ganglia network to investigate closed-loop control of deep brain stimulation (DBS) for Parkinson’s disease (PD). The cortical basal ganglia network model incorporates the (i) the extracellular DBS electric field, (ii) antidromic and orthodromic activation of STN afferent fibers, (iii) the LFP detected at non-stimulating contacts on the DBS electrode and (iv) temporal variation of network beta-band activity within the thalamo-cortico-basal ganglia loop. The model facilitates investigation of clinically-viable closed-loop DBS control approaches, modulating either DBS amplitude or frequency, using an LFP derived measure of network beta-activity.
72.  Cortical model with reinforcement learning drives realistic virtual arm (Dura-Bernal et al 2015)
We developed a 3-layer sensorimotor cortical network of consisting of 704 spiking model-neurons, including excitatory, fast-spiking and low-threshold spiking interneurons. Neurons were interconnected with AMPA/NMDA, and GABAA synapses. We trained our model using spike-timing-dependent reinforcement learning to control a virtual musculoskeletal human arm, with realistic anatomical and biomechanical properties, to reach a target. Virtual arm position was used to simultaneously control a robot arm via a network interface.
73.  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.
74.  CRH modulates excitatory transmission and network physiology in hippocampus (Gunn et al. 2017)
This model simulates the effects of CRH on sharp waves in a rat CA1/CA3 model. It uses the frequency of the sharp waves as an output of the network.
75.  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.
76.  Deconstruction of cortical evoked potentials generated by subthalamic DBS (Kumaravelu et al 2018)
"... High frequency deep brain stimulation (DBS) of the subthalamic nucleus (STN) suppresses parkinsonian motor symptoms and modulates cortical activity. ... Cortical evoked potentials (cEP) generated by STN DBS reflect the response of cortex to subcortical stimulation, and the goal was to determine the neural origin of cEP using a two-step approach. First, we recorded cEP over ipsilateral primary motor cortex during different frequencies of STN DBS in awake healthy and unilateral 6-OHDA lesioned parkinsonian rats. Second, we used a biophysically-based model of the thalamocortical network to deconstruct the neural origin of the cEP. The in vivo cEP included short (R1), intermediate (R2) and long-latency (R3) responses. Model-based cortical responses to simulated STN DBS matched remarkably well the in vivo responses. R1 was generated by antidromic activation of layer 5 pyramidal neurons, while recurrent activation of layer 5 pyramidal neurons via excitatory axon collaterals reproduced R2. R3 was generated by polysynaptic activation of layer 2/3 pyramidal neurons via the cortico-thalamic-cortical pathway. Antidromic activation of the hyperdirect pathway and subsequent intracortical and cortico-thalamo-cortical synaptic interactions were sufficient to generate cEP by STN DBS, and orthodromic activation through basal ganglia-thalamus-cortex pathways was not required. These results demonstrate the utility of cEP to determine the neural elements activated by STN DBS that might modulate cortical activity and contribute to the suppression of parkinsonian symptoms."
77.  Default mode network model (Matsui et al 2014)
Default mode network (DMN) shows intrinsic, high-level activity at rest. We tested a hypothesis proposed for its role in sensory information processing: Intrinsic DMN activity facilitates neural responses to sensory input. A neural network model, consisting of a sensory network (Nsen) and a DMN, was simulated. The Nsen contained cell assemblies. Each cell assembly comprised principal cells, GABAergic interneurons (Ia, Ib), and glial cells. We let the Nsen carry out a perceptual task: detection of sensory stimuli. … This enabled the Nsen to reliably detect the stimulus. We suggest that intrinsic default model network activity may accelerate the reaction speed of the sensory network by modulating its ongoing-spontaneous activity in a subthreshold manner. Ambient GABA contributes to achieve an optimal ongoing spontaneous subthreshold neuronal state, in which GABAergic gliotransmission triggered by the intrinsic default model network activity may play an important role.
78.  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.
79.  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.
80.  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.
81.  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.
82.  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.
83.  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. ...
84.  Disentangling astroglial physiology with a realistic cell model in silico (Savtchenko et al 2018)
"Electrically non-excitable astroglia take up neurotransmitters, buffer extracellular K+ and generate Ca2+ signals that release molecular regulators of neural circuitry. The underlying machinery remains enigmatic, mainly because the sponge-like astrocyte morphology has been difficult to access experimentally or explore theoretically. Here, we systematically incorporate multi-scale, tri-dimensional astroglial architecture into a realistic multi-compartmental cell model, which we constrain by empirical tests and integrate into the NEURON computational biophysical environment. This approach is implemented as a flexible astrocyte-model builder ASTRO. As a proof-of-concept, we explore an in silico astrocyte to evaluate basic cell physiology features inaccessible experimentally. ..."
85.  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).
86.  Distance-dependent inhibition in the hippocampus (Strüber et al. 2017)
Network model of a hippocampal circuit including interneurons and principal cells. Amplitude and decay time course of inhibitory synapses can be systematically changed for different distances between connected cells. Various forms of excitatory drives can be administered to the network including spatially structured input.
87.  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.
88.  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 (http://glx.sourceforge.net/) 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="https://github.com/baubie/dtnet">https://github.com/baubie/dtnet</a>
89.  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 run.py (raw data is generated and a separate graphing program must be used to visualize the results).
90.  Dynamic dopamine modulation in the basal ganglia: Learning in Parkinson (Frank et al 2004,2005)
See README file for all info on how to run models under different tasks and simulated Parkinson's and medication conditions.
91.  Early-onset epileptic encephalopathy (Miceli et al. 2015)
Model files from the paper "Early-Onset Epileptic Encephalopathy Caused by Gain-of-Function Mutations in the Voltage Sensor of Kv7.2 and Kv7.3 Potassium Channel Subunits" by Francesco Miceli, Maria Virginia Soldovieri, Paolo Ambrosino, Michela De Maria, Michele Migliore, Rosanna Migliore, and Maurizio Taglialatela J Neurosci. 2015 Mar 4;35(9):3782-93. The file fig7C.hoc reproduces the simulations shown in Fig.7C of the paper.
92.  Effect of the initial synaptic state on the probability to induce LTP and LTD (Migliore et al. 2015)
NEURON mod files from the paper: M. Migliore, et al. (2015). In this paper, we investigate the possibility that the experimental protocols on synaptic plasticity may result in different consequences (e.g., LTD instead of LTP), according to the initial conditions of the stimulated synapses, and can generate confusing results. Using biophysical models of synaptic plasticity and hippocampal CA1 pyramidal neurons, we study how, why, and to what extent EPSPs observed at the soma after induction of LTP/LTD reflects the actual (local) synaptic state. The model and the results suggest a physiologically plausible explanation of why LTD induction is experimentally difficult, and they offer experimentally testable predictions on the stimulation protocols that may be more effective.
93.  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. ..."
94.  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.
95.  Effects of increasing CREB on storage and recall processes in a CA1 network (Bianchi et al. 2014)
Several recent results suggest that boosting the CREB pathway improves hippocampal-dependent memory in healthy rodents and restores this type of memory in an AD mouse model. However, not much is known about how CREB-dependent neuronal alterations in synaptic strength, excitability and LTP can boost memory formation in the complex architecture of a neuronal network. Using a model of a CA1 microcircuit, we investigate whether hippocampal CA1 pyramidal neuron properties altered by increasing CREB activity may contribute to improve memory storage and recall. With a set of patterns presented to a network, we find that the pattern recall quality under AD-like conditions is significantly better when boosting CREB function with respect to control. The results are robust and consistent upon increasing the synaptic damage expected by AD progression, supporting the idea that the use of CREB-based therapies could provide a new approach to treat AD.
96.  Efficient Method for Computing Synaptic Conductance (Destexhe et al 1994)
A simple model of transmitter release is used to solve first order kinetic equations of neurotransmiter/receptor binding. This method is applied to a glutamate and gabaa receptor. See reference for more details. The method is extended to more complex kinetic schemes in a seperate paper (Destexhe et al J Comp Neuro 1:195-231, 1994). Application to AMPA, NMDA, GABAA, and GABAB receptors is given in a book chapter (Destexhe et al In: The Neurobiology of Computation, Edited by Bower, J., Kluwer Academic Press, Norwell MA, 1995, pp. 9-14.) More information and papers at http://cns.iaf.cnrs-gif.fr/Main.html and through email: Destexhe@iaf.cnrs-gif.fr
97.  Electrical activity of the suprachiasmatic nuclei (Stinchcombe et al. 2017)
A network of SCN neurons coupled though GABA synapses with a light input current.
98.  Electrostimulation to reduce synaptic scaling driven progression of Alzheimers (Rowan et al. 2014)
"... As cells die and synapses lose their drive, remaining cells suffer an initial decrease in activity. Neuronal homeostatic synaptic scaling then provides a feedback mechanism to restore activity. ... The scaling mechanism increases the firing rates of remaining cells in the network to compensate for decreases in network activity. However, this effect can itself become a pathology, ... Here, we present a mechanistic explanation of how directed brain stimulation might be expected to slow AD progression based on computational simulations in a 470-neuron biomimetic model of a neocortical column. ... "
99.  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."
100.  Emergence of Connectivity Motifs in Networks of Model Neurons (Vasilaki, Giugliano 2014)
Recent evidence suggests that short-term dynamics of excitatory synaptic transmission is correlated to stereotypical connectivity motifs. We show that these connectivity motifs emerge in networks of model neurons, from the interactions between short-term synaptic dynamics (SD) and long-term spike-timing dependent plasticity (STDP).
101.  Emergence of physiological oscillation frequencies in neocortex simulations (Neymotin et al. 2011)
"Coordination of neocortical oscillations has been hypothesized to underlie the "binding" essential to cognitive function. However, the mechanisms that generate neocortical oscillations in physiological frequency bands remain unknown. We hypothesized that interlaminar relations in neocortex would provide multiple intermediate loops that would play particular roles in generating oscillations, adding different dynamics to the network. We simulated networks from sensory neocortex using 9 columns of event-driven rule-based neurons wired according to anatomical data and driven with random white-noise synaptic inputs. ..."
102.  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.
103.  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.
104.  Epilepsy may be caused by very small functional changes in ion channels (Thomas et al. 2009)
We used a previously published model of the dentate gyrus with varying degrees of mossy fibre sprouting.We preformed a sensitivity analysis where we systematically varied individual properties of ion channels. The results predict that genetic variations in the properties of sodium channels are likely to have the biggest impact on network excitability. Furthermore, these changes may be as small as 1mV, which is currently undetectable using standard experimental practices.
105.  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.
106.  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. ..."
107.  Excitotoxic loss of dopaminergic cells in PD (Muddapu et al 2019)
"... A couple of the proposed mechanisms, however, show potential for the development of a novel line of PD (Parkinson's disease) therapeutics. One of these mechanisms is the peculiar metabolic vulnerability of SNc (Substantia Nigra pars compacta) cells compared to other dopaminergic clusters; the other is the SubThalamic Nucleus (STN)-induced excitotoxicity in SNc. To investigate the latter hypothesis computationally, we developed a spiking neuron network-model of SNc-STN-GPe system. In the model, prolonged stimulation of SNc cells by an overactive STN leads to an increase in ‘stress’ variable; when the stress in a SNc neuron exceeds a stress threshold, the neuron dies. The model shows that the interaction between SNc and STN involves a positive-feedback due to which, an initial loss of SNc cells that crosses a threshold causes a runaway-effect, leading to an inexorable loss of SNc cells, strongly resembling the process of neurodegeneration. The model further suggests a link between the two aforementioned mechanisms of SNc cell loss. Our simulation results show that the excitotoxic cause of SNc cell loss might initiate by weak-excitotoxicity mediated by energy deficit, followed by strong-excitotoxicity, mediated by a disinhibited STN. A variety of conventional therapies were simulated to test their efficacy in slowing down SNc cell loss. Among them, glutamate inhibition, dopamine restoration, subthalamotomy and deep brain stimulation showed superior neuroprotective-effects in the proposed model."
108.  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
109.  Fast AMPA receptor signaling (Geiger et al 1997)
Glutamatergic transmission at a principal neuron-interneuron synapse was investigated by dual whole-cell patch-clamp recording in rat hippocampal slices combined with morphological analysis and modeling. Simulations based on a compartmental model of the interneuron indicated that the rapid postsynaptic conductance change determines the shape and the somatodendritic integration of EPSPs, thus enabling interneurons to detect synchronous principal neuron activity.
110.  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.
111.  Functional balanced networks with synaptic plasticity (Sadeh et al, 2015)
The model investigates the impact of learning on functional sensory networks. It uses large-scale recurrent networks of excitatory and inhibitory spiking neurons equipped with synaptic plasticity. It explains enhancement of orientation selectivity and emergence of feature-specific connectivity in visual cortex of rodents during development, as reported in experiments.
112.  Functional impact of dendritic branch point morphology (Ferrante et al., 2013)
" ... Here, we first quantified the morphological variability of branch points from two-photon images of rat CA1 pyramidal neurons. We then investigated the geometrical features affecting spike initiation, propagation, and timing with a computational model validated by glutamate uncaging experiments. The results suggest that even subtle membrane readjustments at branch point could drastically alter the ability of synaptic input to generate, propagate, and time action potentials."
113.  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.
114.  Gap junction plasticity as a mechanism to regulate network-wide oscillations (Pernelle et al 2018)
"Oscillations of neural activity emerge when many neurons repeatedly activate together and are observed in many brain regions, particularly during sleep and attention. Their functional role is still debated, but could be associated with normal cognitive processes such as memory formation or with pathologies such as schizophrenia and autism. Powerful oscillations are also a hallmark of epileptic seizures. Therefore, we wondered what mechanism could regulate oscillations. A type of neuronal coupling, called gap junctions, has been shown to promote synchronization between inhibitory neurons. Computational models show that when gap junctions are strong, neurons synchronize together. Moreover recent investigations show that the gap junction coupling strength is not static but plastic and dependent on the firing properties of the neurons. Thus, we developed a model of gap junction plasticity in a network of inhibitory and excitatory neurons. We show that gap junction plasticity can maintain the right amount of oscillations to prevent pathologies from emerging. Finally, we show that gap junction plasticity serves an additional functional role and allows for efficient and robust information transfer."
115.  Generating oscillatory bursts from a network of regular spiking neurons (Shao et al. 2009)
Avian nucleus isthmi pars parvocellularis (Ipc) neurons are reciprocally connected with the tectal layer 10 (L10) neurons and respond with oscillatory bursts to visual stimulation. To elucidate mechanisms of oscillatory bursting in this network of regularly spiking neurons, we investigated an experimentally constrained model of coupled leaky integrate-and-fire neurons with spike-rate adaptation. The model reproduces the observed Ipc oscillatory bursting in response to simulated visual stimulation.
116.  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.
117.  Glutamate diffusion and AMPA receptor activation in the cerebellar glomerulus (Saftenku 2005)
Synaptic conductances are influenced markedly by the geometry of the space surrounding the synapse since the transient glutamate concentration in the synaptic cleft is determined by this geometry. Our paper is an attempt to understand the reasons for slow glutamate diffusion in the cerebellar glomerulus, a structure situated around the enlarged mossy fiber terminal in the cerebellum and surrounded by a glial sheath. ... Our results suggest at least a 7- to 10-fold lower apparent diffusion coefficient of glutamate in the porous medium of the glomerulus than in water. ... See paper for details and more.
118.  Glutamate mediated dendritic and somatic plateau potentials in cortical L5 pyr cells (Gao et al '20)
Our model was built on a reconstructed Layer 5 pyramidal neuron of the rat medial prefrontal cortex, and constrained by 4 sets of experimental data: (i) voltage waveforms obtained at the site of the glutamatergic input in distal basal dendrite, including initial sodium spikelet, fast rise, plateau phase and abrupt collapse of the plateau; (ii) a family of voltage traces describing dendritic membrane responses to gradually increasing intensity of glutamatergic stimulation; (iii) voltage waveforms of backpropagating action potentials in basal dendrites (Antic, 2003); and (iv) the change of backpropagating action potential amplitude in response to drugs that block Na+ or K+ channels (Acker and Antic, 2009). Both, synaptic AMPA/NMDA and extrasynaptic NMDA inputs were placed on basal dendrites to model the induction of local regenerative potentials termed "glutamate-mediated dendritic plateau potentials". The active properties of the cell were tuned to match the voltage waveform, amplitude and duration of experimentally observed plateau potentials. The effects of input location, receptor conductance, channel properties and membrane time constant during plateau were explored. The new model predicted that during dendritic plateau potential the somatic membrane time constant is reduced. This and other model predictions were then tested in real neurons. Overall, the results support our theoretical framework that dendritic plateau potentials bring neuronal cell body into a depolarized state ("UP state"), which lasts 200 - 500 ms, or more. Plateau potentials profoundly change neuronal state -- a plateau potential triggered in one basal dendrite depolarizes the soma and shortens membrane time constant, making the cell more susceptible to action potential firing triggered by other afferent inputs. Plateau potentials may allow cortical pyramidal neurons to tune into ongoing network activity and potentially enable synchronized firing, to form active neural ensembles.
119.  Glutamate-evoked Ca2+ oscillations in single astrocytes (De Pitta et al. 2009) (Manninen et al 2017)
We tested the reproducibility and comparability of four astrocyte models (Manninen, Havela, Linne, 2017). Model by De Pitta et al. (2009) was one of them. We implemented and ran the model by De Pitta et al. (2009) using Jupyter Notebook. Model code produces results of Figure 1 and Figures 3-5 in Manninen, Havela, Linne (2017).
120.  Glutamate-evoked Ca2+ oscillations in single astrocytes (Modified from Dupont et al. 2011)
We tested the reproducibility and comparability of four astrocyte models (Manninen, Havela, Linne, 2017). Model by Dupont et al. (2011) was one of them, but we had to modify the model to get more similar results as in the original publication. We implemented and ran the modified model using Jupyter Notebook. Model code produces results of Figure 1 and Figures 3-5 in Manninen, Havela, Linne (2017).
121.  H-currents effect on the fluctuation of gamma/beta oscillations (Avella-Gonzalez et al., 2015)
This model was designed to study the impact of H-currents on the dynamics of cortical oscillations, and in paticular on the occurrence of high and low amplitude episodes (HAE, LAE) in network oscillations. The H-current is a slow, hyperpolarization-activated, depolarizing current that contributes to neuronal resonance and membrane potential. We characterized amplitude fluctuations in network oscillations by measuring the average durations of HAEs and LAEs, and explored how these were modulated by trains of external spikes, both in the presence and absence of H-channels. We looked at HAE duration, the frequency and power of network oscillations, and the effect of H-channels on the temporal voltage profile in single cells. We found that H-currents increased the oscillation frequency and, in combination with external spikes, representing input from areas outside the network, strongly decreased the synchrony of firing. As a consequence, the oscillation power and the duration of episodes during which the network exhibited high-amplitude oscillations were greatly reduced in the presence of H-channels.
122.  Hippocampal basket cell gap junction network dynamics (Saraga et al. 2006)
2 cell network of hippocampal basket cells connected by gap junctions. Paper explores how distal gap junctions and active dendrites can tune network dynamics.
123.  Hippocampal CA1 NN with spontaneous theta, gamma: full scale & network clamp (Bezaire et al 2016)
This model is a full-scale, biologically constrained rodent hippocampal CA1 network model that includes 9 cells types (pyramidal cells and 8 interneurons) with realistic proportions of each and realistic connectivity between the cells. In addition, the model receives realistic numbers of afferents from artificial cells representing hippocampal CA3 and entorhinal cortical layer III. The model is fully scaleable and parallelized so that it can be run at small scale on a personal computer or large scale on a supercomputer. The model network exhibits spontaneous theta and gamma rhythms without any rhythmic input. The model network can be perturbed in a variety of ways to better study the mechanisms of CA1 network dynamics. Also see online code at http://bitbucket.org/mbezaire/ca1 and further information at http://mariannebezaire.com/models/ca1
124.  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.
125.  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).
126.  Hippocampal Mossy Fiber bouton: presynaptic KV7 channel function (Martinello et al 2019)
127.  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."
128.  Homosynaptic plasticity in the tail withdrawal circuit (TWC) of Aplysia (Baxter and Byrne 2006)
The tail-withdrawal circuit of Aplysia provides a useful model system for investigating synaptic dynamics. Sensory neurons within the circuit manifest several forms of synaptic plasticity. Here, we developed a model of the circuit and investigated the ways in which depression (DEP) and potentiation (POT) contributed to information processing. DEP limited the amount of motor neuron activity that could be elicited by the monosynaptic pathway alone. POT within the monosynaptic pathway did not compensate for DEP. There was, however, a synergistic interaction between POT and the polysynaptic pathway. This synergism extended the dynamic range of the network, and the interplay between DEP and POT made the circuit respond preferentially to long-duration, low-frequency inputs.
129.  Human sleep/wake cycle (Rempe et al. 2010)
This model simulates sleep in the human brain and is consistent with both the flip/flop concept and the two-process model of sleep regulation. The model also gives a possible mechanism for the changes in sleep timing seen in narcolepsy.
130.  Hybrid oscillatory interference / continuous attractor NN of grid cell firing (Bush & Burgess 2014)
Matlab code to simulate a hybrid oscillatory interference - continuous attractor network model of grid cell firing in pyramidal and stellate cells of rodent medial entorhinal cortex
131.  Ih tunes oscillations in an In Silico CA3 model (Neymotin et al. 2013)
" ... We investigated oscillatory control using a multiscale computer model of hippocampal CA3, where each cell class (pyramidal, basket, and oriens-lacunosum moleculare cells), contained type-appropriate isoforms of Ih. Our model demonstrated that modulation of pyramidal and basket Ih allows tuning theta and gamma oscillation frequency and amplitude. Pyramidal Ih also controlled cross-frequency coupling (CFC) and allowed shifting gamma generation towards particular phases of the theta cycle, effected via Ih’s ability to set pyramidal excitability. ..."
132.  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.
133.  Inferior Olive, subthreshold oscillations (Torben-Nielsen, Segev, Yarom 2012)
The Inferior Olive is a brain structure in which neurons are solely connected to each other through gap-junctions. Its behavior is characterized by spontaneous subthreshold oscillation, frequency changes in the subthreshold oscillation, stable phase differences between neurons, and propagating waves of activity. Our model based on actual IO topology can reproduce these behaviors and provides a mechanistic explanation thereof.
134.  Inhibition and glial-K+ interaction leads to diverse seizure transition modes (Ho & Truccolo 2016)
"How focal seizures initiate and evolve in human neocortex remains a fundamental problem in neuroscience. Here, we use biophysical neuronal network models of neocortical patches to study how the interaction between inhibition and extracellular potassium ([K+]o) dynamics may contribute to different types of focal seizures. Three main types of propagated focal seizures observed in recent intracortical microelectrode recordings in humans were modelled ..."
135.  Inhibitory network bistability explains increased activity prior to seizure onset (Rich et al 2020)
" ... the mechanisms predisposing an inhibitory network toward increased activity, specifically prior to ictogenesis, without a permanent change to inputs to the system remain unknown. We address this question by comparing simulated inhibitory networks containing control interneurons and networks containing hyperexcitable interneurons modeled to mimic treatment with 4-Aminopyridine (4-AP), an agent commonly used to model seizures in vivo and in vitro. Our in silico study demonstrates that model inhibitory networks with 4-AP interneurons are more prone than their control counterparts to exist in a bistable state in which asynchronously firing networks can abruptly transition into synchrony driven by a brief perturbation. This transition into synchrony brings about a corresponding increase in overall firing rate. We further show that perturbations driving this transition could arise in vivo from background excitatory synaptic activity in the cortex. Thus, we propose that bistability explains the increase in interneuron activity observed experimentally prior to seizure via a transition from incoherent to coherent dynamics. Moreover, bistability explains why inhibitory networks containing hyperexcitable interneurons are more vulnerable to this change in dynamics, and how such networks can undergo a transition without a permanent change in the drive. ..."
136.  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.
137.  Inverse stochastic resonance of cerebellar Purkinje cell (Buchin et al. 2016)
This code shows the simulations of the adaptive exponential integrate-and-fire model (http://www.scholarpedia.org/article/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.
138.  Ketamine disrupts theta modulation of gamma in a computer model of hippocampus (Neymotin et al 2011)
"Abnormalities in oscillations have been suggested to play a role in schizophrenia. We studied theta-modulated gamma oscillations in a computer model of hippocampal CA3 in vivo with and without simulated application of ketamine, an NMDA receptor antagonist and psychotomimetic. Networks of 1200 multi-compartment neurons (pyramidal, basket and oriens-lacunosum moleculare, OLM, cells) generated theta and gamma oscillations from intrinsic network dynamics: basket cells primarily generated gamma and amplified theta, while OLM cells strongly contributed to theta. ..."
139.  Kinetic NMDA receptor model (Kampa et al 2004)
This kinetic NMDA receptor model is based on voltage-clamp recordings of NMDA receptor-mediated currents in nucleated patches of rat neocortical layer 5 pyramidal neurons (Kampa et al 2004 J Physiol), this model was fit with AxoGraph directly to experimental recordings in order to obtain the optimal values for the parameters. The demo shows the behaviour of a kinetic NMDA receptor model reproducing the data in figure 2. The NMDA receptor model uses realistic rates of magnesium block and its effects on channel desensitisation. Presynaptic transmitter release is necessary for glutamate binding to the receptor. This model was written by Bjoern Kampa, Canberra, 2004.
140.  Kinetic synaptic models applicable to building networks (Destexhe et al 1998)
Simplified AMPA, NMDA, GABAA, and GABAB receptor models useful for building networks are described in a book chapter. One reference paper synthesizes a comprehensive general description of synaptic transmission with Markov kinetic models which is applicable to modeling ion channels, synaptic release, and all receptors. Also a simple introduction to this method is given in a seperate paper Destexhe et al Neural Comput 6:14-18 , 1994). More information and papers at http://cns.iaf.cnrs-gif.fr/Main.html and through email: Destexhe@iaf.cnrs-gif.fr
141.  KInNeSS : a modular framework for computational neuroscience (Versace et al. 2008)
The xml files provided here implement a network of excitatory and inhibitory spiking neurons, governed by either Hodgkin-Huxley or quadratic integrate-and-fire dynamical equations. The code is used to demonstrate the capabilities of the KInNeSS software package for simulation of networks of spiking neurons. The simulation protocol used here is meant to facilitate the comparison of KInNeSS with other simulators reviewed in <a href="http://dx.doi.org/10.1007/s10827-007-0038-6">Brette et al. (2007)</a>. See the associated paper "Versace et al. (2008) KInNeSS : a modular framework for computational neuroscience." for an extensive description of KInNeSS .
142.  L4 cortical barrel NN model receiving thalamic input during whisking or touch (Gutnisky et al. 2017)
Excitatory neurons in layer 4 (L4) in the barrel cortex respond relatively strongly to touch but not to whisker movement (Yu et al., Nat. Neurosci. 2016). The model explains the mechanism underlying this effect. The network is settled to filter out most stationary inputs. Brief touch input passes through because it takes time until feed-forward inhibition silences excitatory neurons receiving brief and strong thalamic excitation.
143.  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..."
144.  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. ..."
145.  Large scale model of the olfactory bulb (Yu et al., 2013)
The readme file currently contains links to the results for all the 72 odors investigated in the paper, and the movie showing the network activity during learning of odor k3-3 (an aliphatic ketone).
146.  Large scale neocortical model for PGENESIS (Crone et al 2019)
This is model code for a large scale neocortical model based on Traub et al. (2005), modified to run on PGENESIS on supercomputing resources. "In this paper (Crone et al 2019), we evaluate the computational performance of the GEneral NEural SImulation System (GENESIS) for large scale simulations of neural networks. While many benchmark studies have been performed for large scale simulations with leaky integrate-and-fire neurons or neuronal models with only a few compartments, this work focuses on higher fidelity neuronal models represented by 50–74 compartments per neuron. ..."
147.  Large-scale model of neocortical slice in vitro exhibiting persistent gamma (Tomsett et al. 2014)
This model contains 15 neuron populations (8 excitatory, 7 inhibitory) arranged into 4 cortical layers (layer 1 empty, layers 2/3 combined). It produces a persistent gamma oscillation driven by layer 2/3. It runs using the VERTEX simulator, which is written in Matlab and is available from http://www.vertexsimulator.org
148.  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.
149.  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. "
150.  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
151.  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 https://acnavasolive.github.io/LCN-HippoModel/. More info: http://hippo-circuitlab.es/
152.  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. ... "
153.  LFP in striatum (Tanaka & Nakamura 2019)
The numerical simulations of LFP generation by cortical pyramidal neuron and medium-sized spiny neurons.
154.  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).
155.  Lobster STG pyloric network model with calcium sensor (Gunay & Prinz 2010) (Prinz et al. 2004)
This pyloric network model simulator is a C/C++ program that saves 384 different calcium sensor values that are candidates for activity sensors (Gunay and Prinz, 2010). The simulator was used to scan all of the 20 million pyloric network models that were previously collected in a database (Prinz et al, 2004).
156.  Locust olfactory network with GGN and full KC population in the mushroom body (Ray et al 2020)
We reconstructed the GGN (giant GABAergic neuron) 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 (Kenyon cell) reciprocally connected to this neuron. Finally, we added a model of the IG (Inhibitor of GGN) to reproduce in vivo odor responses in GGN.
157.  Logarithmic distributions prove that intrinsic learning is Hebbian (Scheler 2017)
"In this paper, we present data for the lognormal distributions of spike rates, synaptic weights and intrinsic excitability (gain) for neurons in various brain areas, such as auditory or visual cortex, hippocampus, cerebellum, striatum, midbrain nuclei. We find a remarkable consistency of heavy-tailed, specifically lognormal, distributions for rates, weights and gains in all brain areas examined. The difference between strongly recurrent and feed-forward connectivity (cortex vs. striatum and cerebellum), neurotransmitter (GABA (striatum) or glutamate (cortex)) or the level of activation (low in cortex, high in Purkinje cells and midbrain nuclei) turns out to be irrelevant for this feature. Logarithmic scale distribution of weights and gains appears to be a general, functional property in all cases analyzed. We then created a generic neural model to investigate adaptive learning rules that create and maintain lognormal distributions. We conclusively demonstrate that not only weights, but also intrinsic gains, need to have strong Hebbian learning in order to produce and maintain the experimentally attested distributions. This provides a solution to the long-standing question about the type of plasticity exhibited by intrinsic excitability."
158.  MDD: the role of glutamate dysfunction on Cingulo-Frontal NN dynamics (Ramirez-Mahaluf et al 2017)
" ...Currently, no mechanistic framework describes how network dynamics, glutamate, and serotonin interact to explain MDD symptoms and treatments. Here, we built a biophysical computational model of 2 areas (vACC and dlPFC) that can switch between emotional and cognitive processing. (Major Depression Disease) MDD networks were simulated by slowing glutamate decay in vACC and demonstrated sustained vACC activation. ..."
159.  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..."
160.  MEG of Somatosensory Neocortex (Jones et al. 2007)
"... To make a direct and principled connection between the SI (somatosensory primary neocortex magnetoencephalography) waveform and underlying neural dynamics, we developed a biophysically realistic computational SI model that contained excitatory and inhibitory neurons in supragranular and infragranular layers. ... our model provides a biophysically realistic solution to the MEG signal and can predict the electrophysiological correlates of human perception."
161.  Memory savings through unified pre- and postsynaptic STDP (Costa et al 2015)
Although it is well known that long-term synaptic plasticity can be expressed both pre- and postsynaptically, the functional consequences of this arrangement have remained elusive. We show that spike-timing-dependent plasticity with both pre- and postsynaptic expression develops receptive fields with reduced variability and improved discriminability compared to postsynaptic plasticity alone. These long-term modifications in receptive field statistics match recent sensory perception experiments. In these simulations we demonstrate that learning with this form of plasticity leaves a hidden postsynaptic memory trace that enables fast relearning of previously stored information, providing a cellular substrate for memory savings. Our results reveal essential roles for presynaptic plasticity that are missed when only postsynaptic expression of long-term plasticity is considered, and suggest an experience-dependent distribution of pre- and postsynaptic strength changes.
162.  Microcircuits of L5 thick tufted pyramidal cells (Hay & Segev 2015)
"... We simulated detailed conductance-based models of TTCs (Layer 5 thick tufted pyramidal cells) forming recurrent microcircuits that were interconnected as found experimentally; the network was embedded in a realistic background synaptic activity. ... Our findings indicate that dendritic nonlinearities are pivotal in controlling the gain and the computational functions of TTCs microcircuits, which serve as a dominant output source for the neocortex. "
163.  Mirror Neuron (Antunes et al 2017)
Modeling Mirror Neurons Through Spike-Timing Dependent Plasticity. This script reproduces Figure 3B.
164.  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."
165.  Model of the cerebellar granular network (Sudhakar et al 2017)
"The granular layer, which mainly consists of granule and Golgi cells, is the first stage of the cerebellar cortex and processes spatiotemporal information transmitted by mossy fiber inputs with a wide variety of firing patterns. To study its dynamics at multiple time scales in response to inputs approximating real spatiotemporal patterns, we constructed a large-scale 3D network model of the granular layer. ..."
166.  Model of the Xenopus tadpole swimming spinal network (Roberts et al. 2014)
This is a NEURON-python and MATLAB simulation code for generating anatomical or probabilistic connectivity and simulating the neuronal dynamics of the neuronal network controlling swimming in Xenopus tadpoles. For more details about this model, see Ferrario et al, 2018, eLife and Roberts et al, 2014, J of Neurosci
167.  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. ...
168.  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.
169.  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.
170.  Motor cortex microcircuit simulation based on brain activity mapping (Chadderdon et al. 2014)
"... We developed a computational model based primarily on a unified set of brain activity mapping studies of mouse M1. The simulation consisted of 775 spiking neurons of 10 cell types with detailed population-to-population connectivity. Static analysis of connectivity with graph-theoretic tools revealed that the corticostriatal population showed strong centrality, suggesting that would provide a network hub. ... By demonstrating the effectiveness of combined static and dynamic analysis, our results show how static brain maps can be related to the results of brain activity mapping."
171.  Motor system model with reinforcement learning drives virtual arm (Dura-Bernal et al 2017)
"We implemented a model of the motor system with the following components: dorsal premotor cortex (PMd), primary motor cortex (M1), spinal cord and musculoskeletal arm (Figure 1). PMd modulated M1 to select the target to reach, M1 excited the descending spinal cord neurons that drove the arm muscles, and received arm proprioceptive feedback (information about the arm position) via the ascending spinal cord neurons. The large-scale model of M1 consisted of 6,208 spiking Izhikevich model neurons [37] of four types: regular-firing and bursting pyramidal neurons, and fast-spiking and low-threshold-spiking interneurons. These were distributed across cortical layers 2/3, 5A, 5B and 6, with cell properties, proportions, locations, connectivity, weights and delays drawn primarily from mammalian experimental data [38], [39], and described in detail in previous work [29]. The network included 486,491 connections, with synapses modeling properties of four different receptors ..."
172.  Multiple mechanisms of short term plasticity at the calyx of Held (Hennig et al. 2008)
This is a new model of the short-term dynamics of glutamatergic synaptic transmission, which incorporates multiple mechanisms acting at differing sites and across a range of different time scales (ms to tens of seconds). In the paper, we show that this model can accurately reproduce the experimentally measured time-course of short term depression across different stimulus frequencies at the calyx of Held. The model demonstrates how multiple forms of activity-dependent modulation of release probability and vesicle pool depletion interact, and shows how stimulus-history-dependent recovery from synaptic depression can arise from dynamics on multiple time scales.
173.  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.
174.  Multitarget pharmacology for Dystonia in M1 (Neymotin et al 2016)
" ... We developed a multiscale model of primary motor cortex, ranging from molecular, up to cellular, and network levels, containing 1715 compartmental model neurons with multiple ion channels and intracellular molecular dynamics. We wired the model based on electrophysiological data obtained from mouse motor cortex circuit mapping experiments. We used the model to reproduce patterns of heightened activity seen in dystonia by applying independent random variations in parameters to identify pathological parameter sets. ..."
175.  Na channel mutations in the dentate gyrus (Thomas et al. 2009)
These are source files to generate the data in Figure 6 from "Mossy fiber sprouting interacts with sodium channel mutations to increase dentate gyrus excitability" Thomas EA, Reid CA, Petrou S, Epilepsia (2009)
176.  NAcc medium spiny neuron: effects of cannabinoid withdrawal (Spiga et al. 2010)
Cannabinoid withdrawal produces a hypofunction of dopaminergic neurons targeting medium spiny neurons (MSN) of the forebrain. Administration of a CB1 receptor antagonist to control rats provoked structural abnormalities, reminiscent of those observed in withdrawal conditions and support the regulatory role of cannabinoids in neurogenesis, axonal growth and synaptogenesis. Experimental observations were incorporated into a realistic computational model which predicts a strong reduction in the excitability of morphologically-altered MSN, yielding a significant reduction in action potential output. These paper provided direct morphological evidence for functional abnormalities associated with cannabinoid dependence at the level of dopaminergic neurons and their post synaptic counterpart, supporting a hypodopaminergic state as a distinctive feature of the “addicted brain”.
177.  Network bursts in cultured NN result from different adaptive mechanisms (Masquelier & Deco 2013)
It is now well established that cultured neuron networks are spontaneously active, and tend to synchronize. Synchronous events typically involve the whole network, and have thus been termed “network spikes” (NS). Using experimental recordings and numerical simulations, we show here that the inter-NS interval statistics are complex, and allow inferring the neural mechanisms at work, in particular the adaptive ones, and estimating a number of parameters to which we cannot access experimentally.
178.  Neural mass model of the neocortex under sleep regulation (Costa et al 2016)
This model generates typical human EEG patterns of sleep stages N2/N3 as well as wakefulness and REM. It further contains a sleep regulatory component, that lets the model transition between those stages independently
179.  Neuronal dendrite calcium wave model (Neymotin et al, 2015)
"... We developed a reaction-diffusion model of an apical dendrite with diffusible inositol triphosphate (IP3 ), diffusible Ca2+, IP3 receptors (IP3 Rs), endoplasmic reticulum (ER) Ca2+ leak, and ER pump (SERCA) on ER. ... At least two modes of Ca2+ wave spread have been suggested: a continuous mode based on presumed relative homogeneity of ER within the cell; and a pseudo-saltatory model where Ca2+ regeneration occurs at discrete points with diffusion between them. We compared the effects of three patterns of hypothesized IP3 R distribution: 1. continuous homogeneous ER, 2. hotspots with increased IP3R density (IP3 R hotspots), 3. areas of increased ER density (ER stacks). All three modes produced Ca2+ waves with velocities similar to those measured in vitro (~50 - 90µm /sec). ... The measures were sensitive to changes in density and spacing of IP3 R hotspots and stacks. ... An extended electrochemical model, including voltage gated calcium channels and AMPA synapses, demonstrated that membrane priming via AMPA stimulation enhances subsequent Ca2+ wave amplitude and duration. Our modeling suggests that pharmacological targeting of IP3 Rs and SERCA could allow modulation of Ca2+ wave propagation in diseases where Ca2+ dysregulation has been implicated. "
180.  Nicotinic control of dopamine release in nucleus accumbens (Maex et al. 2014)
Minimal model of the VTA (ventral segmental area) representing two (GABA versus dopamine) neuron populations and two subtypes of nicotinic receptors (alpha4beta2 versus alpha7). The model is used to tell apart circuit from receptor mechanisms in the nicotinic control of dopamine release and its pharmacological manipulation.
181.  NMDA receptor saturation (Chen et al 2001)
Experiments and modeling reported in the paper Chen N, Ren J, Raymond LA, and Murphy T (2001) support the hypothesis that glutamate has a relatively lower potency at NMDARs than previously thought from agonist application under equilibrium conditions. Further information and reprint requests are available from Dr T.H. Murphy thmurphy at interchange.ubc.ca
182.  NMDAR & GABAB/KIR Give Bistable Dendrites: Working Memory & Sequence Readout (Sanders et al., 2013)
" ...Here, we show that the voltage dependence of the inwardly rectifying potassium (KIR) conductance activated by GABA(B) receptors adds substantial robustness to network simulations of bistability and the persistent firing that it underlies. The hyperpolarized state is robust because, at hyperpolarized potentials, the GABA(B)/KIR conductance is high and the NMDA conductance is low; the depolarized state is robust because, at depolarized potentials, the NMDA conductance is high and the GABA(B)/KIR conductance is low. Our results suggest that this complementary voltage dependence of GABA(B)/KIR and NMDA conductances makes them a "perfect couple" for producing voltage bistability."
183.  Noise promotes independent control of gamma oscillations and grid firing (Solanka et al 2015)
"Neural computations underlying cognitive functions require calibration of the strength of excitatory and inhibitory synaptic connections and are associated with modulation of gamma frequency oscillations in network activity. However, principles relating gamma oscillations, synaptic strength and circuit computations are unclear. We address this in attractor network models that account for grid firing and theta-nested gamma oscillations in the medial entorhinal cortex. ..."
184.  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
185.  Numerical Integration of Izhikevich and HH model neurons (Stewart and Bair 2009)
The Parker-Sochacki method is a new technique for the numerical integration of differential equations applicable to many neuronal models. Using this method, the solution order can be adapted according to the local conditions at each time step, enabling adaptive error control without changing the integration timestep. We apply the Parker-Sochacki method to the Izhikevich ‘simple’ model and a Hodgkin-Huxley type neuron, comparing the results with those obtained using the Runge-Kutta and Bulirsch-Stoer methods.
186.  Olfactory bulb granule cell: effects of odor deprivation (Saghatelyan et al 2005)
The model supports the experimental findings on the effects of postnatal odor deprivation, and shows that a -10mV shift in the Na activation or a reduction in the dendritic length of newborn GC could independently explain the observed increase in excitability.
187.  Olfactory bulb microcircuits model with dual-layer inhibition (Gilra & Bhalla 2015)
A detailed network model of the dual-layer dendro-dendritic inhibitory microcircuits in the rat olfactory bulb comprising compartmental mitral, granule and PG cells developed by Aditya Gilra, Upinder S. Bhalla (2015). All cell morphologies and network connections are in NeuroML v1.8.0. PG and granule cell channels and synapses are also in NeuroML v1.8.0. Mitral cell channels and synapses are in native python.
188.  Olfactory bulb mitral and granule cell column formation (Migliore et al. 2007)
In the olfactory bulb, the processing units for odor discrimination are believed to involve dendrodendritic synaptic interactions between mitral and granule cells. There is increasing anatomical evidence that these cells are organized in columns, and that the columns processing a given odor are arranged in widely distributed arrays. Experimental evidence is lacking on the underlying learning mechanisms for how these columns and arrays are formed. We have used a simplified realistic circuit model to test the hypothesis that distributed connectivity can self-organize through an activity-dependent dendrodendritic synaptic mechanism. The results point to action potentials propagating in the mitral cell lateral dendrites as playing a critical role in this mechanism, and suggest a novel and robust learning mechanism for the development of distributed processing units in a cortical structure.
189.  Olfactory bulb mitral and granule cell: dendrodendritic microcircuits (Migliore and Shepherd 2008)
This model shows how backpropagating action potentials in the long lateral dendrites of mitral cells, together with granule cell actions on mitral cells within narrow columns forming glomerular units, can provide a mechanism to activate strong local inhibition between arbitrarily distant mitral cells. The simulations predict a new role for the dendrodendritic synapses in the multicolumnar organization of the granule cells.
190.  Olfactory Bulb mitral-granule network generates beta oscillations (Osinski & Kay 2016)
This model of the dendrodendritic mitral-granule synaptic network generates gamma and beta oscillations as a function of the granule cell excitability, which is represented by the granule cell resting membrane potential.
191.  Olfactory Bulb Network (Davison et al 2003)
A biologically-detailed model of the mammalian olfactory bulb, incorporating the mitral and granule cells and the dendrodendritic synapses between them. The results of simulation experiments with electrical stimulation agree closely in most details with published experimental data. The model predicts that the time course of dendrodendritic inhibition is dependent on the network connectivity as well as on the intrinsic parameters of the synapses. In response to simulated odor stimulation, strongly activated mitral cells tend to suppress neighboring cells, the mitral cells readily synchronize their firing, and increasing the stimulus intensity increases the degree of synchronization. For more details, see the reference below.
192.  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.
193.  Optimal deep brain stimulation of the subthalamic nucleus-a computational study (Feng et al. 2007)
Here, we use a biophysically-based model of spiking cells in the basal ganglia (Terman et al., Journal of Neuroscience, 22, 2963-2976, 2002; Rubin and Terman, Journal of Computational Neuroscience, 16, 211-235, 2004) to provide computational evidence that alternative temporal patterns of DBS inputs might be equally effective as the standard high-frequency waveforms, but require lower amplitudes. Within this model, DBS performance is assessed in two ways. First, we determine the extent to which DBS causes Gpi (globus pallidus pars interna) synaptic outputs, which are burstlike and synchronized in the unstimulated Parkinsonian state, to cease their pathological modulation of simulated thalamocortical cells. Second, we evaluate how DBS affects the GPi cells' auto- and cross-correlograms.
194.  Orientation preference in L23 V1 pyramidal neurons (Park et al 2019)
"Pyramidal neurons integrate synaptic inputs from basal and apical dendrites to generate stimulus-specific responses. It has been proposed that feed-forward inputs to basal dendrites drive a neuron’s stimulus preference, while feedback inputs to apical dendrites sharpen selectivity. However, how a neuron’s dendritic domains relate to its functional selectivity has not been demonstrated experimentally. We performed 2-photon dendritic micro-dissection on layer-2/3 pyramidal neurons in mouse primary visual cortex. We found that removing the apical dendritic tuft did not alter orientation-tuning. Furthermore, orientation-tuning curves were remarkably robust to the removal of basal dendrites: ablation of 2 basal dendrites was needed to cause a small shift in orientation preference, without significantly altering tuning width. Computational modeling corroborated our results and put limits on how orientation preferences among basal dendrites differ in order to reproduce the post-ablation data. In conclusion, neuronal orientation-tuning appears remarkably robust to loss of dendritic input."
195.  Orientation selectivity in inhibition-dominated recurrent networks (Sadeh and Rotter, 2015)
Emergence of contrast-invariant orientation selectivity in large-scale networks of excitatory and inhibitory neurons using integrate-and-fire neuron models.
196.  Oscillations emerging from noise-driven NNs (Tchumatchenko & Clopath 2014)
" ... Here we show how the oscillation frequency is shaped by single neuron resonance, electrical and chemical synapses.The presence of both gap junctions and subthreshold resonance are necessary for the emergence of oscillations. Our results are in agreement with several experimental observations such as network responses to oscillatory inputs and offer a much-needed conceptual link connecting a collection of disparate effects observed in networks."
197.  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.
198.  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.
199.  Parallel odor processing by mitral and middle tufted cells in the OB (Cavarretta et al 2016, 2018)
"[...] experimental findings suggest that MC and mTC may encode parallel and complementary odor representations. We have analyzed the functional roles of these pathways by using a morphologically and physiologically realistic three-dimensional model to explore the MC and mTC microcircuits in the glomerular layer and deeper plexiform layers. [...]"
200.  Parametric computation and persistent gamma in a cortical model (Chambers et al. 2012)
Using the Traub et al (2005) model of the cortex we determined how 33 synaptic strength parameters control gamma oscillations. We used fractional factorial design to reduce the number of runs required to 4096. We found an expected multiplicative interaction between parameters.
201.  Perceptual judgments via sensory-motor interaction assisted by cortical GABA (Hoshino et al 2018)
"Recurrent input to sensory cortex, via long-range reciprocal projections between motor and sensory cortices, is essential for accurate perceptual judgments. GABA levels in sensory cortices correlate with perceptual performance. We simulated a neuron-astrocyte network model to investigate how top-down, feedback signaling from a motor network (Nmot) to a sensory network (Nsen) affects perceptual judgments in association with ambient (extracellular) GABA levels. In the Nsen, astrocytic transporters modulated ambient GABA levels around pyramidal cells. A simple perceptual task was implemented: detection of a feature stimulus presented to the Nsen. ..."
202.  Persistent synchronized bursting activity in cortical tissues (Golomb et al 2005)
The program simulates a one-dimensional model of a cortical tissue with excitatory and inhibitory populations.
203.  Pipette and membrane patch geometry effects on GABAa currents patch-clamp exps (Moroni et al. 2011)
Ion currents, mediated by GABAa-receptors in outside-out membrane patches, may alter the concentration of Chloride ions inside the pipette and the membrane patch. GABAa-receptors are in fact ionotropic synaptic receptors, selective to Chloride ions. Therefore, chloride fluxes across the membrane patch correlate to GABAa-receptor opening. Chloride ions accumulation, depletion and diffusion, inside the pipette and the membrane patch, affect by definition the Chloride equilibrium (i.e. Nernst) electrical potential. This in turn changes the ionic driving force underlying GABAa-mediated currents. It follows that, in case of very small volumes and confined geometries, voltage-clamp recordings of GABAa-receptor currents carry information on both i) Chloride diffusion and ii) receptor kinetics. The relevance of (i) and (ii) have been studied numerically by defining a 1-dimensional biophysical model, released here to the interested user.
204.  PKMZ synthesis and AMPAR regulation in late long-term synaptic potentiation (Helfer & Shultz 2018)
Stochastic simulation of a set of molecular reactions that implement late long-term potentiation (L-LTP). The model is able to account for a wide range of empirical results, including induction and maintenance of late-phase LTP, cellular memory reconsolidation and the effects of different pharmaceutical interventions.
205.  Prosthetic electrostimulation for information flow repair in a neocortical simulation (Kerr 2012)
This model is an extension of a model (<a href="http://senselab.med.yale.edu/ModelDB/ShowModel.asp?model=138379">138379</a>) recently published in Frontiers in Computational Neuroscience. This model consists of 4700 event-driven, rule-based neurons, wired according to anatomical data, and driven by both white-noise synaptic inputs and a sensory signal recorded from a rat thalamus. Its purpose is to explore the effects of cortical damage, along with the repair of this damage via a neuroprosthesis.
206.  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
207.  Rapid desynchronization of an electrically coupled Golgi cell network (Vervaeke et al. 2010)
Electrical synapses between interneurons contribute to synchronized firing and network oscillations in the brain. However, little is known about how such networks respond to excitatory synaptic input. In addition to detailed electrophysiological recordings and histological investigations of electrically coupled Golgi cells in the cerebellum, a detailed network model of these cells was created. The cell models are based on reconstructed Golgi cell morphologies and the active conductances are taken from an earlier abstract Golgi cell model (Solinas et al 2007, accession no. 112685). Our results show that gap junction coupling can sometimes be inhibitory and either promote network synchronization or trigger rapid network desynchronization depending on the synaptic input. The model is available as a neuroConstruct project and can executable scripts can be generated for the NEURON simulator.
208.  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. ..."
209.  Reconstrucing sleep dynamics with data assimilation (Sedigh-Sarvestani et al., 2012)
We have developed a framework, based on the unscented Kalman filter, for estimating hidden states and parameters of a network model of sleep. The network model includes firing rates and neurotransmitter output of 5 cell-groups in the rat brain.
210.  Reducing variability in motor cortex activity by GABA (Hoshino et al. 2019)
Interaction between sensory and motor cortices is crucial for perceptual decision-making, in which intracortical inhibition might have an important role. We simulated a neural network model consisting of a sensory network (NS) and a motor network (NM) to elucidate the significance of their interaction in perceptual decision-making in association with the level of GABA in extracellular space: extracellular GABA concentration. Extracellular GABA molecules acted on extrasynaptic receptors embedded in membranes of pyramidal cells and suppressed them. A reduction in extracellular GABA concentration either in NS or NM increased the rate of errors in perceptual decision-making, for which an increase in ongoing-spontaneous fluctuations in subthreshold neuronal activity in NM prior to sensory stimulation was responsible. Feedback (NM-to-NS) signaling enhanced selective neuronal responses in NS, which in turn increased stimulus-evoked neuronal activity in NM. We suggest that GABA in extracellular space contributes to reducing variability in motor cortex activity at a resting state and thereby the motor cortex can respond correctly to a subsequent sensory stimulus. Feedback signaling from the motor cortex improves the selective responsiveness of the sensory cortex, which ensures the fidelity of information transmission to the motor cortex, leading to reliable perceptual decision-making.
211.  Reinforcement learning of targeted movement (Chadderdon et al. 2012)
"Sensorimotor control has traditionally been considered from a control theory perspective, without relation to neurobiology. In contrast, here we utilized a spiking-neuron model of motor cortex and trained it to perform a simple movement task, which consisted of rotating a single-joint “forearm” to a target. Learning was based on a reinforcement mechanism analogous to that of the dopamine system. This provided a global reward or punishment signal in response to decreasing or increasing distance from hand to target, respectively. Output was partially driven by Poisson motor babbling, creating stochastic movements that could then be shaped by learning. The virtual forearm consisted of a single segment rotated around an elbow joint, controlled by flexor and extensor muscles. ..."
212.  Reproducibility and comparability of models for astrocyte Ca2+ excitability (Manninen et al 2017)
We tested the reproducibility and comparability of four astrocyte models (Manninen, Havela, Linne, 2017). We implemented and ran the python models using Jupyter Notebook. Model code produces results of Figure 1 and Figures 3-5 and partly Figure 2 in Manninen, Havela, Linne (2017).
213.  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.
214.  Respiratory central pattern generator including Kolliker-Fuse nucleus (Wittman et al 2019)
We present three highly reduced conductance-based models for the core of the respiratory CPG. All successfully simulate respiratory outputs across eupnoeic and vagotomized conditions and show that loss of inhibition to the pontine Kolliker-Fuse nucleus reproduces the key respiratory alterations associated with Rett syndrome.
215.  Respiratory central pattern generator network in mammalian brainstem (Rubin et al. 2009)
This model is a reduced version of a spatially organized respiratory central pattern generation network consisting of four neuronal populations (pre-I, early-I, post-I, and aug-E). In this reduction, each population is represented by a single neuron, in an activity-based framework (which includes the persistent sodium current for the pre-I population). The model includes three sources of external drive and can produce several experimentally observed rhythms.
216.  Response of AMPA receptor kinetic model to glutamate release distance (Allam et al., 2015)
These files model the response of an AMPA receptor kinetic model to the release (and diffusion) of glutamate as a function of distance between receptor and release site.
217.  Ribbon Synapse (Sikora et al 2005)
A model of the ribbon synapse was developed to replicate both pre- and postsynaptic functions of this glutamatergic juncture. The presynaptic portion of the model is rich in anatomical and physiological detail and includes multiple release sites for each ribbon based on anatomical studies of presynaptic terminals, presynaptic voltage at the terminal, the activation of voltage-gated calcium channels and a calcium-dependent release mechanism whose rate varies as a function of the calcium concentration that is monitored at two different sites which control both an ultrafast, docked pool of vesicles and a release ready pool of tethered vesicles. See paper for more and details.
218.  Robust transmission in the inhibitory Purkinje Cell to Cerebellar Nuclei pathway (Abbasi et al 2017)
219.  Role for short term plasticity and OLM cells in containing spread of excitation (Hummos et al 2014)
This hippocampus model was developed by matching experimental data, including neuronal behavior, synaptic current dynamics, network spatial connectivity patterns, and short-term synaptic plasticity. Furthermore, it was constrained to perform pattern completion and separation under the effects of acetylcholine. The model was then used to investigate the role of short-term synaptic depression at the recurrent synapses in CA3, and inhibition by basket cell (BC) interneurons and oriens lacunosum-moleculare (OLM) interneurons in containing the unstable spread of excitatory activity in the network.
220.  Roles of essential kinases in induction of late hippocampal LTP (Smolen et al., 2006)
"… Convergence of multiple kinase activities to induce L-LTP helps to generate a threshold whereby the amount of L-LTP varies steeply with the number of brief (tetanic) electrical stimuli. The model simulates tetanic, -burst, pairing-induced, and chemical L-LTP, as well as L-LTP due to synaptic tagging. The model also simulates inhibition of L-LTP by inhibition of MAPK, CAMKII, PKA, or CAMKIV. The model predicts results of experiments to delineate mechanisms underlying L-LTP induction and expression. …"
221.  Roles of subthalamic nucleus and DBS in reinforcement conflict-based decision making (Frank 2006)
Deep brain stimulation (DBS) of the subthalamic nucleus dramatically improves the motor symptoms of Parkinson's disease, but causes cognitive side effects such as impulsivity. This model from Frank (2006) simulates the role of the subthalamic nucleus (STN) within the basal ganglia circuitry in decision making. The STN dynamically modulates network decision thresholds in proportion to decision conflict. The STN ``hold your horses'' signal adaptively allows the system more time to settle on the best choice when multiple options are valid. The model also replicates effects in Parkinson's patients on and off DBS in experiments designed to test the model (Frank et al, 2007).
222.  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."
223.  Sensorimotor cortex reinforcement learning of 2-joint virtual arm reaching (Neymotin et al. 2013)
"... We developed a model of sensory and motor neocortex consisting of 704 spiking model-neurons. Sensory and motor populations included excitatory cells and two types of interneurons. Neurons were interconnected with AMPA/NMDA, and GABAA synapses. We trained our model using spike-timing-dependent reinforcement learning to control a 2-joint virtual arm to reach to a fixed target. ... "
224.  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.
225.  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).
226.  Short Term Depression, Presynaptic Inhib., Neuron Diversity Roles in Antennal Lobe (Wei & Lo 2020)
227.  Short term plasticity at the cerebellar granule cell (Nieus et al. 2006)
The model reproduces short term plasticity of the mossy fibre to granule cell synapse. To reproduce synaptic currents recorded in experiments, a model of presynaptic release was used to determine the concentration of glutamate in the synaptic cleft that ultimately determined a synaptic response. The parameters of facilitation and depression were determined deconvolving AMPA EPSCs.
228.  Short term plasticity of synapses onto V1 layer 2/3 pyramidal neuron (Varela et al 1997)
This archive contains 3 mod files for NEURON that implement the short term synaptic plasticity model described in Varela, J.A., Sen, K., Gibson, J., Fost, J., Abbott, L.R., and Nelson, S.B.. A quantitative description of short-term plasticity at excitatory synapses in layer 2/3 of rat primary visual cortex. Journal of Neuroscience 17:7926-7940, 1997. Contact ted.carnevale@yale.edu if you have questions about this implementation of the model.
229.  Signaling pathways In D1R containing striatal spiny projection neurons (Blackwell et al 2018)
We implemented a mechanistic model of signaling pathways activated by dopamine D1 receptors, acetylcholine receptors, and glutamate. We use our novel, computationally efficient simulator, NeuroRD, to simulate stochastic interactions both within and between dendritic spines. Results show that the combined activity of several key plasticity molecules correctly predicts the occurrence of either LTP, LTD or no plasticity for numerous experimental protocols.
230.  Simulated cortical color opponent receptive fields self-organize via STDP (Eguchi et al., 2014)
"... In this work, we address the problem of understanding the cortical processing of color information with a possible mechanism of the development of the patchy distribution of color selectivity via computational modeling. ... Our model of the early visual system consists of multiple topographically-arranged layers of excitatory and inhibitory neurons, with sparse intra-layer connectivity and feed-forward connectivity between layers. Layers are arranged based on anatomy of early visual pathways, and include a retina, lateral geniculate nucleus, and layered neocortex. ... After training with natural images, the neurons display heightened sensitivity to specific colors. ..."
231.  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.
232.  Single compartment: nonlinear a5-GABAAR controls synaptic NMDAR activation (Schulz et al 2018)
This 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. This model shows that synaptic activation of these receptors can very effectively control voltage-dependent NMDA-receptor activation. The files contain the NEURON code for Fig.6 and Fig.7. The model is a single dendritic compartment with one glutamatergic and GABAergic synapse. Physiological properties of GABA synapses were modeled as determined by optogenetic activation of inputs during voltage-clamp recordings in Schulz et al. 2018.
233.  SN-MN neurons of Aplysia (Zhou et al. 2014)
Contribution of PKC-dependent processes to the maintenance of short-term facilitation(STF) at SN-MN synapse of Aplysia were exmained. A computational model of transmitter release demonstrated that a PKC-dependent mobilization process was sufficient to explain the maintenance of STF at nondepressed synapses and the facilitation of depressed synapses.
234.  Spatially-varying glutamate diffusion coefficient at CA1 synaptic cleft space (Gupta et al. 2016)
Due to the heterogeneous macromolecular crowding and geometrical irregularity at central excitatory synapses, the diffusion coefficient of glutamate may exhibit spatial variation across the cleft space. To take into account the effect of emergent cleft heterogeneity on the generation of excitatory postsynaptic currents (EPSCs), a gamma statistical distribution of the glutamate diffusion coefficient is considered and, using the principle of superstatistics, the glutamate transients are computed as well as the activation of AMPA receptors is performed. This model demonstrates the numerical simulation of the Brownian diffusion of glutamate under distributed diffusion coefficient, the subsequent stochastic activation of AMPA receptors using Milstein-Nicoll scheme and modified Gillespie algorithm with minimum time-step correction, and the eventual stochastic profile of EPSC generation. The study is based on the CA1 synapses located at the dendrites of CA1 pyramidal neurons in the mammalian hippocampal region.
235.  Specific inhibition of dendritic plateau potential in striatal projection neurons (Du et al 2017)
We explored dendritic plateau potentials in a biophysically detailed SPN model. We coupled the dendritic plateaus to different types of inhibitions (dendritic fast and slow inhibitions, perisomatic inhibition from FS interneurons , etc.) We found the inhibition provides precise control over the plateau potential, and thus the spiking output of SPNs.
236.  Spike timing detection in different forms of LTD (Doi et al 2005)
To understand the spike-timing detection mechanisms in cerebellar long-term depression (LTD), we developed a kinetic model of Ca dynamics within a Purkinje dendritic spine. In our kinetic simulation, IP3 was first produced via the metabotropic pathway of parallel fiber (PF) inputs, and the Ca influx in response to the climbing fiber (CF) input triggered regenerative Ca-induced Ca release from the internal stores via the IP3 receptors activated by the increased IP3. The delay in IP3 increase caused by the PF metabotropic pathway generated the optimal PF–CF interval. The Ca dynamics revealed a threshold for large Ca2 release that decreased as IP3 increased, and it coherently explained the different forms of LTD. See paper for more and details.
237.  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
238.  Spiking neuron model of the basal ganglia (Humphries et al 2006)
A spiking neuron model of the basal ganglia (BG) circuit (striatum, STN, GP, SNr). Includes: parallel anatomical channels; tonic dopamine; dopamine receptors in striatum, STN, and GP; burst-firing in STN; GABAa, AMPA, and NMDA currents; effects of synaptic location. Model demonstrates selection and switching of input signals. Replicates experimental data on changes in slow-wave (<1 Hz) and gamma-band oscillations within BG nuclei following lesions and pharmacological manipulations.
239.  Spine neck plasticity controls postsynaptic calcium signals (Grunditz et al. 2008)
This model was set up to dissect the relative contribution of different channels to the spine calcium transients measured at single spines.
240.  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.
241.  Status epilepticus alters dentate basket cell tonic inhibition (Yu J et al 2013)
Status epilepticus (SE) leads to changes in dentate inhibitory neuronal networks and alters synaptic and tonic inhibition in granule cells. Recently, we identified that one week after pilocarpine-induced status epilepticus, dentate fast-spiking basket cells (FS-BCs), which underlie fast perisomatic inhibition, show two distinct changes in inhibition: (1) enhanced tonic currents (IGABA) and (2)depolarizing shift in GABA reversal (EGABA) following SE. These two changes can have opposing effects on neuronal inhibition with increases in tonic GABA conductance (gGABA) reducing excitability when the GABA currents are shunting (or hyperpolarizing) and potentially enhancing excitability when GABA currents are depolarizing. The following model is used to examine the post-SE changes in tonic GABA conductance, together with the depolarized GABA reversal potential modify FS-BC excitability and dentate network activity.
242.  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.
243.  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).
244.  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)
245.  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).
246.  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.
247.  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.
248.  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.
249.  Striatal Spiny Projection Neuron (SPN) plasticity rule (Jedrzejewska-Szmek et al 2016)
250.  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.
251.  Structure-dynamics relationships in bursting neuronal networks revealed (Mäki-Marttunen et al. 2013)
This entry includes tools for generating and analyzing network structure, and for running the neuronal network simulations on them.
252.  Subiculum network model with dynamic chloride/potassium homeostasis (Buchin et al 2016)
This is the code implementing the single neuron and spiking neural network dynamics. The network has the dynamic ion concentrations of extracellular potassium and intracellular chloride. The code contains multiple parameter variations to study various mechanisms of the neural excitability in the context of chloride homeostasis.
253.  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.
254.  Surround Suppression in V1 via Withdraw of Balanced Local Excitation in V1 (Shushruth 2012)
The model is mean-field network models, which is set up as a so-called ring-model, i. e. it is a highly idealized model of an orientation hypercolumn in primary visual cortex. Long-range intra-areal and inter-areal feedback connections are modeled phenomenologically as an external input. In this model, there are recurrent interactions via short-range local connections between orientation columns, but not between hypercolumns.
255.  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.
256.  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.
257.  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.
258.  Synaptic plasticity: pyramid->pyr and pyr->interneuron (Tsodyks et al 1998)
An implementation of a model of short-term synaptic plasticity with NEURON. The model was originally described by Tsodyks et al., who assumed that the synapse acted as a current source, but this implementation treats it as a conductance change. Tsodyks, M., Pawelzik, K., Markram, H. Neural networks with dynamic synapses. Neural Computation 10:821-835, 1998. Tsodyks, M., Uziel, A., Markram, H. Synchrony generation in recurrent networks with frequency-dependent synapses. J. Neurosci. 2000 RC50.
259.  Synaptic scaling balances learning in a spiking model of neocortex (Rowan & Neymotin 2013)
Learning in the brain requires complementary mechanisms: potentiation and activity-dependent homeostatic scaling. We introduce synaptic scaling to a biologically-realistic spiking model of neocortex which can learn changes in oscillatory rhythms using STDP, and show that scaling is necessary to balance both positive and negative changes in input from potentiation and atrophy. We discuss some of the issues that arise when considering synaptic scaling in such a model, and show that scaling regulates activity whilst allowing learning to remain unaltered.
260.  Synaptic transmission at the calyx of Held (Graham et al 2001)
This model allows the user to investigate faciliation and depression in a complex Monte Carlo model of the calyx of Held, a giant synapse in the mammalian auditory system (Graham et al, 2001)
261.  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.
262.  Thalamic network model of deep brain stimulation in essential tremor (Birdno et al. 2012)
"... Thus the decreased effectiveness of temporally irregular DBS trains is due to long pauses in the stimulus trains, not the degree of temporal irregularity alone. We also conducted computer simulations of neuronal responses to the experimental stimulus trains using a biophysical model of the thalamic network. Trains that suppressed tremor in volunteers also suppressed fluctuations in thalamic transmembrane potential at the frequency associated with cerebellar burst-driver inputs. Clinical and computational findings indicate that DBS suppresses tremor by masking burst-driver inputs to the thalamus and that pauses in stimulation prevent such masking. Although stimulation of other anatomic targets may provide tremor suppression, we propose that the most relevant neuronal targets for effective tremor suppression are the afferent cerebellar fibers that terminate in the thalamus."
263.  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.
264.  Thalamic quiescence of spike and wave seizures (Lytton et al 1997)
A phase plane analysis of a two cell interaction between a thalamocortical neuron (TC) and a thalamic reticularis neuron (RE).
265.  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.
266.  Thalamocortical augmenting response (Bazhenov et al 1998)
In the cortical model, augmenting responses were more powerful in the "input" layer compared with those in the "output" layer. Cortical stimulation of the network model produced augmenting responses in cortical neurons in distant cortical areas through corticothalamocortical loops and low-threshold intrathalamic augmentation. ... The predictions of the model were compared with in vivo recordings from neurons in cortical area 4 and thalamic ventrolateral nucleus of anesthetized cats. The known intrinsic properties of thalamic cells and thalamocortical interconnections can account for the basic properties of cortical augmenting responses. See reference for details. NEURON implementation note: cortical SU cells are getting slightly too little stimulation - reason unknown.
267.  Thalamocortical control of propofol phase-amplitude coupling (Soplata et al 2017)
"The anesthetic propofol elicits many different spectral properties on the EEG, including alpha oscillations (8-12 Hz), Slow Wave Oscillations (SWO, 0.1-1.5 Hz), and dose-dependent phase-amplitude coupling (PAC) between alpha and SWO. Propofol is known to increase GABAA inhibition and decrease H-current strength, but how it generates these rhythms and their interactions is still unknown. To investigate both generation of the alpha rhythm and its PAC to SWO, we simulate a Hodgkin-Huxley network model of a hyperpolarized thalamus and corticothalamic inputs. ..."
268.  Thalamocortical model of spike and wave seizures (Suffczynski et al. 2004)
SIMULINK macroscopic model of transitions between normal (spindle) activity and spike and wave (SW) discharges in the thalamocortical network. The model exhibits bistability properties and stochastic fluctuations present in the network may flip the system between the two operational states. The predictions of the model were compared with real EEG data in rats and humans. A possibility to abort an ictal state by a single counter stimulus is suggested by the model.
269.  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."
270.  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.
271.  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. ..."
272.  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."
273.  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.
274.  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.
275.  Vertical System (VS) tangential cells network model (Trousdale et al. 2014)
Network model of the VS tangential cell system, with 10 cells per hemisphere. Each cell is a two compartment model with one compartment for dendrites and one for the axon. The cells are coupled through axonal gap junctions. The code allows to simulate responses of the VS network to a variety of visual stimuli to investigate coding as a function of gap junction strength.
276.  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.
277.  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.

Re-display model names without descriptions