|
|
)
(NEURON is a simulation environment for developing and exercising models of neurons and networks of neurons. It is particularly well-suited to problems where cable properties of cells play an important role, possibly including extracellular potential close to the membrane), and where cell membrane properties are complex, involving many ion-specific channels, ion accumulation, and second messengers. It evolved from a long collaboration between Michael Hines and John W. Moore at the Department of Neurobiology, Duke University. Their express goal was to create a tool designed specifically for solving the equations that describe nerve cells.)
| Models | Description |
| 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. | |
| A detailed and fast model of extracellular recordings (Camunas-Mesa & Qurioga 2013) | |
| "We present a novel method to generate realistic simulations of extracellular recordings. The simulations were obtained by superimposing the activity of neurons placed randomly in a cube of brain tissue. Detailed models of individual neurons were used to reproduce the extracellular action potentials of close-by neurons. ..." | |
| A fast model of voltage-dependent NMDA Receptors (Moradi et al. 2012) | |
| 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. | |
| A model for interaural time difference sensitivity in the medial superior olive (Zhou et al 2005) | |
| This model simulates responses of neurons to interaural time difference (ITD) in the medial superior olive (MSO) of the mammalian brainstem. The model has a bipolar cell structure and incorporates two anatomic observations in the MSO: (1) the axon arises from the dendrite that receives ipsilateral inputs and (2) inhibitory synapses are located primarily on the soma in adult animals. Fine adjustment of the best ITD is achieved by the interplay of somatic sodium currents and synaptic inhibitory currents. The model suggests a mechanism for dynamically "fine-tuning" the ITD sensitivity of MSO cells by the opponency between depolarizing sodium currents and hyperpolarizing inhibitory currents. | |
| 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. | |
| A multi-compartment model for interneurons in the dLGN (Halnes et al. 2011) | |
| This model for dLGN interneurons is presented in two parameterizations (P1 & P2), which were fitted to current-clamp data from two different interneurons (IN1 & IN2). The model qualitatively reproduces the responses in IN1 & IN2 under 8 different experimental condition, and quantitatively reproduces the I/O-relations (#spikes elicited as a function of injected current). | |
| 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. | |
| A set of reduced models of layer 5 pyramidal neurons (Bahl et al. 2012) | |
| These are the NEURON files for 10 different models of a reduced L5 pyramidal neuron. The parameters were obtained by automatically fitting the models to experimental data using a multi objective evolutionary search strategy. Details on the algorithm can be found at www.g-node.org/emoo and in Bahl et al. (2012). | |
| A simplified cerebellar Purkinje neuron (the PPR model) (Brown et al. 2010) | |
| These models were implemented in NEURON by Sherry-Ann Brown in the laboratory of Leslie M. Loew. The files reproduce Figures 2c-f from Brown et al, 2010 "Virtual NEURON: a Strategy For Merged Biochemical and Electrophysiological Modeling". | |
| A single column thalamocortical network model (Traub et al 2005) | |
| To better understand population phenomena in thalamocortical neuronal ensembles, we have constructed a preliminary network model with 3,560 multicompartment neurons (containing soma, branching dendrites, and a portion of axon). Types of neurons included superficial pyramids (with regular spiking [RS] and fast rhythmic bursting [FRB] firing behaviors); RS spiny stellates; fast spiking (FS) interneurons, with basket-type and axoaxonic types of connectivity, and located in superficial and deep cortical layers; low threshold spiking (LTS) interneurons, that contacted principal cell dendrites; deep pyramids, that could have RS or intrinsic bursting (IB) firing behaviors, and endowed either with non-tufted apical dendrites or with long tufted apical dendrites; thalamocortical relay (TCR) cells; and nucleus reticularis (nRT) cells. To the extent possible, both electrophysiology and synaptic connectivity were based on published data, although many arbitrary choices were necessary. | |
| A two-layer biophysical olfactory bulb model of cholinergic neuromodulation (Li and Cleland 2013) | |
| This is a two-layer biophysical olfactory bulb (OB) network model to study cholinergic neuromodulation. Simulations show that nicotinic receptor activation sharpens mitral cell receptive field, while muscarinic receptor activation enhances network synchrony and gamma oscillations. This general model suggests that the roles of nicotinic and muscarinic receptors in OB are both distinct and complementary to one another, together regulating the effects of ascending cholinergic inputs on olfactory bulb transformations. | |
| AP back-prop. explains threshold variability and rapid rise (McCormick et al. 2007, Yu et al. 2008) | |
| This simple axon-soma model explained how the rapid rising phase in the somatic spike is derived from the propagated axon initiated spike, and how the somatic spike threshold variance is affected by spike propagation. | |
| AP initiation and propagation in type II cochlear ganglion cell (Hossain et al 2005) | |
| The model of type II cochlear ganglion cell was based on the immunostaining of the mouse auditory pathway. Specific antibodies were used to map the distribution of voltage-dependent sodium channels along the two unmyelinated axon-like processes of the bipolar ganglion cells. Three distinct hot spots were detected. A high density of sodium channels was present over the entire trajectory of sensory endings beneath the outer hair cells (the most distal portion of the peripheral axon). THE other two hot spots were localized in the initial segments of both of the axons that flank the unmyelinated bipolar ganglion cell bodies. A biophysical model indicates that all three hot spots might play important roles in action potential initiation and propagation. For instance, the hot spot in the receptor segment is important for transforming the receptor potentials into a full blown action potential (Supplemental Fig. 1). The hot spots in the two paraganglionic axon initial segments are there to ensure the successful propagation of action potentials from the peripheral to the central axon through the cell body. The Readme.txt file provides step by step instructions on how to recreate Figures 6 and 7 of Hossain et al., 2005 paper. | |
| AP shape and parameter constraints in optimization of compartment models (Weaver and Wearne 2006) | |
| "... We construct an objective function that includes both time-aligned action potential shape error and errors in firing rate and firing regularity. We then implement a variant of simulated annealing that introduces a recentering algorithm to handle infeasible points outside the boundary constraints. We show how our objective function captures essential features of neuronal firing patterns, and why our boundary management technique is superior to previous approaches." | |
| Accurate and fast simulation of channel noise in conductance-based model neurons (Linaro et al 2011) | |
| We introduce and operatively present a general method to simulate channel noise in conductance-based model neurons, with modest computational overheads. Our approach may be considered as an accurate generalization of previous proposal methods, to the case of voltage-, ion-, and ligand-gated channels with arbitrary complexity. We focus on the discrete Markov process descriptions, routinely employed in experimental identification of voltage-gated channels and synaptic receptors. | |
| Action Potential initiation and backpropagation in Neocortical L5 Pyramidal Neuron (Hu et al. 2009) | |
| "...Previous computational studies have yielded conflicting conclusions about the role of Na+ channel density and biophysical properties in action potential initiation as a result of inconsistent estimates of channel density. Our modeling studies integrated the immunostaining and electrophysiological results and showed that the lowest threshold for action potential initiation at the distal AIS was largely determined by the density of low-threshold Nav1.6 channels ... Distinct from the function of Nav1.6 channel, the Nav1.2 channel may control action potential backpropagation because of its high density at the proximal AIS and high threshold. ... In conclusion, distal AIS accumulation of Nav1.6 channels determines the low threshold for action potential initiation; whereas proximal AIS accumulation of Nav1.2 channels sets the threshold for the generation of somatodendritic potentials and ensures action potential backpropagation to the soma and dendrites. Thus, Nav1.6 and Nav1.2 channels serve distinct functions in action potential initiation and backpropagation." | |
| Action potential reconstitution from measured current waveforms (Alle et al. 2009) | |
| This NEURON code reconstitutes action potentials in a model of a hippocampal mossy fiber from experimentally measured sodium, potassium and calcium current waveforms as described in Alle et al. (2009). | |
| Action potential-evoked Na+ influx are similar in axon and soma (Fleidervish et al. 2010) | |
| "In cortical pyramidal neurons, the axon initial segment (AIS) is pivotal in synaptic integration. It has been asserted that this is because there is a high density of Na+ channels in the AIS. However, we found that action potential–associated Na+ flux, as measured by high-speed fluorescence Na+ imaging, was about threefold larger in the rat AIS than in the soma. Spike-evoked Na+ flux in the AIS and the first node of Ranvier was similar and was eightfold lower in basal dendrites. ... In computer simulations, these data were consistent with the known features of action potential generation in these neurons." | |
| Active dendrites and spike propagation in a hippocampal interneuron (Saraga et al 2003) | |
| We create multi-compartment models of an Oriens-Lacunosum/Moleculare (O-LM) hippocampal interneuron using passive properties, channel kinetics, densities and distributions specific to this cell type, and explore its signaling characteristics. We find that spike initiation depends on both location and amount of input, as well as the intrinsic properties of the interneuron. Distal synaptic input always produces strong back-propagating spikes whereas proximal input could produce both forward and back-propagating spikes depending on the input strength. Please see paper for more details. Fernanda.Saraga@utoronto.ca | |
| Active dendritic action potential propagation (Casale & McCormick 2011) | |
| This model explores the dendritic sodium and potassium conductances needed to recapitulate voltage-sensitive dye optical recordings of thalamic interneuron dendrites in the dorsal lateral geniculate nucleus. Model ion channels were selected based on pharmacological data. | |
| Activity dependent conductances in a neuron model (Liu et al. 1998) | |
| "... We present a model of a stomatogastric ganglion (STG) neuron in which several Ca2+-dependent pathways are used to regulate the maximal conductances of membrane currents in an activity-dependent manner. Unlike previous models of this type, the regulation and modification of maximal conductances by electrical activity is unconstrained. The model has seven voltage-dependent membrane currents and uses three Ca2+ sensors acting on different time scales. ... The model suggests that neurons may regulate their conductances to maintain fixed patterns of electrical activity, rather than fixed maximal conductances, and that the regulation process requires feedback systems capable of reacting to changes of electrical activity on a number of different time scales." | |
| Activity dependent regulation of pacemaker channels by cAMP (Wang et al 2002) | |
| Demonstration of the physiological consequences of the cyclic allosteric gating scheme for Ih mediated by HCN2 in thalamocortical relay cells. | |
| Afferent Integration in the NAcb MSP Cell (Wolf et al. 2005) | |
| "We describe a computational model of the principal cell in the nucleus accumbens (NAcb), the medium spiny projection (MSP) neuron. The model neuron, constructed in NEURON, includes all of the known ionic currents in these cells and receives synaptic input from simulated spike trains via NMDA, AMPA, and GABAA receptors. ... results suggest that afferent information integration by the NAcb MSP cell may be compromised by pathology in which the NMDA current is altered or modulated, as has been proposed in both schizophrenia and addiction." | |
| Alcohol excites Cerebellar Golgi Cells by inhibiting the Na+/K+ ATPase (Botta et al.2010) | |
| Patch-clamp in cerebellar slices and computer modeling show that ethanol excites Golgi cells by inhibiting the Na+/K+ ATPase. In particular, voltage-clamp recordings of Na+/K+ ATPase currents indicated that ethanol partially inhibits this pump and this effect could be mimicked by low concentrations of the Na+/K+ ATPase blocker ouabain. The partial inhibition of Na+/K+ ATPase in a computer model of the Golgi cell reproduced these experimental findings that established a novel mechanism of action of ethanol on neural excitability. | |
| Altered complexity in layer 2/3 pyramidal neurons (Luuk van der Velden et al. 2012) | |
| " ... Our experimental results show that hypercomplexity of the apical dendritic tuft of layer 2/3 pyramidal neurons affects neuronal excitability by reducing the amount of spike frequency adaptation. This difference in firing pattern, related to a higher dendritic complexity, was accompanied by an altered development of the afterhyperpolarization slope with successive action potentials. Our abstract and realistic neuronal models, which allowed manipulation of the dendritic complexity, showed similar effects on neuronal excitability and confirmed the impact of apical dendritic complexity. Alterations of dendritic complexity, as observed in several pathological conditions such as neurodegenerative diseases or neurodevelopmental disorders, may thus not only affect the input to layer 2/3 pyramidal neurons but also shape their firing pattern and consequently alter the information processing in the cortex." | |
| Amyloid beta (IA block) effects on a model CA1 pyramidal cell (Morse et al. 2010) | |
| The model simulations provide evidence oblique dendrites in CA1 pyramidal neurons are susceptible to hyper-excitability by amyloid beta block of the transient K+ channel, IA. See paper for details. | |
| 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. | |
| 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 | |
| Arteriolar networks: Spread of potential (Crane et al 2001) | |
| Crane, G.J., Hines, M.L., and Neild, T.O. (2001) Simulating the spread of membrane potential changes in arteriolar networks. Microcirculation 8:33-43. This model uses a gap junction density mechanism to couple arteriolar smooth muscle and endothelium in microvascular trees. | |
| Artificial neuron model (Izhikevich 2003) | |
| A model is presented that reproduces spiking and bursting behavior of known types of cortical neurons. The model combines the biologically plausibility of Hodgkin–Huxley-type dynamics and the computational efficiency of integrate-and-fire neurons. Using this model, one can simulate tens of thousands of spiking cortical neurons in real time (1 ms resolution) using a desktop PC. | |
| Availability of low-threshold Ca2+ current in retinal ganglion cells (Lee SC et al. 2003) | |
| "... we measured T-type current of isolated goldfish retinal ganglion cells with perforated-patch voltageclamp methods in solutions containing a normal extracellular Ca2+ concentration. The voltage sensitivities and rates of current activation, inactivation, deactivation, and recovery from inactivation were similar to those of expressed +1G (CaV3.1) Ca2+ channel clones, except that the rate of deactivation was significantly faster. We reproduced the amplitude and kinetics of measured T currents with a numerical simulation based on a kinetic model developed for an +1G Ca2+ channel. Finally, we show that this model predicts the increase of T-type current made available between resting potential and spike threshold by repetitive hyperpolarizations presented at rates that are within the bandwidth of signals processed in situ by these neurons." | |
| Axonal NaV1.6 Sodium Channels in AP Initiation of CA1 Pyramidal Neurons (Royeck et al. 2008) | |
| "... We show that the Na+ channel NaV1.6 displays a striking aggregation at the AIS of cortical neurons. ... In combination with simulations using a realistic computer model of a CA1 pyramidal cell, our results imply that a hyperpolarized voltage-dependence of activation of AIS NaV1.6 channels is important both in determining spike threshold and localizing spike initiation to the AIS. ... These results suggest that NaV1.6 subunits at the AIS contribute significantly to its role as spike trigger zone and shape repetitive discharge properties of CA1 neurons." | |
| 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. ..." | |
| Balance of excitation and inhibition (Carvalho and Buonomano 2009) | |
| " ... Here, theoretical analyses reveal that excitatory synaptic strength controls the threshold of the neuronal input-output function, while inhibitory plasticity alters the threshold and gain. Experimentally, changes in the balance of excitation and inhibition in CA1 pyramidal neurons also altered their input-output function as predicted by the model. These results support the existence of two functional modes of plasticity that can be used to optimize information processing: threshold and gain plasticity." | |
| 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. | |
| Biophysical and phenomenological models of spike-timing dependent plasticity (Badoual et al. 2006) | |
| "Spike-timing dependent plasticity (STDP) is a form of associative synaptic modification which depends on the respective timing of pre- and post-synaptic spikes. The biophysical mechanisms underlying this form of plasticity are currently not known. We present here a biophysical model which captures the characteristics of STDP, such as its frequency dependency, and the effects of spike pair or spike triplet interactions. ... A simplified phenomenological model is also derived..." | |
| Biophysically realistic neural modeling of the MEG mu rhythm (Jones et al. 2009) | |
| "Variations in cortical oscillations in the alpha (7–14 Hz) and beta (15–29 Hz) range have been correlated with attention, working memory, and stimulus detection. The mu rhythm recorded with magnetoencephalography (MEG) is a prominent oscillation generated by Rolandic cortex containing alpha and beta bands. Despite its prominence, the neural mechanisms regulating mu are unknown. We characterized the ongoing MEG mu rhythm from a localized source in the finger representation of primary somatosensory (SI) cortex. Subjects showed variation in the relative expression of mu-alpha or mu-beta, which were nonoverlapping for roughly 50% of their respective durations on single trials. To delineate the origins of this rhythm, a biophysically principled computational neural model of SI was developed, with distinct laminae, inhibitory and excitatory neurons, and feedforward (FF, representative of lemniscal thalamic drive) and feedback (FB, representative of higher-order cortical drive or input from nonlemniscal thalamic nuclei) inputs defined by the laminar location of their postsynaptic effects. ..." | |
| Boolean network-based analysis of the apoptosis network (Mai and Liu 2009) | |
| "To understand the design principles of the molecular interaction network associated with the irreversibility of cell apoptosis and the stability of cell surviving, we constructed a Boolean network integrating both the intrinsic and extrinsic pro-apoptotic pathways with pro-survival signal transduction pathways. We performed statistical analyses of the dependences of cell fate on initial states and on input signals. The analyses reproduced the well-known pro- and anti-apoptotic effects of key external signals and network components. We found that the external GF signal by itself did not change the apoptotic ratio from randomly chosen initial states when there is no external TNF signal, but can significantly offset apoptosis induced by the TNF signal. ..." | |
| Broadening of activity with flow across neural structures (Lytton et al. 2008) | |
| "Synfire chains have long been suggested as a substrate for perception and information processing in the nervous system. However, embedding activation chains in a densely connected nervous matrix risks spread of signal that will obscure or obliterate the message. We used computer modeling and physiological measurements in rat hippocampus to assess this problem of activity broadening. We simulated a series of neural modules with feedforward propagation and random connectivity within each module and from one module to the next. ..." | |
| Bursting and resonance in cerebellar granule cells (D'Angelo et al 2001) | |
| In this study we report theta-frequency (3–12 Hz) bursting and resonance in rat cerebellar granule cells and show that these neurons express a previously unidentified slow repolarizing K1 current (IK-slow ). Our experimental and modeling results indicate that IK-slow was necessary for both bursting and resonance. See paper for more. | |
| Bursting respiratory net: clustered architecture gives large phase diff`s (Fietkiewicz et al 2011) | |
| Using a previous model of respiratory rhythm generation, we modified the network architecture such that cells can be segregated into two clusters. Cells within a given cluster burst with smaller phase differences than do cells from different clusters. This may explain the large phase differences seen experimentally, as reported in the paper. | |
| CA1 Pyramidal Neuron: slow Na+ inactivation (Migliore 1996) | |
| Model files from the paper: M. Migliore, Modeling the attenuation and failure of action potentials in the dendrites of hippocampal neurons, Biophys. J. 71:2394-403 (1996). Please see the below readme file for installation and use instructions. Contact michele.migliore@pa.ibf.cnr.it if you have any questions about the implementation of the model. | |
| CA1 interneuron: K currents (Lien et al 2002) | |
| NEURON mod files for slow and fast K-DR, and K-A potassium currents in inhibitory interneurones of stratum oriens-alveus of the hippocampal CA1 region. | |
| CA1 oriens alveus interneurons: signaling properties (Minneci et al. 2007) | |
| The model supports the experimental findings showing that the dynamic interaction between cells with various firing patterns could differently affect GABAergic signaling, leading to a wide range of interneuronal communication within the hippocampal network. | |
| CA1 pyramidal cell: reconstructed axonal arbor and failures at weak gap junctions (Vladimirov 2011) | |
| Model of pyramidal CA1 cells connected by gap junctions in their axons. Cell geometry is based on anatomical reconstruction of rat CA1 cell (NeuroMorpho.Org ID: NMO_00927) with long axonal arbor. Model init_2cells.hoc shows failures of second spike propagation in a spike doublet, depending on conductance of an axonal gap junction. Model init_ring.hoc shows that spike failure result in reentrant oscillations of a spike in a loop of axons connected by gap junctions, where one gap junction is weak. The paper shows that in random networks of axons connected by gap junctions, oscillations are driven by single pacemaker loop of axons. The shortest loop, around which a spike can travel, is the most likely pacemaker. This principle allows us to predict the frequency of oscillations from network connectivity and visa versa. We propose that this type of oscillations corresponds to so-called fast ripples in epileptic hippocampus. | |
| CA1 pyramidal neuron (Migliore et al 1999) | |
| Hippocampal CA1 pyramidal neuron model from the paper M.Migliore, D.A Hoffman, J.C. Magee and D. Johnston (1999) Role of an A-type K+ conductance in the back-propagation of action potentials in the dendrites of hippocampal pyramidal neurons, J. Comput. Neurosci. 7, 5-15. Instructions are provided in the below README file.Contact michele.migliore@pa.ibf.cnr.it if you have any questions about the implementation of the model. | |
| CA1 pyramidal neuron synaptic integration (Li and Ascoli 2006, 2008) | |
| The model shows how different input patterns (irregular & asynchronous, irregular & synchronous, regular & asynchronous, regular & synchronous) affect the neuron's output rate when 1000 synapses are distributed in the proximal apical dendritic tree of a hippocampus CA1 pyramidal neuron. | |
| CA1 pyramidal neuron to study INaP properties and repetitive firing (Uebachs et al. 2010) | |
| A model of a CA1 pyramidal neuron containing a biophysically realistic morphology and 15 distributed voltage and Ca2+-dependent conductances. Repetitive firing is modulated by maximal conductance and the voltage dependence of the persistent Na+ current (INaP). | |
| CA1 pyramidal neuron: as a 2-layer NN and subthreshold synaptic summation (Poirazi et al 2003) | |
| We developed a CA1 pyramidal cell model calibrated with a broad spectrum of in vitro data. Using simultaneous dendritic and somatic recordings, and combining results for two different response measures (peak vs. mean EPSP), two different stimulus formats (single shock vs. 50 Hz trains), and two different spatial integration conditions (within vs. between-branch summation), we found the cell's subthreshold responses to paired inputs are best described as a sum of nonlinear subunit responses, where the subunits correspond to different dendritic branches. In addition to suggesting a new type of experiment and providing testable predictions, our model shows how conclusions regarding synaptic arithmetic can be influenced by an array of seemingly innocuous experimental design choices. | |
| CA1 pyramidal neuron: Ih current (Migliore et al. 2012) | |
| NEURON files from the paper: Migliore M, Migliore R (2012) Know Your Current Ih: Interaction with a Shunting Current Explains the Puzzling Effects of Its Pharmacological or Pathological Modulations. PLoS ONE 7(5): e36867. doi:10.1371/journal.pone.0036867. Experimental findings on the effects of Ih current modulation, which is particularly involved in epilepsy, appear to be inconsistent. In the paper, using a realistic model we show how and why a shunting current, such as that carried by TASK-like channels, dependent on the Ih peak conductance is able to explain virtually all experimental findings on Ih up- or down-regulation by modulators or pathological conditions. | |
| CA1 pyramidal neuron: Synaptic Scaling (Magee, Cook 2000) | |
| Jeffrey Magee and Erik Cook found evidence in experiments and modeling that support the hypothesis that an increase in synaptic conductance for synapses at larger distances from the soma is responsible for reducing the location dependence (relative to the soma) of synapses. | |
| 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. | |
| CA1 pyramidal neuron: conditional boosting of dendritic APs (Watanabe et al 2002) | |
| Model files from the paper Watanabe S, Hoffman DA, Migliore M, Johnston D (2002). The experimental and modeling results support the hypothesis that dendritic K-A channels and the boosting of back-propagating action potentials contribute to the induction of LTP in CA1 neurons. See the paper for details. Questions about the model may be addressed to Michele Migliore: michele.migliore@pa.ibf.cnr.it | |
| 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). | |
| 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. | |
| CA1 pyramidal neuron: effects of Ih on distal inputs (Migliore et al 2004) | |
| NEURON mod files from the paper: M. Migliore, L. Messineo, M. Ferrante Dendritic Ih selectively blocks temporal summation of unsynchronized distal inputs in CA1 pyramidal neurons, J.Comput. Neurosci. 16:5-13 (2004). The model demonstrates how the dendritic Ih in pyramidal neurons could selectively suppress AP generation for a volley of excitatory afferents when they are asynchronously and distally activated. | |
| CA1 pyramidal neuron: effects of Lamotrigine on dendritic excitability (Poolos et al 2002) | |
| NEURON mod files from N. Poolos, M. Migliore, and D. Johnston, Nature Neuroscience (2002). The experimental and modeling results in this paper demonstrate for the first time that neuronal excitability can be altered by pharmaceuticals acting selectively on dendrites, and suggest an important role for Ih in controlling dendritic excitability and epileptogenesis. | |
| CA1 pyramidal neuron: effects of R213Q and R312W Kv7.2 mutations (Miceli et al. 2013) | |
| NEURON mod files from the paper: Miceli et al, Genotype–phenotype correlations in neonatal epilepsies caused by mutations in the voltage sensor of Kv7.2 potassium channel subunits, PNAS 2013 Feb 25. [Epub ahead of print] In this paper, functional studies revealed that in homomeric or heteromeric configuration with KV7.2 and/or KV7.3 subunits, R213W and R213Q mutations markedly destabilized the open state, causing a dramatic decrease in channel voltage sensitivity. Modeling these channels in CA1 hippocampal pyramidal cells revealed that both mutations increased cell firing frequency, with the R213Q mutation prompting more dramatic functional changes compared with the R213W mutation. | |
| CA1 pyramidal neuron: functional significance of axonal Kv7 channels (Shah et al. 2008) | |
| The model used in this paper confirmed the experimental findings suggesting that axonal Kv7 channels are critically and uniquely required for determining the inherent spontaneous firing of hippocampal CA1 pyramids, independently of alterations in synaptic activity. The model predicts that the axonal Kv7 density could be 3-5 times that at the soma. | |
| CA1 pyramidal neuron: h channel-dependent deficit of theta oscill. resonance (Marcelin et al. 2008) | |
| This model was used to confirm and support experimental data suggesting that the neuronal/circuitry changes associated with temporal lobe epilepsy, including Ih-dependent inductive mechanisms, can disrupt hippocampal theta function. | |
| 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. | |
| 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. | |
| 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. | |
| 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. | |
| CA1 pyramidal neuron: synaptically-induced bAP predicts synapse location (Sterratt et al. 2012) | |
| This is an adaptation of Poirazi et al.'s (2003) CA1 model that is used to measure BAP-induced voltage and calcium signals in spines after simulated Schaffer collateral synapse stimulation. In the model, the peak calcium concentration is highly correlated with soma-synapse distance under a number of physiologically-realistic suprathreshold stimulation regimes and for a range of dendritic morphologies. There are also simulations demonstrating that peak calcium can be used to set up a synaptic democracy in a homeostatic manner, whereby synapses regulate their synaptic strength on the basis of the difference between peak calcium and a uniform target value. | |
| CA1 pyramidal neurons: binding properties and the magical number 7 (Migliore et al. 2008) | |
| NEURON files from the paper: Single neuron binding properties and the magical number 7, by M. Migliore, G. Novara, D. Tegolo, Hippocampus, in press (2008). In an extensive series of simulations with realistic morphologies and active properties, we demonstrate how n radial (oblique) dendrites of these neurons may be used to bind n inputs to generate an output signal. The results suggest a possible neural code as the most effective n-ple of dendrites that can be used for short-term memory recollection of persons, objects, or places. Our analysis predicts a straightforward physiological explanation for the observed puzzling limit of about 7 short-term memory items that can be stored by humans. | |
| CA1 pyramidal neurons: effects of Alzheimer (Culmone and Migliore 2012) | |
| The model predicts possible therapeutic treatments of Alzheimers's Disease in terms of pharmacological manipulations of channels' kinetic and activation properties. The results suggest how and which mechanism can be targeted by a drug to restore the original firing conditions. The simulations reproduce somatic membrane potential in control conditions, when 90% of membrane is affected by AD (Fig.4A of the paper), and after treatment (Fig.4B of the paper). | |
| 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. | |
| CA1 pyramidal neurons: effects of a Kv7.2 mutation (Miceli et al. 2009) | |
| NEURON mod files from the paper: Miceli et al, Neutralization of a unique, negatively-charged residue in the voltage sensor of K(V)7.2 subunits in a sporadic case of benign familial neonatal seizures, Neurobiol Dis., in press (2009). In this paper, the model revealed that the gating changes introduced by a mutation in K(v)7.2 genes encoding for the neuronal KM current in a case of benign familial neonatal seizures, increased cell firing frequency, thereby triggering the neuronal hyperexcitability which underlies the observed neonatal epileptic condition. | |
| CA1 stratum radiatum interneuron multicompartmental model (Katona et al. 2011) | |
| The model examines dendritic NMDA-spike generation and propagation in the dendrites of CA1 stratum radiatum interneurons. It contains NMDA-channels in a clustered pattern on a dendrite and K-channels. The simulation shows the whole NMDA spike and the rising phase of the traces in separate windows. | |
| CA3 Pyramidal Neuron (Migliore et al 1995) | |
| Model files from the paper: M. Migliore, E. Cook, D.B. Jaffe, D.A. Turner and D. Johnston, Computer simulations of morphologically reconstructed CA3 hippocampal neurons, J. Neurophysiol. 73, 1157-1168 (1995). Demonstrates how the same cell could be bursting or non bursting according to the Ca-independent conductance densities. Includes calculation of intracellular Calcium. Instructions are provided in the below README file. Contact michele.migliore@pa.ibf.cnr.it if you have any questions about the implementation of the model. | |
| CA3 pyramidal cell: rhythmogenesis in a reduced Traub model (Pinsky, Rinzel 1994) | |
| Fig. 2A and 3 are reproduced in this simulation of Pinsky PF, Rinzel J (1994). | |
| CA3 pyramidal neuron (Lazarewicz et al 2002) | |
| The model shows how using a CA1-like distribution of active dendritic conductances in a CA3 morphology results in dendritic initiation of spikes during a burst. | |
| 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. | |
| CA3 pyramidal neuron: firing properties (Hemond et al. 2008) | |
| In the paper, this model was used to identify how relative differences in K+ conductances, specifically KC, KM, & KD, between cells contribute to the different characteristics of the three types of firing patterns observed experimentally. | |
| CN Octopus Cell: Ih current (Bal, Oertel 2000) | |
| NEURON mod files for the Ih current from the paper R. Bal and D. Oertel Hyperpolarization-Activated, Mixed-Cation Current (Ih) in Octopus Cells of the Mammalian Cochlear Nucleus, J. Neurophysiol. 84, 806-817 (2000). Contact michele.migliore@pa.ibf.cnr.it if you have any questions about the implementation of the model. | |
| CN bushy, stellate neurons (Rothman, Manis 2003) | |
| Using kinetic data from three different K+ currents in acutely isolated neurons, a single electrical compartment model representing the soma of a ventral cochlear nucleus (VCN) neuron was created. The K+ currents include a fast transient current (IA), a slow-inactivating low-threshold current (ILT), and a noninactivating high-threshold current (IHT). The model also includes a fast-inactivating Na+ current, a hyperpolarization-activated cation current (Ih), and 1-50 auditory nerve synapses. With this model, the role IA, ILT, and IHT play in shaping the discharge patterns of VCN cells is explored. Simulation results indicate these currents have specific roles in shaping the firing patterns of stellate and bushy CN cells. (see readme.txt and the papers, esp 2003c, for details). Any questions regarding these implementations should be directed to: pmanis@med.unc.edu 2 April 2004 Paul B Manis, Ph.D. | |
| CN pyramidal fusiform cell (Kanold, Manis 2001) | |
| Pyramidal cells in the dorsal cochlear nucleus (DCN) show three characteristic discharge patterns in response tones: pauser, buildup, and regular firing. Experimental evidence suggests that a rapidly inactivating K+ current (I(KIF)) plays a critical role in generating these discharge patterns. To explore the role of I(KIF), we used a computational model based on the biophysical data. The model replicated the dependence of the discharge pattern on the magnitude and duration of hyperpolarizing prepulses, and I(KIF) was necessary to convey this dependence. Experimentally, half-inactivation voltage and kinetics of I(KIF) show wide variability. Varying these parameters in the model ... suggests that pyramidal cells can adjust their sensitivity to different temporal patterns of inhibition and excitation by modulating the kinetics of I(KIF). Overall, I(KIF) is a critical conductance controlling the excitability of DCN pyramidal cells. (See readme.txt and paper for details). Any questions regarding these implementations should be directed to: pmanis@med.unc.edu 2 April 2004 Paul B Manis, Ph.D. | |
| Ca-dependent K Channel: kinetics from rat muscle (Moczydlowski, Latorre 1983) NEURON | |
| Macroscopic channel model based on Moczydlowski, E. and Latorre, R. (1983). Gating kinetics of Ca++ activated K+ channels from rat muscle incorporated into planar lipid bilayers. J. Gen. Physiol. 82: 511-542 See README file for more information. | |
| Ca1 pyramidal neuron: reduction model (Marasco et al. 2012) | |
| "... Here we introduce a new, automatic and fast method to map realistic neurons into equivalent reduced models running up to >40 times faster while maintaining a very high accuracy of the membrane potential dynamics during synaptic inputs, and a direct link with experimental observables. The mapping of arbitrary sets of synaptic inputs, without additional fine tuning, would also allow the convenient and efficient implementation of a new generation of large-scale simulations of brain regions reproducing the biological variability observed in real neurons, with unprecedented advances to understand higher brain functions." | |
| 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. | |
| Calcium and potassium currents of olfactory bulb juxtaglomerular cells (Masurkar and Chen 2011) | |
| Inward and outward currents of the olfactory bulb juxtaglomerular cells are characterized in the experiments and modeling in these two Masurkar and Chen 2011 papers. | |
| Calcium spikes in basal dendrites (Kampa and Stuart 2006) | |
| This model was published in Kampa & Stuart (2006) J Neurosci 26(28):7424-32. The simulation creates two plots showing voltage and calcium changes in basal dendrites of layer 5 pyramidal neurons during action potential backpropagation. created by B. Kampa (2006) | |
| Calcium waves in neuroblastoma cells (Fink et al. 2000) | |
| Uses a model of IP3-mediated release of Ca from endoplasmic reticulum (ER) to study how initiation and propagation of Ca waves are affected by cell geometry, spatial distributions of ER and IP3 generation, and diffusion of Ca and mobile buffer. | |
| Carbon nanotubes as electrical interfaces to neurons (Giugliano et al. 2008) | |
| In the present NEURON model, we explore simple phenomenological models of the extracellular coupling, occurring at the neuron-metal microelectrode junction and (possibly) at the neuron-carbon nanotube junction. | |
| Cardiac Atrial Cell (Courtemanche et al 1998) | |
| Marc Courtemanche, Rafael J. Ramirez, and Stanley Nattel. Ionic mechanisms underlying human atrial action potential properties insights from a mathematical model Am J Physiol Heart Circ Physiol 1998 275: H301-H321. The implementation of this model in NEURON was contributed by Ingemar Jacobson. | |
| Cardiac action potentials and pacemaker activity of sinoatrial node (DiFrancesco & Noble 1985) | |
| "Equations have been developed to describe cardiac action potentials and pacemaker activity. The model takes account of extensive developments in experimental work ..." | |
| Cardiac sarcomere dynamics (Negroni and Lascano 1996) | |
| "A muscle model establishing the link between cross-bridge dynamics and intracellular Ca2+ kinetics was assessed by simulation of experiments performed in isolated cardiac muscle. The model is composed by the series arrangement of muscle units formed by inextensible thick and thin filaments in parallel with an elastic element. Attached cross-bridges act as independent force generators whose force is linearly related to the elongation of their elastic structure. Ca2+ kinetics is described by a four-state system of sites on the thin filament associated with troponin C: sites with free troponin C (T), sites with Ca2+ bound to troponin C (TCa); sites with Ca2+ bound to troponin C and attached cross-bridges (TCa*); and sites with troponin C not associated with Ca2+ and attached cross-bridges (T*). The intracellular Ca2+ concentration ([Ca2+]) is controlled solely by the sarcoplasmic reticulum through an inflow function and a saturated outflow pump function. ..." | |
| Cell splitting in neural networks extends strong scaling (Hines et al. 2008) | |
| Neuron tree topology equations can be split into two subtrees and solved on different processors with no change in accuracy, stability, or computational effort; communication costs involve only sending and receiving two double precision values by each subtree at each time step. Application of the cell splitting method to two published network models exhibits good runtime scaling on twice as many processors as could be effectively used with whole-cell balancing. | |
| Cerebellar Golgi cell (Solinas et al. 2007a, 2007b) | |
| "... Our results suggest that a complex complement of ionic mechanisms is needed to fine-tune separate aspects of the neuronal response dynamics. Simulations also suggest that the Golgi cell may exploit these mechanisms to obtain a fine regulation of timing of incoming mossy fiber responses and granular layer circuit oscillation and bursting." | |
| Cerebellar Purkinje Cell: resurgent Na current and high frequency firing (Khaliq et al 2003) | |
| These mod files supplied by Dr Raman are for the below two references. ... we modeled action potential firing by simulating eight currents directly recorded from Purkinje cells in both wild-type and (mutant) med mice. Regular, high-frequency firing was slowed in med Purkinje neurons. In addition to disrupted sodium currents, med neurons had small but significant changes in potassium and leak currents. Simulations indicated that these modified non-sodium currents could not account for the reduced excitability of med cells but instead slightly facilitated spiking. The loss of NaV1.6-specific kinetics, however, slowed simulated spontaneous activity. Together, the data suggest that across a range of conditions, sodium currents with a resurgent component promote and accelerate firing. See papers for more and details. | |
| Cerebellar cortex oscil. robustness from Golgi cell gap jncs (Simoes de Souza and De Schutter 2011) | |
| " ... Previous one-dimensional network modeling of the cerebellar granular layer has been successfully linked with a range of cerebellar cortex oscillations observed in vivo. However, the recent discovery of gap junctions between Golgi cells (GoCs), which may cause oscillations by themselves, has raised the question of how gap-junction coupling affects GoC and granular-layer oscillations. To investigate this question, we developed a novel two-dimensional computational model of the GoC-granule cell (GC) circuit with and without gap junctions between GoCs. ..." | |
| Cerebellar purkinje cell: K and Ca channels regulate APs (Miyasho et al 2001) | |
| We adopted De Schutter and Bower's model as the starting point, then modified the descriptions of several ion channels, such as the P-type Ca channel and the delayed rectifier K channel, and added class-E Ca channels and D-type K channels to the model. Our new model reproduces most of our experimental results and supports the conclusions of our experimental study that class-E Ca channels and D-type K channels are present and functioning in the dendrites of Purkinje neurons. | |
| Cerebellar purkinje cell: interacting Kv3 and Na currents influence firing (Akemann, Knopfel 2006) | |
| Purkinje neurons spontaneously generate action potentials in the absence of synaptic drive and thereby exert a tonic, yet plastic, input to their target cells in the deep cerebellar nuclei. Purkinje neurons express two ionic currents with biophysical properties that are specialized for high-frequency firing: resurgent sodium currents and potassium currents mediated by Kv3.3. … Numerical simulations indicated that Kv3.3 increases the spontaneous firing rate via cooperation with resurgent sodium currents. We conclude that the rate of spontaneous action potential firing of Purkinje neurons is controlled by the interaction of Kv3.3 potassium currents and resurgent sodium currents. See paper for more and details. | |
| Chirp stimulus responses in a morphologically realistic model (Narayanan and Johnston, 2007) | |
| ...we built a multicompartmental model with a morphologically realistic three-dimensional reconstruction of a CA1 pyramidal neuron. The only active conductance we added to the model was the h conductance. ... We conclude that experimentally observed gradient in density of h channels could theoretically account for experimentally observed gradient in resonance properties (Narayanan and Johnston, 2007). | |
| Classic model of the Tritonia Swim CPG (Getting, 1989) | |
| Classic model developed by Petter Getting of the 3-cell core CPG (DSI, C2, and VSI-B) mediating escape swimming in Tritonia diomedea. Cells use a hybrid integrate-and-fire scheme pioneered by Peter Getting. Each model cell is reconstructed from extensive physiological measurements to precisely mimic I-F curves, synaptic waveforms, and functional connectivity. **However, continued physiological measurements show that Getting may have inadvertently incorporated modulatory and or polysynaptic effects -- the properties of this model do *not* match physiological measurements in rested preparations.** This simulation reconstructs the Getting model as reported in: Getting (1989) 'Reconstruction of small neural networks' In Methods in Neural Modeling, 1st ed, p. 171-196. See also, an earlier version of this model reported in Getting (1983). Every attempt has been made to replicate the 1989 model as precisely as possible. | |
| Coincidence detection in avian brainstem (Simon et al 1999) | |
| A detailed biophysical model of coincidence detector neurons in the nucleus laminaris (auditory brainstem) which are purported to detect interaural time differences (ITDs) from Simon et al 1999. | |
| Compartmental model of a mitral cell (Popovic et al. 2005) | |
| Usage of a morphologically realistic compartmental model of a mitral cell and data obtained from whole-cell patch-clamp and voltage-imaging experiments in order to explore passive parameter space in which reported low EPSP attenuation is observed. | |
| Compartmentalization of GABAergic inhibition by dendritic spines (Chiu et al. 2013) | |
| A spiny dendrite model supports the hypothesis that only inhibitory inputs on spine heads, not shafts, compartmentalizes inhibition of calcium signals to spine heads as seen in paired inhibition with back-propagating action potential experiments on prefrontal cortex layer 2/3 pyramidal neurons in mouse (Chiu et al. 2013). | |
| Competition for AP initiation sites in a circuit controlling simple learning (Cruz et al. 2007) | |
| "The spatial and temporal patterns of action potential initiations were studied in a behaving leech preparation to determine the basis of increased firing that accompanies sensitization, a form of non-associative learning requiring the S-interneurons. ... The S-interneurons, one in each ganglion and linked by electrical synapses with both neighbors to form a chain, are interposed between sensory and motor neurons. ... the single site with the largest initiation rate, the S-cell in the stimulated segment, suppressed initiations in adjacent ganglia. Experiments showed this was both because (1) it received the earliest, greatest input and (2) the delayed synaptic input to the adjacent S-cells coincided with the action potential refractory period. A compartmental model of the S-cell and its inputs showed that a simple, intrinsic mechanism of inexcitability after each action potential may account for suppression of impulse initiations. Thus, a non-synaptic competition between neurons alters synaptic integration in the chain. In one mode, inputs to different sites sum independently, whereas in another, synaptic input to a single site precisely specifies the overall pattern of activity." | |
| Complex CA1-neuron to study AP initiation (Wimmer et al. 2010) | |
| Complex model of a pyramidal CA1-neuron, adapted from Royeck, M., et al. Role of axonal NaV1.6 sodium channels in action potential initiation of CA1 pyramidal neurons. Journal of neurophysiology 100, 2361-2380 (2008). It contains a biophysically realistic morphology comprising 265 compartments (829 segments) and 15 different distributed Ca2+- and/or voltage-dependent conductances. | |
| Computational Surgery (Lytton et al. 2011) | |
| Figure 2 in Neocortical simulation for epilepsy surgery guidance: Localization and intervention, by William W. Lytton, Samuel A. Neymotin, Jason C. Wester, and Diego Contreras in Computational Surgery and Dual Training, Springer, 2011 | |
| 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) | |
| 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. | |
| Computer simulations of neuron-glia interactions mediated by ion flux (Somjen et al. 2008) | |
| "... To examine the effect of glial K+ uptake, we used a model neuron equipped with Na+, K+, Ca2+ and Cl− conductances, ion pumps and ion exchangers, surrounded by interstitial space and glia. The glial membrane was either “passive”, incorporating only leak channels and an ion exchange pump, or it had rectifying K+ channels. We computed ion fluxes, concentration changes and osmotic volume changes. ... We conclude that voltage gated K+ currents can boost the effectiveness of the glial “potassium buffer” and that this buffer function is important even at moderate or low levels of excitation, but especially so in pathological states." | |
| Conditions of dominant effectiveness of distal dendrites (Korogod, Kulagina 1998) | |
| The model illustrates and explains bistable spatial patterns of the current transfer effectiveness in the active dendrite with distributed (multiple) tonic excitatory, NMDA type, synaptic input. | |
| Conduction in uniform myelinated axons (Moore et al 1978) | |
| Examines the relative sensitivity of the velocity of impulse propagation to changes in nodal and internodal parameters. | |
| Contrast invariance by LGN synaptic depression (Banitt et al. 2007) | |
| "Simple cells in layer 4 of the primary visual cortex of the cat show contrast-invariant orientation tuning, in which the amplitude of the peak response is proportional to the stimulus contrast but the width of the tuning curve hardly changes with contrast. This study uses a detailed model of spiny stellate cells (SSCs) from cat area 17 to explain this property. The model integrates our experimental data, including morphological and intrinsic membrane properties and the number and spatial distribution of four major synaptic input sources of the SSC: the dorsal lateral geniculate nucleus (dLGN) and three cortical sources. ... The model response is in close agreement with experimental results, in terms of both output spikes and membrane voltage (amplitude and fluctuations), with reasonable exceptions given that recurrent connections were not incorporated." | |
| Controlling KCa channels with different Ca2+ buffering models in Purkinje cell (Anwar et al. 2010) | |
| In this work, we compare the dynamics of different buffering models during generation of a dendritic Ca2+ spike in a single compartment model of a Purkinje cell dendrite. The Ca2+ buffering models used are 1) a single Ca2+ pool, 2) two Ca2+ pools respectively for the fast and slow transients, 3) a detailed calcium model with buffers, pump (Schmidt et al., 2003), and diffusion and 4) a calcium model with buffers, pump and diffusion compensation. The parameters of single pool and double pool are tuned, using Neurofitter (Van Geit et al., 2007), to approximate the behavior of detailed calcium dynamics over range of 0.5 µM to 8 µM of intracellular calcium. The diffusion compensation is modeled using a buffer-like mechanism called DCM. To use DCM robustly for different diameter compartments, its parameters are estimated, using Neurofitter (Van Geit et al., 2007), as a function of compartment diameter (0.8 µm-20 µm). | |
| Correcting space clamp in dendrites (Schaefer et al. 2003 and 2007) | |
| In voltage-clamp experiments, incomplete space clamp distorts the recorded currents, rendering accurate analysis impossible. Here, we present a simple numerical algorithm that corrects such distortions. The method enabled accurate retrieval of the local densities, kinetics, and density gradients of somatic and dendritic channels. The correction method was applied to two-electrode voltage-clamp recordings of K currents from the apical dendrite of layer 5 neocortical pyramidal neurons. The generality and robustness of the algorithm make it a useful tool for voltage-clamp analysis of voltage-gated currents in structures of any morphology that is amenable to the voltage-clamp technique. | |
| 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. | |
| Current flow during PAP in squid axon at diameter change (Joyner et al 1980) | |
| From the paper abstract: An impulse ... sees an increased electrical load at regions of increasing diameter or at branch points with certain morphologies. We present here theoretical and experimental studies on the changes in membrane current and axial current associated with diameter changes. The theoretical studies were done with numerical solutions for cable equations that were generalized to include a varying diameter; the Hodgkin-Huxley equations were used to represent the membrane properties. ... As an action potential approaches a region of increased electrical load, the action potential amplitude and rate of rise decrease, but there is a marked increase in the magnitude of the inward sodium current. ... (See paper for more details.) | |
| D2 dopamine receptor modulation of interneuronal activity (Maurice et al. 2004) | |
| "... Using a combination of electrophysiological, molecular, and computational approaches, the studies reported here show that D2 dopamine receptor modulation of Na+ currents underlying autonomous spiking contributes to a slowing of discharge rate, such as that seen in vivo. Four lines of evidence support this conclusion. ... Fourth, simulation of cholinergic interneuron pacemaking revealed that a modest increase in the entry of Na+ channels into the slow-inactivated state was sufficient to account for the slowing of pacemaker discharge. These studies establish a cellular mechanism linking dopamine and the reduction in striatal cholinergic interneuron activity seen in the initial stages of associative learning." See paper for more and details. | |
| DG granule cell: I-A model (Beck et al 1992) | |
| NEURON mod files for the I-A current from the paper: Beck H, Ficker E, Heinemann U. Properties of two voltage-activated potassium currents in acutely isolated juvenile rat dentate gyrus granule cells. J. Neurophysiol. 68, 2086-2099 (1992) Contact michele.migliore@pa.ibf.cnr.it if you have any questions about the implementation of the model. | |
| Demyelinated and remyelinating axon conductances (Hines, Shrager 1991) | |
| Hines, Michael and Peter Shrager (1991). A computational test of the requirements for conduction in demyelinated axons. J. Restorative Neurology and Neuroscience. 3 81--93. | |
| 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. | |
| Dendritic L-type Ca currents in motoneurons (Carlin et al 2000) | |
| A component of recorded currents demonstrated kinetics consistent with a current originating at a site spatially segregated from the soma. In response to step commands this component was seen as a late-onset, low amplitude persistent current whilst in response to depolarizing-repolarizing ramp commands a low voltage clockwise current hysteresis was recorded. Simulations using a neuromorphic motoneuron model could reproduce these currents only if a noninactivating calcium conductance was placed in the dendritic compartments. | |
| Dendritic Na+ spike initiation and backpropagation of APs in active dendrites (Nevian et al. 2007) | |
| NEURON model used to create simulations shown in figure 6 of the paper. The model includes two point processes; one for dendritic spike initiation and the other for somatic action potential generation. The effect of filtering by imperfect recording electrode can be examined in somatic and dendritic locations. | |
| 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. | |
| Dendritic tip geometry effects electrical properties (Tsutsui, Oka 2001) | |
| In their teleost thalamic neuron models the authors demonstrate a dramatic increase in the passive propagation of synaptic inputs through the dendritic stalk to the soma in cells with larger tips. | |
| Dendro-dendritic synaptic circuit (Shepherd Brayton 1979) | |
| A NEURON simulation has been created to model the passive spread of an EPSP from a mitral cell synapse on a granule cell spine. The EPSP was shown to propagate subthreshold through the dendritic shaft into an adjacent spine with significant amplitude (figure 2B). | |
| 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. | |
| 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. | |
| Dentate gyrus (Morgan et al. 2007, 2008, Santhakumar et al. 2005, Dyhrfjeld-Johnsen et al. 2007) | |
| This model was implemented by Rob Morgan in the Soltesz lab at UC Irvine. It is a scaleable model of the rat dentate gyrus including four cell types. This model runs in serial (on a single processor) and has been published at the size of 50,000 granule cells (with proportional numbers of the other cells). | |
| Dentate gyrus granule cell: calcium and calcium-dependent conductances (Aradi and Holmes 1999) | |
| We have constructed a detailed model of a hippocampal dentate granule (DG) cell that includes nine different channel types. Channel densities and distributions were chosen to reproduce reported physiological responses observed in normal solution and when blockers were applied. The model was used to explore the contribution of each channel type to spiking behavior with particular emphasis on the mechanisms underlying postspike events. ... The model was used to predict changes in channel densities that could lead to epileptogenic burst discharges and to predict the effect of altered buffering capacity on firing behavior. We conclude that the clustered spatial distributions of calcium related channels, the presence of slow delayed rectifier potassium currents in dendrites, and calcium buffering properties, together, might explain the resistance of DG cells to the development of epileptogenic burst discharges. | |
| 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. | |
| Dentate gyrus network model (Santhakumar et al 2005) | |
| Mossy cell loss and mossy fiber sprouting are two characteristic consequences of repeated seizures and head trauma. However, their precise contributions to the hyperexcitable state are not well understood. Because it is difficult, and frequently impossible, to independently examine using experimental techniques whether it is the loss of mossy cells or the sprouting of mossy fibers that leads to dentate hyperexcitability, we built a biophysically realistic and anatomically representative computational model of the dentate gyrus to examine this question. The 527-cell model, containing granule, mossy, basket, and hilar cells with axonal projections to the perforant-path termination zone, showed that even weak mossy fiber sprouting (10-15% of the strong sprouting observed in the pilocarpine model of epilepsy) resulted in the spread of seizure-like activity to the adjacent model hippocampal laminae after focal stimulation of the perforant path. See reference for more and details. | |
| Detailed passive cable model of Dentate Gyrus Basket Cells (Norenberg et al. 2010) | |
| Fast-spiking, parvalbumin-expressing basket cells (BCs) play a key role in feedforward and feedback inhibition in the hippocampus. ... To quantitatively address this question, we developed detailed passive cable models of BCs in the dentate gyrus based on dual somatic or somatodendritic recordings and complete morphologic reconstructions. Both specific membrane capacitance and axial resistivity were comparable to those of pyramidal neurons, but the average somatodendritic specific membrane resistance (R(m)) was substantially lower in BCs. Furthermore, R(m) was markedly nonuniform, being lowest in soma and proximal dendrites, intermediate in distal dendrites, and highest in the axon. ... Further computational analysis revealed that these unique cable properties accelerate the time course of synaptic potentials at the soma in response to fast inputs, while boosting the efficacy of slow distal inputs. These properties will facilitate both rapid phasic and efficient tonic activation of BCs in hippocampal microcircuits. | |
| Differential modulation of pattern and rate in a dopamine neuron model (Canavier and Landry 2006) | |
| "A stylized, symmetric, compartmental model of a dopamine neuron in vivo shows how rate and pattern can be modulated either concurrently or differentially. If two or more parameters in the model are varied concurrently, the baseline firing rate and the extent of bursting become decorrelated, which provides an explanation for the lack of a tight correlation in vivo and is consistent with some independence of the mechanisms that generate baseline firing rates versus bursting. ..." See paper for more and details. | |
| Direct recruitment of S1 interneurons via ICMS (Overstreet and Helms Tillery, submitted) | |
| Study of the interneurons recruited by intracortical microstimulation in primary somatosensory cortex. Code includes morphological models for eight types of interneurons, NEURON code to simulate ICMS, and an artificial reconstruction of a 3D slab of cortex implemented in MATLAB. | |
| Direct recruitment of S1 pyramidal neurons via ICMS (Overstreet et al., submitted) | |
| Study of the pyramidal neurons recruited by intracortical microstimulation in primary somatosensory cortex. Code includes morphological models for seven types of pyramidal neurons, NEURON code to simulate ICMS, and an artificial reconstruction of a 3D slab of cortex implemented in MATLAB. | |
| Discrete event simulation in the NEURON environment (Hines and Carnevale 2004) | |
| A short introduction to how "integrate and fire" cells are implemented in NEURON. Network simulations that use only artificial spiking cells are extremely efficient, with runtimes proportional to the total number of synaptic inputs received and independent of the number of cells or problem time. | |
| Dorsal root ganglion (DRG) neuronal model (Amir, Devor 2003) | |
| The model shows that an electrically excitable soma is not necessary for spike through-conduction in the t-shaped geometry of a dorsal root ganglion neuron axon. Electrical excitability of the soma is required, however, for soma spike invasion. See papers for details and more. | |
| Dorsal root ganglion (DRG) neuronal model (Kovalsky et al. 2009) | |
| This model, diverged from oscillatory parameters seen in live cells and failed to produce characteristic ectopic discharge patterns. Here we show that use of a more complete set of Na+ conductances--which includes several delayed components--enables simulation of the entire repertoire of oscillation-triggered electrogenic phenomena seen in live dorsal root ganglion (DRG) neurons. This includes a physiological window of induction and natural patterns of spike discharge. An INa+ component at 2-20 ms was particularly important, even though it represented only a tiny fraction of overall INa+ amplitude. With the addition of a delayed rectifier IK+ the singlet firing seen in some DRG neurons can also be simulated. The model reveals the key conductances that underlie afferent ectopia, conductances that are potentially attractive targets in the search for more effective treatments of neuropathic pain. | |
| Drosophila projection neuron electrotonic structure (Gouwens and Wilson 2009) | |
| We address the issue of how electrical signals propagate in Drosophila neurons by modeling the electrotonic structure of the antennal lobe projection neurons innervating glomerulus DM1. The readme file contains instructions for running the model. | |
| 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). | |
| Dynamical model of olfactory bulb mitral cell (Rubin, Cleland 2006) | |
| This four-compartment mitral cell exhibits endogenous subthreshold oscillations, phase resetting, and evoked spike phasing properties as described in electrophysiological studies of mitral cells. It is derived from the prior work of Davison et al (2000) and Bhalla and Bower (1993). See readme.txt for details. | |
| Effect of voltage sensitive fluorescent proteins on neuronal excitability (Akemann et al. 2009) | |
| "Fluorescent protein voltage sensors are recombinant proteins that are designed as genetically encoded cellular probes of membrane potential using mechanisms of voltage-dependent modulation of fluorescence. Several such proteins, including VSFP2.3 and VSFP3.1, were recently reported with reliable function in mammalian cells. ... Expression of these proteins in cell membranes is accompanied by additional dynamic membrane capacitance, ... We used recordings of sensing currents and fluorescence responses of VSFP2.3 and of VSFP3.1 to derive kinetic models of the voltage-dependent signaling of these proteins. Using computational neuron simulations, we quantitatively investigated the perturbing effects of sensing capacitance on the input/output relationship in two central neuron models, a cerebellar Purkinje and a layer 5 pyramidal neuron. ... ". The Purkinje cell model is included in ModelDB. | |
| 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. ..." | |
| Effects of KIR current inactivation in NAc Medium Spiny Neurons (Steephen and Manchanda 2009) | |
| "Inward rectifying potassium (KIR) currents in medium spiny (MS) neurons of nucleus accumbens inactivate significantly in ~40% of the neurons but not in the rest, which may lead to differences in input processing by these two groups. Using a 189-compartment computational model of the MS neuron, we investigate the influence of this property using injected current as well as spatiotemporally distributed synaptic inputs. Our study demonstrates that KIR current inactivation facilitates depolarization, firing frequency and firing onset in these neurons. ..." | |
| Effects of synaptic location and timing on synaptic integration (Rall 1964) | |
| Reproduces figures 5 - 8 from Rall, W. Theoretical significance of dendritic trees for neuronal input-output relations. In: Neural Theory and Modeling, ed. Reiss, R.F., Palo Alto: Stanford University Press (1964). | |
| Effects of the membrane AHP on the Lateral Superior Olive (LSO) (Zhou & Colburn 2010) | |
| This simulation study investigated how membrane afterhyperpolarization (AHP) influences spiking activity of neurons in the Lateral Superior Olive (LSO). The model incorporates a general integrate-and-fire spiking mechanism with a first-order adaptation channel. Simulations focus on differentiating the effects of GAHP, tauAHP, and input strength on (1) spike interval statistics, such as negative serial correlation and chopper onset, and (2) neural sensitivity to interaural level difference (ILD) of LSO neurons. The model simulated electrophysiological data collected in cat LSO (Tsuchitani and Johnson, 1985). | |
| 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 | |
| Electrically-coupled Retzius neurons (Vazquez et al. 2009) | |
| "Dendritic electrical coupling increases the number of effective synaptic inputs onto neurons by allowing the direct spread of synaptic potentials from one neuron to another. Here we studied the summation of excitatory postsynaptic potentials (EPSPs) produced locally and arriving from the coupled neuron (transjunctional) in pairs of electrically-coupled Retzius neurons of the leech. We combined paired recordings of EPSPs, the production of artificial EPSPs (APSPs) in neuron pairs with different coupling coefficients and simulations of EPSPs produced in the coupled dendrites. ..." | |
| 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. ..." | |
| Encoding and retrieval in a model of the hippocampal CA1 microcircuit (Cutsuridis et al. 2009) | |
| This NEURON code implements a small network model (100 pyramidal cells and 4 types of inhibitory interneuron) of storage and recall of patterns in the CA1 region of the mammalian hippocampus. Patterns of PC activity are stored either by a predefined weight matrix generated by Hebbian learning, or by STDP at CA3 Schaffer collateral AMPA synapses. | |
| Engaging distinct oscillatory neocortical circuits (Vierling-Claassen et al. 2010) | |
| "Selective optogenetic drive of fast-spiking (FS) interneurons (INs) leads to enhanced local field potential (LFP) power across the traditional “gamma” frequency band (20–80 Hz; Cardin et al., 2009). In contrast, drive to regular-spiking (RS) pyramidal cells enhances power at lower frequencies, with a peak at 8 Hz. The first result is consistent with previous computational studies emphasizing the role of FS and the time constant of GABAA synaptic inhibition in gamma rhythmicity. However, the same theoretical models do not typically predict low-frequency LFP enhancement with RS drive. To develop hypotheses as to how the same network can support these contrasting behaviors, we constructed a biophysically principled network model of primary somatosensory neocortex containing FS, RS, and low-threshold spiking (LTS) INs. ..." | |
| Ephaptic interactions in olfactory nerve (Bokil et al 2001) | |
| Bokil, H., Laaris, N., Blinder, K., Ennis, M., and Keller, A. (2001) Ephaptic interactions in the mammalian olfactory system. J. Neurosci. 21:RC173(1-5) | |
| Estimation and Production of Time Intervals (Migliore et al 2001) | |
| NEURON model files from the paper M. Migliore, L. Messineo, M. Cardaci, G.F. Ayala, Quantitative modeling of perception and production of time intervals, J.Neurophysiol. 86, 2754-2760 (2001). Contact michele.migliore@pa.ibf.cnr.it if you have any questions about the implementation of the model. | |
| Excitability of PFC Basal Dendrites (Acker and Antic 2008) | |
| ".. We carried out multi-site voltage-sensitive dye imaging of membrane potential transients from thin basal branches of prefrontal cortical pyramidal neurons before and after application of channel blockers. We found that backpropagating action potentials (bAPs) are predominantly controlled by voltage-gated sodium and A-type potassium channels. In contrast, pharmacologically blocking the delayed rectifier potassium, voltage-gated calcium or Ih, conductance had little effect on dendritic action potential propagation. Optically recorded bAP waveforms were quantified and multicompartmental modeling (NEURON) was used to link the observed behavior with the underlying biophysical properties. The best-fit model included a non-uniform sodium channel distribution with decreasing conductance with distance from the soma, together with a non-uniform (increasing) A-type potassium conductance. AP amplitudes decline with distance in this model, but to a lesser extent than previously thought. We used this model to explore the mechanisms underlying two sets of published data involving high frequency trains of action potentials, and the local generation of sodium spikelets. ..." | |
| Excitability of the soma in central nervous system neurons (Safronov et al 2000) | |
| The ability of the soma of a spinal dorsal horn neuron, a spinal ventral horn neuron, and a hippocampal pyramidal neuron to generate action potentials was studied using experiments and computer simulations. By comparing recordings ... of a dorsal horn neuron with simulated responses, it was shown that computer models can be adequate for the study of somatic excitability. The modeled somata of both spinal neurons were unable to generate action potentials, showing only passive and local responses to current injections. ... In contrast to spinal neurons, the modeled soma of the hippocampal pyramidal neuron generated spikes with an overshoot of +9 mV. It is concluded that the somata of spinal neurons cannot generate action potentials and seem to resist their propagation from the axon to dendrites. ... See paper for more and details. | |
| Excitatory and inhibitory interactions in populations of model neurons (Wilson and Cowan 1972) | |
| Coupled nonlinear differential equations are derived for the dynamics of spatially localized populations containing both excitatory and inhibitory model neurons. Phase plane methods and numerical solutions are then used to investigate population responses to various types of stimuli. The results obtained show simple and multiple hysteresis phenomena and limit cycle activity. The latter is particularly interesting since the frequency of the limit cycle oscillation is found to be a monotonic function of stimulus intensity. Finally, it is proved that the existence of limit cycle dynamics in response to one class of stimuli implies the existence of multiple stable states and hysteresis in response to a different class of stimuli. The relation between these findings and a number of experiments is discussed. | |
| Extracellular Action Potential Simulations (Gold et al 2007) | |
| This package recreates the the principal experiments described in (Gold, Henze and Koch, 2007) and includes the core code necessary to create your own Extracellular Action Potential Simulations. | |
| Facilitation by residual calcium (Stockbridge, Hines 1982) | |
| The residual calcium hypothesis is compatible with facilitation of transmitter release from the neuromuscular junction. | |
| 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. | |
| Fast sodium channel gating in mossy fiber axons (Schmidt-Heiber et al. 2010) | |
| "... To study the mechanisms underlying AP initiation in unmyelinated hippocampal mossy fibers of adult mice, we recorded sodium currents in axonal and somatic membrane patches. We demonstrate that sodium channel density in the proximal axon is ~5 times higher than in the soma. Furthermore, sodium channel activation and inactivation are ~2 times faster. Modeling revealed that the fast activation localized the initiation site to the proximal axon even upon strong synaptic stimulation, while fast inactivation contributed to energy-efficient membrane charging during APs. ..." | |
| Fast-spiking cortical interneuron (Golomb et al. 2007) | |
| Cortical fast-spiking (FS) interneurons display highly variable electrophysiological properties. We hypothesize that this variability emerges naturally if one assumes a continuous distribution of properties in a small set of active channels. We construct a minimal, single-compartment conductance-based model of FS cells that includes transient Na+, delayed-rectifier K+, and slowly inactivating d-type K+ conductances. The model may display delay to firing. Stuttering (elliptic bursting) and subthreshold oscillations may be observed for small Na+ window current. | |
| Febrile seizure-induced modifications to Ih (Chen et al 2001) | |
| Modeling and experiments in the paper Chen K,Aradi I, Thom N,Eghbal-Ahmadi M, Baram TZ, and Soltesz I (2001) support the hypothesis that modified Ih currents strongly influence inhibitory inputs in CA1 cells and that the depolarizing shift in Ih activation plays a primary role in this process. Please see the paper for details. Some modeling details are available at http://www.ucihs.uci.edu/anatomy/soltesz/supp.htm Correspondance should be addressed to isoltesz@uci.edu (modeling was done by Ildiko Aradi, iaradi@uci.edu) | |
| Feedforward heteroassociative network with HH dynamics (Lytton 1998) | |
| Using the original McCulloch-Pitts notion of simple on and off spike coding in lieu of rate coding, an Anderson-Kohonen artificial neural network (ANN) associative memory model was ported to a neuronal network with Hodgkin-Huxley dynamics. | |
| Fluctuating synaptic conductances recreate in-vivo-like activity (Destexhe et al 2001) | |
| This model (and experiments) reported in Destexhe, Rudolh, Fellous, and Sejnowski (2001) support the hypothesis that many of the characteristics of cortical neurons in vivo can be explained by fast glutamatergic and GABAergic conductances varying stochastically. Some of these cortical neuron characteristics of fluctuating synaptic origin are a depolarized membrane potential, the presence of high-amplitude membrane potential fluctuations, a low input resistance and irregular spontaneous firing activity. In addition, the point-conductance model could simulate the enhancement of responsiveness due to background activity. For more information please contact Alain Destexhe. email: Destexhe@iaf.cnrs-gif.fr | |
| Fly lobular plate VS cell (Borst and Haag 1996, et al. 1997, et al. 1999) | |
| In a series of papers the authors conducted experiments to develop understanding and models of fly visual system HS, CS, and VS neurons. This model recreates the VS neurons from those papers with enough success to merit approval by Borst although some discrepancies remain (see readme). | |
| Four cortical interneuron subtypes (Kubota et al. 2011) | |
| " ... Using electron microscopy and serial reconstructions, we analyzed the dendritic trees of four morphologically distinct neocortical interneuron subtypes to reveal two underlying organizational principles common to all. First, cross-sectional areas at any given point within a dendrite were proportional to the summed length of all dendritic segments distal to that point. ... Second, dendritic cross-sections became progressively more elliptical at more proximal, larger diameter, dendritic locations. Finally, computer simulations revealed that these conserved morphological features limit distance dependent filtering of somatic EPSPs and facilitate distribution of somatic depolarization into all dendritic compartments. ..." | |
| Frog second-order vestibular neuron models (Rössert 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. | |
| Fronto-parietal visuospatial WM model with HH cells (Edin et al 2007) | |
| 1) J Cogn Neurosci: 3 structural mechanisms that had been hypothesized to underlie vsWM development during childhood were evaluated by simulating the model and comparing results to fMRI. It was concluded that inter-regional synaptic connection strength cause vsWM development. 2) J Integr Neurosci: Given the importance of fronto-parietal connections, we tested whether connection asymmetry affected resistance to distraction. We drew the conclusion that stronger frontal connections are benefiction. By comparing model results to EEG, we concluded that the brain indeed has stronger frontal-to-parietal connections than vice versa. | |
| Fully Implicit Parallel Simulation of Single Neurons (Hines et al. 2008) | |
| A 3-d reconstructed neuron model can be simulated in parallel on a dozen or so processors and experience almost linear speedup. Network models can be simulated when there are more processors than cells. | |
| 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." | |
| Functional structure of mitral cell dendritic tuft (Djurisic et al. 2008) | |
| The computational modeling component of Djurisic et al. 2008 addressed two primary questions: whether amplification by active currents is necessary to explain the relatively mild attenuation suffered by tuft EPSPs spreading along the primary dendrite to the soma; what accounts for the relatively uniform peak EPSP amplitude throughout the tuft. These simulations show that passive spread from tuft to soma is sufficient to yield the low attenuation of tuft EPSPs, and that random distribution of a biologically plausible number of excitatory synapses throughout the tuft can produce the experimentally observed uniformity of depolarization. | |
| GPi/GPe neuron models (Johnson and McIntyre 2008) | |
| Model files for two types of non-human primate neurons used in the paper: simplified versions of 1) a GPi neuron and 2) a GPe axon collateralizing in GPi en route to STN. | |
| Gamma oscillations in hippocampal interneuron networks (Bartos et al 2002) | |
| To examine whether an interneuron network with fast inhibitory synapses can act as a gamma frequency oscillator, we developed an interneuron network model based on experimentally determined properties. In comparison to previous interneuron network models, our model was able to generate oscillatory activity with higher coherence over a broad range of frequencies (20-110 Hz). In this model, high coherence and flexibility in frequency control emerge from the combination of synaptic properties, network structure, and electrical coupling. | |
| Gamma oscillations in hippocampal interneuron networks (Wang, Buzsaki 1996) | |
| The authors investigated the hypothesis that 20-80Hz neuronal (gamma) oscillations can emerge in sparsely connected network models of GABAergic fast-spiking interneurons. They explore model NN synchronization and compare their results to anatomical and electrophysiological data from hippocampal fast spiking interneurons. | |
| Generalized Carnevale-Hines algorithm (van Elburg and van Ooyen 2009) | |
| Demo illustrating the behaviour of the integrate-and-fire model in the parameter regime relevant for the generalized event-based Carnevale-Hines integration scheme. The demo includes the improved implementation of the IntFire4 mechanism. | |
| Geometry-induced features of current transfer in neuronal dendrites (Korogod, Kulagina 1998) | |
| The impact of dendritic geometry on somatopetal transfer of the current generated by steady uniform activation of excitatory synaptic conductance distributed over passive, or active (Hodgkin-Huxley type), dendrites was studied in simulated neurons. | |
| 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. | |
| HMM of Nav1.7 WT and F1449V (Gurkiewicz et al. 2011) | |
| Neuron mod files for the WT and F1449V Na+ currents from the paper: Kinetic Modeling of Nav1.7 Provides Insight Into Erythromelalgia-associated F1449V Mutation M. Gurkiewicz, A. Korngreen, S. Waxman, and A. Lampert. J.Neurophysiol. (2011). The parameters for the K65, K53 and K63 transitions were derived from microscopic reversibility relationships in the model. | |
| High frequency oscillations in a hippocampal computational model (Stacey et al. 2009) | |
| "... Using a physiological computer model of hippocampus, we investigate random synaptic activity (noise) as a potential initiator of HFOs (high-frequency oscillations). We explore parameters necessary to produce these oscillations and quantify the response using the tools of stochastic resonance (SR) and coherence resonance (CR). ... Our results show that, under normal coupling conditions, synaptic noise was able to produce gamma (30–100 Hz) frequency oscillations. Synaptic noise generated HFOs in the ripple range (100–200 Hz) when the network had parameters similar to pathological findings in epilepsy: increased gap junctions or recurrent synaptic connections, loss of inhibitory interneurons such as basket cells, and increased synaptic noise. ... We propose that increased synaptic noise and physiological coupling mechanisms are sufficient to generate gamma oscillations and that pathologic changes in noise and coupling similar to those in epilepsy can produce abnormal ripples." | |
| High frequency oscillations induced in three gap-junction coupled neurons (Tseng et al. 2008) | |
| Here we showed experimentally that high frequency oscillations (up to 600 Hz) were easily induced in a purely gap-junction coupled network by simple two stimuli with very short interval. The root cause is that the second elicited spike suffered from slow propagation speed and failure to transmit through a low-conductance junction. Similiar results were also obtained in these simulation. | |
| 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. | |
| Hippocampus temporo-septal engram shift model (Lytton 1999) | |
| Temporo-septal engram shift model of hippocampal memory. The model posits that memories gradually move along the hippocampus from a temporal encoding site to ever more septal sites from which they are recalled. We propose that the sense of time is encoded by the location of the engram along the temporo-septal axis. | |
| Hodgkin-Huxley model of persistent activity in PFC neurons (Winograd et al. 2008) (NEURON python) | |
| The paper demonstrate a form of graded persistent activity activated by hyperpolarization. This phenomenon is modeled based on a slow calcium regulation of Ih, similar to that introduced earlier for thalamic neurons (see Destexhe et al., J Neurophysiol. 1996). The only difference is that the calcium signal is here provided by the high-threshold calcium current (instead of the low-threshold calcium current in thalamic neurons). | |
| Hodgkin-Huxley model of persistent activity in prefrontal cortex neurons (Winograd et al. 2008) | |
| The paper demonstrate a form of graded persistent activity activated by hyperpolarization. This phenomenon is modeled based on a slow calcium regulation of Ih, similar to that introduced earlier for thalamic neurons (see Destexhe et al., J Neurophysiol. 1996). The only difference is that the calcium signal is here provided by the high-threshold calcium current (instead of the low-threshold calcium current in thalamic neurons). | |
| Hodgkin-Huxley models of different classes of cortical neurons (Pospischil et al. 2008) | |
| "We review here the development of Hodgkin– Huxley (HH) type models of cerebral cortex and thalamic neurons for network simulations. The intrinsic electrophysiological properties of cortical neurons were analyzed from several preparations, and we selected the four most prominent electrophysiological classes of neurons. These four classes are “fast spiking”, “regular spiking”, “intrinsically bursting” and “low-threshold spike” cells. For each class, we fit “minimal” HH type models to experimental data. ..." | |
| Homeostatic synaptic plasticity (Rabinowitch and Segev 2006a,b) | |
| (2006a): "We investigated analytically and numerically the interplay between two opposing forms of synaptic plasticity: positive-feedback, long-term potentiation/depression (LTP/LTD), and negative-feedback, homeostatic synaptic plasticity (HSP). A detailed model of a CA1 pyramidal neuron, with numerous HSP-modifiable dendritic synapses, demonstrates that HSP may have an important role in selecting which spatial patterns of LTP/LTD are to last. ... Despite the negative-feedback nature of HSP, under both local and global HSP, numerous synaptic potentiations/depressions can persist. These experimentally testable results imply that HSP could be significantly involved in shaping the spatial distribution of synaptic weights in the dendrites and not just normalizing it, as is currently believed." (2006b): "Homeostatic synaptic plasticity (HSP) is an important mechanism attributed with the slow regulation of the neuron's activity. Whenever activity is chronically enhanced, HSP weakens the weights of the synapses in the dendrites and vice versa. Because dendritic morphology and its electrical properties partition the dendritic tree into functional compartments, we set out to explore the interplay between HSP and dendritic compartmentalization. ... The spatial distribution of synaptic weights throughout the dendrites will markedly differ under the local versus global HSP mechanisms. We suggest an experimental paradigm to unravel which type of HSP mechanism operates in the dendritic tree. The answer to this question will have important implications to our understanding of the functional organization of the neuron." | |
| Hopfield and Brody model (Hopfield, Brody 2000) | |
| NEURON implementation of the Hopfield and Brody model from the papers: JJ Hopfield and CD Brody (2000) JJ Hopfield and CD Brody (2001). Instructions are provided in the below readme.txt file. | |
| Hopfield and Brody model (Hopfield, Brody 2000) (NEURON+python) | |
| Demonstration of Hopfield-Brody snychronization using artificial cells in NEURON+python. | |
| Ih levels roles in bursting and regular-spiking subiculum pyramidal neurons (van Welie et al 2006) | |
| Pyramidal neurons in the subiculum typically display either bursting or regular-spiking behavior. ... Here we report that bursting neurons posses a hyperpolarization-activated cation current (Ih) that is two-fold larger (conductance: 5.3 ± 0.5 nS) than in regularspiking neurons (2.2 ± 0.6 nS), while Ih exhibits similar voltage-dependent and kinetic properties in both classes of neurons. Bursting and regular-spiking neurons display similar morphology. The difference in Ih between the two classes is not responsible for the distinct firing patterns, since neither pharmacological blockade of Ih nor enhancement of Ih using a dynamic clamp affects the qualitative firing patterns. Instead, the difference in Ih between bursting and regular-spiking neurons determines the temporal integration of evoked synaptic input from the CA1 area. In response to 50 Hz stimulation, bursting neurons, with a large Ih, show ~50% less temporal summation than regular-spiking neurons. ... A computer simulation model of a subicular neuron with the properties of either a bursting or a regular-spiking neuron confirmed the pivotal role of Ih in temporal integration of synaptic input. These data suggest that in the subicular network, bursting neurons are better suited to discriminate the content of high frequency input, such as that occurring during gamma oscillations, compared to regular-spiking neurons. See paper for more and details. | |
| 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. | |
| Impact of dendritic size and topology on pyramidal cell burst firing (van Elburg and van Ooyen 2010) | |
| The code provided here was written to systematically investigate which of the
physical parameters controlled by dendritic morphology underlies the differences
in spiking behaviour observed in different realizations of the
'ping-pong'-model. Structurally varying dendritic topology and length in a
simplified model allows us to separate out the physical parameters derived from
morphology underlying burst firing. To perform the parameter scans we created a new NEURON tool the MultipleRunControl which can be used to easily set up a parameter scan and write the simulation results to file. Using this code we found that not input conductance but the arrival time of the return current, as measured provisionally by the average electrotonic path length, determines whether the pyramidal cell (with ping-pong model dynamics) will burst or fire single spikes. | |
| Increased computational accuracy in multi-compartmental cable models (Lindsay et al. 2005) | |
| Compartmental models of dendrites are the most widely used tool for investigating their electrical behaviour. Traditional models assign a single potential to a compartment. This potential is associated with the membrane potential at the centre of the segment represented by the compartment. All input to that segment, independent of its location on the segment, is assumed to act at the centre of the segment with the potential of the compartment. By contrast, the compartmental model introduced in this article assigns a potential to each end of a segment, and takes into account the location of input to a segment on the model solution by partitioning the effect of this input between the axial currents at the proximal and distal boundaries of segments. For a given neuron, the new and traditional approaches to compartmental modelling use the same number of locations at which the membrane potential is to be determined, and lead to ordinary differential equations that are structurally identical. However, the solution achieved by the new approach gives an order of magnitude better accuracy and precision than that achieved by the latter in the presence of point process input. | |
| 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. | |
| Inferring connection proximity in electrically coupled networks (Cali et al. 2007) | |
| In order to explore electrical coupling in the nervous system and its network-level organization, it is imperative to map the electrical synaptic microcircuits, in analogy with in vitro studies on monosynaptic and disynaptic chemical coupling. However, “walking” from cell to cell over large distances with a glass pipette is challenging, and microinjection of (fluorescent) dyes diffusing through gap-junctions remains so far the only method available to decipher such microcircuits even though technical limitations exist. Based on circuit theory, we derived analytical descriptions of the AC electrical coupling in networks of isopotential cells. We then proposed an operative electrophysiological protocol to distinguish between direct electrical connections and connections involving one or more intermediate cells. This method allows inferring the number of intermediate cells, generalizing the conventional coupling coefficient, which provides limited information. We provide here some analysis and simulation scripts that used to test our method through computer simulations, in vitro recordings, theoretical and numerical methods. Key words: Gap-Junctions; Electrical Coupling; Networks; ZAP current; Impedance. | |
| Input Fluctuations effects on f-I curves (Arsiero et al. 2007) | |
| "... We examined in vitro frequency versus current (f-I) relationships of layer 5 (L5) pyramidal cells of the rat medial prefrontal cortex (mPFC) using fluctuating stimuli. ...our results show that mPFC L5 pyramidal neurons retain an increased sensitivity to input fluctuations, whereas their sensitivity to the input mean diminishes to near zero. ..." | |
| 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. | |
| Intracortical synaptic potential modulation by presynaptic somatic potential (Shu et al. 2006, 2007) | |
| " ... Here we show that the voltage fluctuations associated with dendrosomatic synaptic activity propagate significant distances along the axon, and that modest changes in the somatic membrane potential of the presynaptic neuron modulate the amplitude and duration of axonal action potentials and, through a Ca21- dependent mechanism, the average amplitude of the postsynaptic potential evoked by these spikes. These results indicate that synaptic activity in the dendrite and soma controls not only the pattern of action potentials generated, but also the amplitude of the synaptic potentials that these action potentials initiate in local cortical circuits, resulting in synaptic transmission that is a mixture of triggered and graded (analogue) signals." | |
| Ion channel modeling with whole cell and a genetic algorithm (Gurkiewicz and Korngreen 2007) | |
| "... Here we show that a genetic search algorithm in combination with a gradient descent algorithm can be used to fit whole-cell voltage-clamp data to kinetic models with a high degree of accuracy. Previously, ion channel stimulation traces were analyzed one at a time, the results of these analyses being combined to produce a picture of channel kinetics. Here the entire set of traces from all stimulation protocols are analysed simultaneously. The algorithm was initially tested on simulated current traces produced by several Hodgkin-Huxley–like and Markov chain models of voltage-gated potassium and sodium channels. ... Finally, the algorithm was used for finding the kinetic parameters of several voltage-gated sodium and potassium channels models by matching its results to data recorded from layer 5 pyramidal neurons of the rat cortex in the nucleated outside-out patch configuration. The minimization scheme gives electrophysiologists a tool for reproducing and simulating voltage-gated ion channel kinetics at the cellular level." | |
| Irregular oscillations produced by cyclic recurrent inhibition (Friesen, Friesen 1994) | |
| Model of recurrent cyclic inhibition as described on p.119 of Friesen and Friesen (1994), which was slightly modified from Szekely's model (1965) of a network for producing alternating limb movements. | |
| Irregular spiking in NMDA-driven prefrontal cortex neurons (Durstewitz and Gabriel 2006) | |
| Slow N-Methyl-D-aspartic acid (NMDA) synaptic currents are assumed to strongly contribute to the persistently elevated firing rates observed in prefrontal cortex (PFC) during working memory. During persistent activity, spiking of many neurons is highly irregular. ... The highest interspike-interval (ISI) variability occurred in a transition regime where the subthreshold membrane potential distribution shifts from mono- to bimodality, ... Predictability within irregular ISI series was significantly higher than expected from a noise-driven linear process, indicating that it might best be described through complex (potentially chaotic) nonlinear deterministic processes. Accordingly, the phenomena observed in vitro could be reproduced in purely deterministic biophysical model neurons. High spiking irregularity in these models emerged within a chaotic, close-to-bifurcation regime characterized by a shift of the membrane potential distribution from mono- to bimodality and by similar ISI return maps as observed in vitro. ... NMDA-induced irregular dynamics may have important implications for computational processes during working memory and neural coding. | |
| JitCon: Just in time connectivity for large spiking networks (Lytton et al. 2008) | |
| This simulation is primarily an illustration and is not well optimized for actually running large networks. jitcon.mod contains a large amount of C level code, understanding of which requires some knowledge of Neuron internals | |
| KV1 channel governs cerebellar output to thalamus (Ovsepian et al. 2013) | |
| The output of the cerebellum to the motor axis of the central nervous system is orchestrated mainly by synaptic inputs and intrinsic pacemaker activity of deep cerebellar nuclear (DCN) projection neurons. Herein, we demonstrate that the soma of these cells is enriched with KV1 channels produced by mandatory multi-merization of KV1.1, 1.2 alpha andKV beta2 subunits. Being constitutively active, the K+ current (IKV1) mediated by these channels stabilizes the rate and regulates the temporal precision of self-sustained firing of these neurons. ... Through the use of multi-compartmental modelling and ... the physiological significance of the described functions for processing and communication of information from the lateral DCN to thalamic relay nuclei is established. | |
| 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. ..." | |
| 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. | |
| 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 | |
| L5b PC model constrained for BAC firing and perisomatic current step firing (Hay et al., 2011) | |
| "... L5b pyramidal cells have been the subject of extensive experimental and modeling studies, yet conductance-based models of these cells that faithfully reproduce both their perisomatic Na+-spiking behavior as well as key dendritic active properties, including Ca2+ spikes and back-propagating action potentials, are still lacking. Based on a large body of experimental recordings from both the soma and dendrites of L5b pyramidal cells in adult rats, we characterized key features of the somatic and dendritic firing and quantified their statistics. We used these features to constrain the density of a set of ion channels over the soma and dendritic surface via multi-objective optimization with an evolutionary algorithm, thus generating a set of detailed conductance-based models that faithfully replicate the back-propagating action potential activated Ca2+ spike firing and the perisomatic firing response to current steps, as well as the experimental variability of the properties. ... The models we present provide several experimentally-testable predictions and can serve as a powerful tool for theoretical investigations of the contribution of single-cell dynamics to network activity and its computational capabilities. " | |
| 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). | |
| LTP in cerebellar mossy fiber-granule cell synapses (Saftenku 2002) | |
| We simulated synaptic transmission and modified a simple model of long-term potentiation (LTP) and long-term depression (LTD) in order to describe long-term plasticity related changes in cerebellar mossy fiber-granule cell synapses. In our model, protein autophosphorylation, leading to the maintenance of long-term plasticity, is controlled by Ca2+ entry through the NMDA receptor channels. The observed nonlinearity in the development of long-term changes of EPSP in granule cells is explained by the difference in the rate constants of two independent autocatalytic processes. | |
| 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). | |
| Lateral dendrodenditic inhibition in the Olfactory Bulb (David et al. 2008) | |
| Mitral cells, the principal output neurons of the olfactory bulb, receive direct synaptic activation from primary sensory neurons. Shunting inhibitory inputs delivered by granule cell interneurons onto mitral cell lateral dendrites are believed to influence spike timing and underlie coordinated field potential oscillations. Lateral dendritic shunt conductances delayed spiking to a degree dependent on both their electrotonic distance and phase of onset. Recurrent inhibition significantly narrowed the distribution of mitral cell spike times, illustrating a tendency towards coordinated synchronous activity. This result suggests an essential role for early mechanisms of temporal coordination in olfaction. The model was adapted from Davison et al, 2003, but include additional noise mechanisms, long lateral dendrite, and specific synaptic point processes. | |
| 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. " | |
| Learning spatial transformations through STDP (Davison, Frégnac 2006) | |
| A common problem in tasks involving the integration of spatial information from multiple senses, or in sensorimotor coordination, is that different modalities represent space in different frames of reference. Coordinate transformations between different reference frames are therefore required. One way to achieve this relies on the encoding of spatial information using population codes. The set of network responses to stimuli in different locations (tuning curves) constitute a basis set of functions which can be combined linearly through weighted synaptic connections in order to approximate non-linear transformations of the input variables. The question then arises how the appropriate synaptic connectivity is obtained. This model shows that a network of spiking neurons can learn the coordinate transformation from one frame of reference to another, with connectivity that develops continuously in an unsupervised manner, based only on the correlations available in the environment, and with a biologically-realistic plasticity mechanism (spike timing-dependent plasticity). | |
| Leech Mechanosensory Neurons: Synaptic Facilitation by Reflected APs (Baccus 1998) | |
| This model by Stephen Baccus explores the phenomena of action potential (AP) propagation at branch boints in axons. APs are sometimes transmitted down the efferent processes and sometimes are reflected back to the axon of AP origin or neither. See the paper for details. The model zip file contains a readme.txt which list introductory steps to follow to run the simulation. Stephen Baccus's email address: baccus@fas.harvard.edu | |
| Linear vs non-linear integration in CA1 oblique dendrites (Gómez González et al. 2011) | |
| The hippocampus in well known for its role in learning and memory processes. The CA1 region is the output of the hippocampal formation and pyramidal neurons in this region are the elementary units responsible for the processing and transfer of information to the cortex. Using this detailed single neuron model, it is investigated the conditions under which individual CA1 pyramidal neurons process incoming information in a complex (non-linear) as opposed to a passive (linear) manner. This detailed compartmental model of a CA1 pyramidal neuron is based on one described previously (Poirazi, 2003). The model was adapted to five different reconstructed morphologies for this study, and slightly modified to fit the experimental data of (Losonczy, 2006), and to incorporate evidence in pyramidal neurons for the non-saturation of NMDA receptor-mediated conductances by single glutamate pulses. We first replicate the main findings of (Losonczy, 2006), including the very brief window for nonlinear integration using single-pulse stimuli. We then show that double-pulse stimuli increase a CA1 pyramidal neuron’s tolerance for input asynchrony by at last an order of magnitude. Therefore, it is shown using this model, that the time window for nonlinear integration is extended by more than an order of magnitude when inputs are short bursts as opposed to single spikes. | |
| Local variable time step method (Lytton, Hines 2005) | |
| The local variable time-step method utilizes separate variable step integrators for individual neurons in the network. It is most suitable for medium size networks in which average synaptic input intervals to a single cell are much greater than a fixed step dt. | |
| Low Threshold Calcium Currents in TC cells (Destexhe et al 1998) | |
| In Destexhe, Neubig, Ulrich, and Huguenard (1998) experiments and models examine low threshold calcium current's (IT, or T-current) distribution in thalamocortical (TC) cells. Multicompartmental modeling supports the hypothesis that IT currents have a density at least several fold higher in the dendrites than the soma. The IT current contributes significantly to rebound bursts and is thought to have important network behavior consequences. See the paper for details. See also http://cns.iaf.cnrs-gif.fr Correspondance may be addressed to Alain Destexhe: Destexhe@iaf.cnrs-gif.fr | |
| 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." | |
| MNTB Neuron: Kv3.1 currents (Wang et al 1998) | |
| Model of Medial Nucleus of the Trapezoid Body (MNTB) neurons described in Lu-Yang Wang, Li Gan, Ian D. Forsythe and Leonard K. Kaczmarek. Contribution of the Kv3.1 potassium channel to high-frequency firing in mouse auditory neurones. J. Physiol (1998) 509.1 183-194. Created by David Kornfeld, Byram Hills High School, Armonk NY. Please email dbk1@mindspring.com for questions about the model. See Readme.txt below for more info. | |
| Mammalian Ventricular Cell (Beeler and Reuter 1977) | |
| This classic model of ventricular myocardial fibres was implemented by Francois Gannier. "... Four individual components of ionic current were formulated mathematically in terms of Hodgkin-Huxley type equations. The model incorporates two voltage- and time-dependent inward currents, the excitatory inward sodium current, illa, and a secondary or slow inward current, is, primarily carried by calcium ions. A time-independent outward potassium current, iK1, exhibiting inward-going rectification, and a voltage- and time-dependent outward current, i.1, primarily carried by potassium ions, are further elements of the model...." | |
| Mapping function onto neuronal morphology (Stiefel and Sejnowski 2007) | |
| "... We used an optimization procedure to find neuronal morphological structures for two computational tasks: First, neuronal morphologies were selected for linearly summing excitatory synaptic potentials (EPSPs); second, structures were selected that distinguished the temporal order of EPSPs. The solutions resembled the morphology of real neurons. In particular the neurons optimized for linear summation electrotonically separated their synapses, as found in avian nucleus laminaris neurons, and neurons optimized for spike-order detection had primary dendrites of significantly different diameter, as found in the basal and apical dendrites of cortical pyramidal neurons. ..." | |
| Mathematical model for windup (Aguiar et al. 2010) | |
| "Windup is characterized as a frequency-dependent increase in the number of evoked action potentials in dorsal horn neurons in response to electrical stimulation of afferent C-fibers. ... The approach presented here relies on mathematical and computational analysis to study the mechanism(s) underlying windup. From experimentally obtained windup profiles, we extract the time scale of the facilitation mechanisms that may support the characteristics of windup. Guided by these values and using simulations of a biologically realistic compartmental model of a wide dynamic range (WDR) neuron, we are able to assess the contribution of each mechanism for the generation of action potentials windup. ..." | |
| Mechanisms of fast rhythmic bursting in a layer 2/3 cortical neuron (Traub et al 2003) | |
| This simulation is based on the reference paper listed below.
This port was made by Roger D Traub and Maciej T Lazarewicz (mlazarew@seas.upenn.edu) Thanks to Ashlen P Reid for help with porting a morphology of the cell. | |
| Mechanisms of magnetic stimulation of central nervous system neurons (Pashut et al. 2011) | |
| Transcranial magnetic stimulation (TMS) is a widely applied tool for probing cognitive function in humans and is one of the best tools for clinical treatments and interfering with cognitive tasks. Surprisingly, while TMS has been commercially available for decades, the cellular mechanisms underlying magnetic stimulation remain unclear. Here we investigate these mechanisms using compartmental modeling. We generated a numerical scheme allowing simulation of the physiological response to magnetic stimulation of neurons with arbitrary morphologies and active properties. Computational experiments using this scheme suggested that TMS affects neurons in the central nervous system (CNS) primarily by somatic stimulation. | |
| Medial vestibular neuron models (Quadroni and Knopfel 1994) | |
| The structure and the parameters of the model cells were chosen to reproduce the responses of type A and type B MVNns as described in electrophysiological recordings. The emergence of oscillatory firing under these two specific experimental conditions is consistent with electrophysiological recordings not used during construction of the model. We, therefore, suggest that these models have a high predictive value. | |
| Membrane potential changes in dendritic spines during APs and synaptic input (Palmer & Stuart 2009) | |
| " ... Finally, we used simulations of our experimental observations in morphologically realistic models to estimate spine neck resistance. These simulations indicated that spine neck resistance ranges up to ~500 M Ohm. Spine neck resistances of this magnitude reduce somatic EPSPs by ~15%, indicating that the spine neck is unlikely to act as a physical device to significantly modify synaptic strength." | |
| Midbrain dopamine neuron: firing patterns (Canavier 1999) | |
| Sodium dynamics drives the generation of slow oscillations postulated to underly NMDA-evoked bursting activity. | |
| Model of SK current`s influence on precision in Globus Pallidus Neurons (Deister et al. 2009) | |
| " ... In numerical simulations, the availability of both Na+ and A-type K+ channels during autonomous firing were reduced when SK channels were removed, and a nearly equal reduction in Na+ and K+ subthreshold-activated ion channel availability produced a large decrease in the neuron's slope conductance near threshold. This change made the neuron more sensitive to intrinsically generated noise. In vivo, this change would also enhance the sensitivity of GP (Globus Pallidus) neurons to small synaptic inputs." | |
| Modeling local field potentials (Bedard et al. 2004) | |
| This demo simulates a model of local field potentials (LFP) with variable resistivity. This model reproduces the low-pass frequency filtering properties of extracellular potentials. The model considers inhomogeneous spatial profiles of conductivity and permittivity, which result from the multiple media (fluids, membranes, vessels, ...) composing the extracellular space around neurons. Including non-constant profiles of conductivity enables the model to display frequency filtering properties, ie slow events such as EPSPs/IPSPs are less attenuated than fast events such as action potentials. The demo simulates Fig 6 of the paper. | |
| Modelling reduced excitability in aged CA1 neurons as a Ca-dependent process (Markaki et al. 2005) | |
| "We use a multi-compartmental model of a CA1 pyramidal cell to study changes in hippocampal excitability that result from aging-induced alterations in calcium-dependent membrane mechanisms. The model incorporates N- and L-type calcium channels which are respectively coupled to fast and slow afterhyperpolarization potassium channels. Model parameters are calibrated using physiological data. Computer simulations reproduce the decreased excitability of aged CA1 cells, which results from increased internal calcium accumulation, subsequently larger postburst slow afterhyperpolarization, and enhanced spike frequency adaptation. We find that aging-induced alterations in CA1 excitability can be modelled with simple coupling mechanisms that selectively link specific types of calcium channels to specific calcium-dependent potassium channels." | |
| Models of Na channels from a paper on the PKC control of I Na,P (Baker 2005) | |
| "The tetrodotoxin-resistant (TTX-r) persistent Na(+) current, attributed to Na(V)1.9, was recorded in small (< 25 mum apparent diameter) dorsal root ganglion (DRG) neurones cultured from P21 rats and from adult wild-type and Na(V)1.8 null mice. ... Numerical simulation of the up-regulation qualitatively reproduced changes in sensory neurone firing properties. ..." Note: models of NaV1.8 and NaV1.9 and also persistent and transient Na channels that collectively model Nav 1.1, 1.6, and 1.7 are present in this model. | |
| Modulation of temporal integration window (Migliore, Shepherd 2002) | |
| Model simulation file from the paper M.Migliore and Gordon M. Shepherd Emerging rules for distributions of active dendritic properties underlying specific neuronal functions. Nature Rev. Neurosci. 3, 362-370 (2002). | |
| Multicompartmental cerebellar granule cell model (Diwakar et al. 2009) | |
| A detailed multicompartmental model was used to study neuronal electroresponsiveness of cerebellar granule cells in rats. Here we show that, in cerebellar granule cells, Na+ channels are enriched in the axon, especially in the hillock, but almost absent from soma and dendrites. Numerical simulations indicated that granule cells have a compact electrotonic structure allowing EPSPs to diffuse with little attenuation from dendrites to axon. The spike arose almost simultaneously along the whole axonal ascending branch and invaded the hillock, whose activation promoted spike back-propagation with marginal delay (<200 micros) and attenuation (<20 mV) into the somato-dendritic compartment. For details check the cited article. | |
| MyFirstNEURON (Houweling, Sejnowski 1997) | |
| MyFirstNEURON is a NEURON demo by Arthur Houweling and Terry Sejnowski. Perform experiments from the book 'Electrophysiology of the Neuron, A Companion to Shepherd's Neurobiology, An Interactive Tutorial' by John Huguenard & David McCormick, Oxford University Press 1997, or design your own one or two cell simulation. For more information see http://www.cnl.salk.edu/Simulations. Salk Institute, Computational Neurobiology Lab, 10010 North Torrey Pines Rd., La Jolla CA 092037. Email: arthur@salk.edu | |
| Myelinated axon conduction velocity (Brill et al 1977) | |
| Examines conduction velocity as function of internodal length. | |
| 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”. | |
| NEURON + Python (Hines et al. 2009) | |
| The NEURON simulation program now allows Python to be used alone or in combination with NEURON's traditional Hoc interpreter. Adding Python to NEURON has the immediate benefit of making available a very extensive suite of analysis tools written for engineering and science. It also catalyzes NEURON software development by offering users a modern programming tool that is recognized for its flexibility and power to create and maintain complex programs. At the same time, nothing is lost because all existing models written in Hoc, including GUI tools, continue to work without change and are also available within the Python context. An example of the benefits of Python availability is the use of the xml module in implementing NEURON's Import3D and CellBuild tools to read MorphML and NeuroML model specifications. | |
| NEURON interfaces to MySQL and the SPUD feature extraction algorithm (Neymotin et al. 2007) | |
| See the readme.txt for information on setting up this interface to a MySQL server from the NEURON simulator. Note the SPUD feature extraction algorithm includes its own readme in the spud directory. | |
| 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@interchange.ubc.ca | |
| Na+ channel dependence of AP initiation in cortical pyramidal neuron (Kole et al. 2008) | |
| In this simulation action potential initiation, action potential properties and the role of axon initial segment Na+ channels are investigated in a realistic model of a layer 5 pyramidal neuron axon initial segment. The main Na+ channel properties were constrained by experimental data and the axon initial segment was reconstructed. Model parameters were constrained by direct recordings at the axon initial segment. | |
| Nav1.6 sodium channel model in globus pallidus neurons (Mercer et al. 2007) | |
| Model files for the paper Mercer JN, Chan CS, Tkatch T, Held J, Surmeier DJ. Nav1.6 sodium channels are critical to pacemaking and fast spiking in globus pallidus neurons.,J Neurosci. 2007 Dec 5;27(49):13552-66. | |
| Neocort. pyramidal cells subthreshold somatic voltage controls spike propagation (Munro Kopell 2012) | |
| There is suggestive evidence that pyramidal cell axons in neocortex may be coupled by gap junctions into an ``axonal plexus" capable of generating Very Fast Oscillations (VFOs) with frequencies exceeding 80 Hz. It is not obvious, however, how a pyramidal cell in such a network could control its output when action potentials are free to propagate from the axons of other pyramidal cells into its own axon. We address this problem by means of simulations based on 3D reconstructions of pyramidal cells from rat somatosensory cortex. We show that somatic depolarization enables propagation via gap junctions into the initial segment and main axon, while somatic hyperpolarization disables it. We show further that somatic voltage cannot effectively control action potential propagation through gap junctions on minor collaterals; action potentials may therefore propagate freely from such collaterals regardless of somatic voltage. In previous work, VFOs are all but abolished during the hyperpolarization phase of slow-oscillations induced by anesthesia in vivo. This finding constrains the density of gap junctions on collaterals in our model and suggests that axonal sprouting due to cortical lesions may result in abnormally high gap junction density on collaterals, leading in turn to excessive VFO activity and hence to epilepsy via kindling. | |
| Neocortical Layer I: I-A and I-K (Zhou, Hablitz 1996) | |
| NEURON mod files for the I-A and I-K currents from the paper: Zhou FM, Hablitz JJ. Layer I neurons of the rat neocortex. II. Voltage-dependent outward currents. J Neurophysiol 1996 76:668-82. | |
| Neocortical pyramidal neuron: deep; effects of dopamine (Durstewitz et al 2000) | |
| "... Simulated dopamine strongly enhanced high, delay-type activity but not low, spontaneous activity in the model network. Furthermore the strength of an afferent stimulation needed to disrupt delay-type activity increased with the magnitude of the dopamine-induced shifts in network parameters, making the currently active representation much more stable. Stability could be increased by dopamine-induced enhancements of the persistent Na(+) and N-methyl-D-aspartate (NMDA) conductances. Stability also was enhanced by a reduction in AMPA conductances. The increase in GABA(A) conductances that occurs after stimulation of dopaminergic D1 receptors was necessary in this context to prevent uncontrolled, spontaneous switches into high-activity states (i.e., spontaneous activation of task-irrelevant representations). In conclusion, the dopamine-induced changes in the biophysical properties of intrinsic ionic and synaptic conductances conjointly acted to highly increase stability of activated representations in PFC networks and at the same time retain control over network behavior and thus preserve its ability to adequately respond to task-related stimuli. ..." See paper and references for more and details. | |
| Nerve terminal currents at lizard neuromuscular junction (Lindgren, Moore 1989) | |
| Loose patch clamp measurement of presynaptic ionic currents at lizard neuromuscular junction compared with computer simulations. | |
| Network recruitment to coherent oscillations in a hippocampal model (Stacey et al. 2011) | |
| "... Here we demonstrate, via a detailed computational model, a mechanism whereby physiological noise and coupling initiate oscillations and then recruit neighboring tissue, in a manner well described by a combination of Stochastic Resonance and Coherence Resonance. We develop a novel statistical method to quantify recruitment using several measures of network synchrony. This measurement demonstrates that oscillations spread via preexisting network connections such as interneuronal connections, recurrent synapses, and gap junctions, provided that neighboring cells also receive sufficient inputs in the form of random synaptic noise. ..." | |
| Networks of spiking neurons: a review of tools and strategies (Brette et al. 2007) | |
| This package provides a series of codes that simulate networks of spiking neurons (excitatory and inhibitory, integrate-and-fire or Hodgkin-Huxley type, current-based or conductance-based synapses; some of them are event-based). The same networks are implemented in different simulators (NEURON, GENESIS, NEST, NCS, CSIM, XPP, SPLIT, MVAspike; there is also a couple of implementations in SciLab and C++). The codes included in this package are benchmark simulations; see the associated review paper Brette et al. (2007) available at this link http://arxiv.org/abs/q-bio.NC/0611089 The main goal is to provide a series of benchmark simulations of networks of spiking neurons, and demonstrate how these are implemented in the different simulators overviewed in the paper. See also details in the enclosed file Appendix2.pdf, which describes these different benchmarks. Some of these benchmarks were based on the Vogels-Abbott model (Vogels TP and Abbott LF 2005). | |
| Neural Query System NQS Data-Mining From Within the NEURON Simulator (Lytton 2006) | |
| NQS is a databasing program with a query command modeled loosely on the SQL select command. Please see the manual NQS.pdf for details of use. An NQS database must be populated with data to be used. This package includes MFP (model fingerprint) which provides an example of NQS use with the model provided in the modeldb folder (see readme for usage). | |
| Neuronal morphology goes digital ... (Parekh & Ascoli 2013) | |
| An illustration of a NEURON model and why reconstructing morphologies is useful in this regard (i.e. investigating spatial/temporal aspect of how different currents and voltage propagate in dendrites). | |
| Neurophysiological impact of inactivation pathways in A-type K+ channels (Fineberg et al 2012) | |
| These models predict the differential effects of varying pathways of inactivation (closed state inactivation, CSI, or open state inactivation, OSI). Specifically, Markov models of Kv4 potassium channels with CSI or CSI+OSI were inserted into the CA1 pyramidal neuron model from Migliore et al (1999; ModelDB accession #2796) to determine the neurophysiological impact of inactivation pathways. Furthermore, Markov models of Kv4.2 and Kv3.4 channels are used to illustrate a method by which to test what pathway of inactivation a channel uses. | |
| Nigral dopaminergic neurons: effects of ethanol on Ih (Migliore et al. 2008) | |
| We use a realistic computational model of dopaminergic neurons in vivo to suggest that ethanol, through its effects on Ih, modifies the temporal structure of the spiking activity. The model predicts that the dopamine level may increase much more during bursting than pacemaking activity, especially in those brain regions with a slow dopamine clearance rate. The results suggest that a selective pharmacological remedy could thus be devised against the rewarding effects of ethanol that are postulated to mediate alcohol abuse and addiction, targeting the specific HCN genes expressed in dopaminergic neurons. | |
| 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 | |
| Novel Na current with slow de-inactivation (Tsutsui, Oka 2002) | |
| The authors found a novel Na current in teleost thalamic nuclei was well described by the m^3 h Hodgkin-Huxley model. The kinetic parameters derived from their experiments (see the reference for details) revealed that the h gate had a large time constant (~100ms at -80 to -50mV). This explains the thalamic neurons long refractory period and the gradual recovery of AP amplitude as the inter spike interval grows. | |
| O-LM interneuron model (Lawrence et al. 2006) | |
| Exploring the kinetics and distribution of the muscarinic potassium channel, IM, in 2 O-LM interneuron morphologies. Modulation of the ion channel by drugs such as XE991 (antagonist) and retigabine (agonist) are simulated in the models to examine the role of IM in spiking properties. | |
| 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. | |
| Olfactory Mitral Cell (Bhalla, Bower 1993) | |
| This is a conversion to NEURON of the mitral cell model described in Bhalla and Bower (1993). The original model was written in GENESIS and is available by joining BABEL, the GENESIS users' group. | |
| Olfactory Mitral Cell (Davison et al 2000) | |
| A four-compartment model of a mammalian olfactory bulb mitral cell, reduced from the complex 286-compartment model described by Bhalla and Bower (1993). The compartments are soma/axon, secondary dendrites, primary dendrite shaft and primary dendrite tuft. The reduced model runs 75 or more times faster than the full model, making its use in large, realistic network models of the olfactory bulb practical. | |
| Olfactory Mitral Cell (Shen et al 1999) | |
| Mitral cell model with standard parameters for the paper: Shen, G.Y., Chen, W. R., Midtgaard, J., Shepherd, G.M., and Hines, M.L. (1999) Computational Analysis of Action Potential Initiation in Mitral Cell Soma and Dendrites Based on Dual Patch Recordings. Journal of Neurophysiology 82:3006. Contact Michael.Hines@yale.edu if you have any questions about the implementation of the model. | |
| Olfactory Mitral Cell: I-A and I-K currents (Wang et al 1996) | |
| NEURON mod files for the I-A and I-K currents from the paper: X.Y. Wang, J.S. McKenzie and R.E. Kemm, Whole-cell K+ currents in identified olfactory bulb output neurones of rats. J Physiol. 1996 490.1:63-77. Please see the readme.txt included in the model file for more information. | |
| Olfactory Mitral cell: AP initiation modes (Chen et al 2002) | |
| The mitral cell primary dendrite plays an important role in transmitting distal olfactory nerve input from olfactory glomerulus to the soma-axon initial segment. To understand how dendritic active properties are involved in this transmission, we have combined dual soma and dendritic patch recordings with computational modeling to analyze action-potential initiation and propagation in the primary dendrite. | |
| Olfactory Periglomerular Cells: I-h kinetics (Cadetti, Belluzzi 2001) | |
| NEURON mod files for the Ih current from the paper: Cadetti L, Belluzzi O. Hyperpolarisation-activated current in glomerular cells of the rat olfactory bulb. Neuroreport 12:3117-20 (2001). | |
| Olfactory bulb cluster formation (Migliore et al. 2010) | |
| Functional roles of distributed synaptic clusters in the mitral-granule cell network of the olfactory bulb. | |
| 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. | |
| 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. | |
| 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. | |
| Olfactory bulb mitral cell gap junction NN model: burst firing and synchrony (O`Connor et al. 2012) | |
| In a network of 6 mitral cells connected by gap junction in the apical dendrite tuft, continuous current injections of 0.06 nA are injected into 20 locations in the apical tufts of two of the mitral cells. The current injections into one of the cells starts 10 ms after the other to generate asynchronous firing in the cells (Migliore et al. 2005 protocol). Firing of the cells is asynchronous for the first 120 ms. However after the burst firing phase is completed the firing in all cells becomes synchronous. | |
| Olfactory bulb mitral cell: synchronization by gap junctions (Migliore et al 2005) | |
| In a realistic model of two electrically connected mitral cells, the paper shows that the somatically-measured experimental properties of Gap Junctions (GJs) may correspond to a variety of different local coupling strengths and dendritic distributions of GJs in the tuft. The model suggests that the propagation of the GJ-induced local tuft depolarization is a major mechanim for intraglomerular synchronization of mitral cells. | |
| Oscillating neurons in the cochlear nucleus (Bahmer Langner 2006a, b, and 2007) | |
| "Based on the physiological and anatomical data, we propose a model consisting of a minimum network of two choppers that are interconnected with a synaptic delay of 0.4 ms (Bahmer and Langner 2006a) . Such minimum delays have been found in different systems and in various animals (e.g. Hackett, Jackson, and Rubel 1982; Borst, Helmchen, and Sakmann 1995). The choppers receive input from both the auditory nerve and an onset neuron. This model can reproduce the mean, standard deviation, and coefficient of variation of the ISI and the dynamic features of AM coding of choppers." | |
| Parallel network simulations with NEURON (Migliore et al 2006) | |
| The NEURON simulation environment has been extended to support parallel network simulations. The performance of three published network models with very different spike patterns exhibits superlinear speedup on Beowulf clusters. | |
| 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. | |
| Phase response curve of a globus pallidal neuron (Fujita et al. 2011) | |
| We investigated how changes in ionic conductances alter the phase response curve (PRC) of a globus pallidal (GP) neuron and stability of a synchronous activity of a GP network, using a single-compartmental conductance-based neuron model. The results showed the PRC and the stability were influenced by changes in the persistent sodium current, the Kv3 potassium, the M-type potassium and the calcium-dependent potassium current. | |
| Presynaptic calcium dynamics at neuromuscular junction (Stockbridge, Moore 1984) | |
| The diffusion of calcium is effectively reduced by the ratio of bound to free calcium. Treating the release magnitude as proportional to the fourth power of calcium concentration next to the membrane gives reasonable facilitation with very little release between spikes. | |
| Prosthetic electrostimulation for information flow repair in a neocortical simulation (Kerr 2012) | |
| This model is an extension of a model (138379) 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. | |
| Pyramidal Neuron Deep, Superficial; Aspiny, Stellate (Mainen and Sejnowski 1996) | |
| This package contains compartmental models of four reconstructed neocortical neurons (layer 3 Aspiny, layer 4 Stellate, layer 3 and layer 5 Pyramidal neurons) with active dendritic currents using NEURON. Running this simulation demonstrates that an entire spectrum of firing patterns can be reproduced in this set of model neurons which share a common distribution of ion channels and differ only in their dendritic geometry. The reference paper is: Z. F. Mainen and T. J. Sejnowski (1996) Influence of dendritic structure on firing pattern in model neocortical neurons. Nature 382: 363-366. See also http://www.cnl.salk.edu/~zach/methods.html and http://www.cnl.salk.edu/~zach/ More info in readme.txt file below made visible by clicking on the patdemo folder and then on the readme.txt file. | |
| Pyramidal Neuron Deep: K+ kinetics (Korngreen, Sakmann 2000) | |
| NEURON mod files for the slow and fast K+ currents from the paper: Voltage-gated K+ channels in layer 5 neocortical pyramidal neurones from young rats: subtypes and gradients A. Korngreen and B. Sakmann, J.Physiol. 525.3, 621-639 (2000). | |
| Pyramidal Neuron Deep: attenuation in dendrites (Stuart, Spruston 1998) | |
| Stuart, G. and Spruston, N. Determinants of voltage attenuation in neocortical pyramidal neuron dendrites. Journal of Neuroscience 18:3501-3510, 1998. | |
| Pyramidal Neuron: Deep, Thalamic Relay and Reticular, Interneuron (Destexhe et al 1998, 2001) | |
| This package shows single-compartment models of different classes of cortical neurons, such as the "regular-spiking", "fast-spiking" and "bursting" (LTS) neurons. The mechanisms included are the Na+ and K+ currents for generating action potentials (INa, IKd), the T-type calcium current (ICaT), and a slow voltage-dependent K+ current (IM). See http://cns.fmed.ulaval.ca/alain_demos.html | |
| Pyramidal neuron coincidence detection tuned by dendritic branching pattern (Schaefer et al 2006) | |
| "... We examined the relationship between dendritic arborization and the coupling between somatic and dendritic action potential (AP) initiation sites in layer 5 (L5) neocortical pyramidal neurons. Coupling was defined as the relative reduction in threshold for initiation of a dendritic calcium AP due to a coincident back-propagating AP. Simulations based on reconstructions of biocytin-filled cells showed that addition of oblique branches of the main apical dendrite in close proximity to the soma (d < 140 um) increases the coupling between the apical and axosomatic AP initiation zones, whereas incorporation of distal branches decreases coupling. ... We conclude that variation in dendritic arborization may be a key determinant of variability in coupling (49+-17%; range 19-83%; n = 37) and is likely to outweigh the contribution made by variations in active membrane properties. Thus coincidence detection of inputs arriving from different cortical layers is strongly regulated by differences in dendritic arborization." | |
| Pyramidal neuron conductances state and STDP (Delgado et al. 2010) | |
| Neocortical neurons in vivo process each of their individual inputs in the context of ongoing synaptic background activity, produced by the thousands of presynaptic partners a typical neuron has. That background activity affects multiple aspects of neuronal and network function. However, its effect on the induction of spike-timing dependent plasticity (STDP) is not clear. Using the present biophysically-detailed computational model, it is not only able to replicate the conductance-dependent shunting of dendritic potentials (Delgado et al,2010), but show that synaptic background can truncate calcium dynamics within dendritic spines, in a way that affects potentiation more strongly than depression. This program uses a simplified layer 2/3 pyramidal neuron constructed in NEURON. It was similar to the model of Traub et al., J Neurophysiol. (2003), and consisted of a soma, an apical shaft, distal dendrites, five basal dendrites, an axon, and a single spine. The spine’s location was variable along the apical shaft (initial 50 μm) and apical. The axon contained an axon hillock region, an initial segment, segments with myelin, and nodes of Ranvier, in order to have realistic action potential generation. For more information about the model see supplemental material, Delgado et al 2010. | |
| Rat subthalamic projection neuron (Gillies and Willshaw 2006) | |
| A computational model of the rat subthalamic nucleus projection neuron is constructed using electrophysiological and morphological data and a restricted set of channel specifications. The model cell exhibits a wide range of electrophysiological behaviors characteristic of rat subthalamic neurons. It reveals that a key set of three channels play a primary role in distinguishing behaviors: a high-voltage-activated calcium channel (Cav 1.2.-1.3), a low-voltage-activated calcium channel (Cav 3.-), and a small current calcium-activated potassium channel (KCa 2.1-2.3). See paper for more and details. | |
| Reciprocal regulation of rod and cone synapse by NO (Kourennyi et al 2004) | |
| We constructed models of rod and cone photoreceptors using NEURON software to predict how changes in Ca channels would affect the light response in these cells and in postsynaptic horizontal cells. | |
| Reconstructing cerebellar granule layer evoked LFP using convolution (ReConv) (Diwakar et al. 2011) | |
| The model allows reconstruction of evoked local field potentials as seen in the cerebellar granular layer. The approach uses a detailed model of cerebellar granule neuron to generate data traces and then uses a "ReConv" or jittered repetitive convolution technique to reproduce post-synaptic local field potentials in the granular layer. The algorithm was used to generate both in vitro and in vivo evoked LFP and reflected the changes seen during LTP and LTD, when such changes were induced in the underlying neurons by modulating release probability of synapses and sodium channel regulated intrinsic excitability of the cells. | |
| Recording from rod bipolar axon terminals in situ (Oltedal et al 2007) | |
| "... Whole cell recordings from axon terminals and cell bodies were used to investigate the passive membrane properties of rod bipolar cells and analyzed with a two-compartment equivalent electrical circuit model developed by Mennerick et al. For both terminal- and soma-end recordings, capacitive current decays were well fitted by biexponential functions. Computer simulations of simplified models of rod bipolar cells demonstrated that estimates of the capacitance of the axon terminal compartment can depend critically on the recording location, with terminal-end recordings giving the best estimates. Computer simulations and whole cell recordings demonstrated that terminal-end recordings can yield more accurate estimates of the peak amplitude and kinetic properties of postsynaptic currents generated at the axon terminals due to increased electrotonic filtering of these currents when recorded at the soma. ..." See paper for more and details. | |
| Regulation of a slow STG rhythm (Nadim et al 1998) | |
| Frequency regulation of a slow rhythm by a fast periodic input. Nadim, F., Manor, Y., Nusbaum, M. P., Marder, E. (1998) J. Neurosci. 18: 5053-5067 | |
| Regulation of firing frequency in a midbrain dopaminergic neuron model (Kuznetsova et al. 2010) | |
| A dopaminergic (DA) neuron model with a morphologicaly realistic dendritic architecture. The model captures several salient features of DA neurons under different pharmacological manipulations and exhibits depolarization block for sufficiently high current pulses applied to the soma. | |
| Regulation of the firing pattern in dopamine neurons (Komendantov et al 2004) | |
| Midbrain dopaminergic (DA) neurons in vivo exhibit two major firing patterns: single-spike firing and burst firing. The firing pattern expressed is dependent on both the intrinsic properties of the neurons and their excitatory and inhibitory synaptic inputs. Experimental data suggest that the activation of NMDA and GABAA receptors is crucial contributor to the initiation and suppression of burst firing, respectively, and that blocking calcium-activated potassium channels can facilitate burst firing. This multi-compartmental model of a DA neuron with a branching structure was developed and calibrated based on in vitro experimental data to explore the effects of different levels of activation of NMDA and GABAA receptors as well as the modulation of the SK current on the firing activity. | |
| Reinforcement learning of targeted movement (Chadderdon et al. 2012) | |
| Rejuvenation model of dopamine neuron (Chan et al. 2007) | |
| Model files for the paper C. Savio Chan, et al. 'Rejuvenation' protects neurons in mouse models of Parkinson's disease, Nature 447, 1081-1086(28 June 2007). | |
| Resonance properties through Chirp stimulus responses (Narayanan Johnston 2007, 2008) | |
| ...we constructed a simple, single-compartment
model with Ih as the only active current... we found that both resonance frequency and resonance strength increased monotonically with the increase in the h conductance, supporting the notion of a direct, graded relationship between h conductance and resonance properties... (Narayanan and Johnston, 2007). ...we show that the h channels introduce an apparent negative delay in the local voltage response of these neurons with respect to the injected current within the theta frequency range... we found that the total inductive phase increased monotonically with the h conductance, whereas it had a bell-shaped dependence on both the membrane voltage and the half-maximal activation voltage for the h conductance. (Narayanan and Johnston, 2008). | |
| Response properties of neocort. neurons to temporally modulated noisy inputs (Koendgen et al. 2008) | |
| Neocortical neurons are classified by current–frequency relationship. This is a static description and it may be inadequate to interpret neuronal responses to time-varying stimuli. Theoretical studies (Brunel et al., 2001; Fourcaud-Trocmé et al. 2003; Fourcaud-Trocmé and Brunel 2005; Naundorf et al. 2005) suggested that single-cell dynamical response properties are necessary to interpret ensemble responses to fast input transients. Further, it was shown that input-noise linearizes and boosts the response bandwidth, and that the interplay between the barrage of noisy synaptic currents and the spike-initiation mechanisms determine the dynamical properties of the firing rate. In order to allow a reader to explore such simulations, we prepared a simple NEURON implementation of the experiments performed in Köndgen et al., 2008 (see also Fourcaud-Trocmé al. 2003; Fourcaud-Trocmé and Brunel 2005). In addition, we provide sample MATLAB routines for exploring the sandwich model proposed in Köndgen et al., 2008, employing a simple frequdency-domain filtering. The simulations and the MATLAB routines are based on the linear response properties of layer 5 pyramidal cells estimated by injecting a superposition of a small-amplitude sinusoidal wave and a background noise, as in Köndgen et al., 2008. | |
| Retinal Ganglion Cell: I-A (Benison et al 2001) | |
| NEURON mod files for the K-A current from the papers: (model) Benison G, Keizer J, Chalupa LM, Robinson DW. Modeling temporal behavior of postnatal cat retinal ganglion cells. J.Theor.Biol. 210:187-199 (2001) and (experiment) Skaliora I, Robinson DW, Scobey RP, Chalupa LM., Properties of K+ conductances in cat retinal ganglion cells during the period of activity-mediated refinements in retinofugal pathways. Eur.J.Neurosci. 7:1558-1568 (1995). | |
| Retinal Ganglion Cell: I-CaN and I-CaL (Benison et al. 2001) | |
| NEURON mod files for the CaN and CaL currents from the papers: Huang, S.-J. & Robinson, D.W. (1998). Activation and Inactivation properties of voltage-gated calcium currents in developing cat retinal ganglion cells. Neuroscience 85:239-247 (experimental) and Benison G. Keizer J., Chalupa L.M., Robinson D.W., (2001) J. theor. Biol. 210:187-199 (theoretical). | |
| Retinal Ganglion Cell: I-K (Skaliora et al 1995) | |
| NEURON mod files for the K-DR current from the paper: Skaliora I, Robinson DW, Scobey RP, Chalupa LM. Properties of K+ conductances in cat retinal ganglion cells during the period of activity-mediated refinements in retinofugal pathways. Eur J Neurosci 1995 7(7):1558-1568. See the readme.txt file below for more information. | |
| Retinal Ganglion Cell: I-Na,t (Benison et al 2001) | |
| NEURON mod files for the Na current from the papers: (model) Benison G, Keizer J, Chalupa LM, Robinson DW. Modeling temporal behavior of postnatal cat retinal ganglion cells. J Theor Biol. 2001 210:187-99 and a reference from this paper: (experimental) Skaliora I, Scobey RP, Chalupa LM. Prenatal development of excitability in cat retinal ganglion cells: action potentials and sodium currents. J Neurosci 1993 13:313-23. See the readme.txt file below for more information. | |
| Retinal Photoreceptor: I Potassium (Beech, Barnes 1989) | |
| NEURON mod files for a Potassium current from the paper: Beech DJ, Barnes S. Characterization of a voltage-gated K+ channel that accelerates the rod response to dim light. Neuron 3:573-81 (1989). | |
| Rhesus Monkey Layer 3 Pyramidal Neurons: V1 vs PFC (Amatrudo, Weaver et al. 2012) | |
| Whole-cell patch-clamp recordings and high-resolution 3D morphometric analyses of layer 3 pyramidal neurons in in vitro slices of monkey primary visual cortex (V1) and dorsolateral granular prefrontal cortex (dlPFC) revealed that neurons in these two brain areas possess highly distinctive structural and functional properties. ... Three-dimensional reconstructions of V1 and dlPFC neurons were incorporated into computational models containing Hodgkin-Huxley and AMPA- and GABAA-receptor gated channels. Morphology alone largely accounted for observed passive physiological properties, but led to AP firing rates that differed more than observed empirically, and to synaptic responses that opposed empirical results. Accordingly, modeling predicts that active channel conductances differ between V1 and dlPFC neurons. The unique features of V1 and dlPFC neurons are likely fundamental determinants of area-specific network behavior. The compact electrotonic arbor and increased excitability of V1 neurons support the rapid signal integration required for early processing of visual information. The greater connectivity and dendritic complexity of dlPFC neurons likely support higher level cognitive functions including working memory and planning. | |
| 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. | |
| Rod photoreceptor (Barnes and Hille 1989, Publio et al. 2006, Kourennyi and Liu et al. 2004) | |
| This a conductance-based model of a rod photoreceptor cell based on other modeling works (Barnes and Hille 1989 and Publio et al. 2006 and Kourennyi and Liu et al. 2004 ). In this model four types of ionic channels identified in the inner segment of the rod: nonselective cation channel (h), delayed rectifying potassium channel (Kv), noninactivating potassium channel (Kx) and calcium channel (Ca) was used. The model accurately reproduces the rod response when stimulated with a simulated photocurrent signal. We can show the effect of nonselective cation channel. The absence of this channel cause increasing the peak amplitude and the time to reach the peak of voltage response and absence of transient mode in this response. | |
| Role of Ih in firing patterns of cold thermoreceptors (Orio et al., 2012) | |
| " ... Here we investigated the role of Ih in cold-sensitive (CS) nerve endings, where cold sensory transduction actually takes place. Corneal CS nerve endings in mice show a rhythmic spiking activity at neutral skin temperature that switches to bursting mode when the temperature is lowered. ... Mathematical modeling shows that the firing phenotype of CS nerve endings from HCN1−/− mice can be reproduced by replacing HCN1 channels with the slower HCN2 channels rather than by abolishing Ih. We propose that Ih carried by HCN1 channels helps tune the frequency of the oscillation and the length of bursts underlying regular spiking in cold thermoreceptors, having important implications for neural coding of cold sensation. " | |
| STD-dependent and independent encoding of Input irregularity as spike rate (Luthman et al. 2011) | |
| "... We use a conductance-based model of a CN neuron to study the effect of the regularity of Purkinje cell spiking on CN neuron activity. We find that increasing the irregularity of Purkinje cell activity accelerates the CN neuron spike rate and that the mechanism of this recoding of input irregularity as output spike rate depends on the number of Purkinje cells converging onto a CN neuron. ..." | |
| 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). | |
| Salamander retinal ganglian cells: morphology influences firing (Sheasby, Fohlmeister 1999) | |
| Nerve impulse entrainment and other excitation and passive phenomena are analyzed for a morphologically diverse and exhaustive data set (n=57) of realistic (3-dimensional computer traced) soma-dendritic tree structures of ganglion cells in the tiger salamander (Ambystoma tigrinum) retina. | |
| Salamander retinal ganglion cell: ion channels (Fohlmeister, Miller 1997) | |
| A realistic five (5) channel spiking model reproduces the bursting behavior of tiger salamander ganglion cells in the retina. Please see the readme for more information. | |
| Selective control of cortical axonal spikes by a slowly inactivating K+ current (Shu et al. 2007) | |
| We discovered a low-threshold, slowly inactivating K+ current, containing Kv1.2 alpha subunits, in axon initial segment, playing a key role in the modulation of spike threshold and spike duration as well as the spike timing in prefrontal cortex layer V pyramidal cell of ferrets and rats. A kd.mod file implements this D current and put it in the axonal model: Neuron_Dcurrent.hoc. Run the model to see the gradual modulation effect over seconds on spike shape. | |
| Shaping of action potentials by different types of BK channels (Jaffe et al., 2011) | |
| Dentate gyrus granule cells highly express the beta4 accessory subunit which confer BK channels with type II properties. The properties of heterologously-expressed BK channels (with and without the beta4 subunit) were used to construct channel models. These were then used to study how they affect single action potentials and trains of spikes in a model dentate gyrus granule cells (based on Aradi and Holmes, 1999). | |
| 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. | |
| 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. | |
| Signal integration in LGN cells (Briska et al 2003) | |
| Computer models were used to investigate passive properties of lateral geniculate nucleus thalamocortical cells and thalamic interneurons based on in vitro whole-cell study. Two neurons of each type were characterized physiologically and morphologically. Differences in the attenuation of propagated signals depend on both cell morphology and signal frequency. See the paper for details. | |
| Signal integration in a CA1 pyramidal cell (Graham 2001) | |
| This model investigates signal integration in the dendritic tree of a hippocampal CA1 pyramidal cell when different combinations of active channels are present in the tree (Graham, 2001) | |
| Simple and accurate Diffusion Approximation algorithm for stochastic ion channels | |
| " ... We derived the (Stochastic Differential Equations) SDE explicitly for any given ion channel kinetic scheme. The resulting generic equations were surprisingly simple and interpretable – allowing an easy, transparent and efficient (Diffusion Approximation) DA implementation, avoiding unnecessary approximations. The algorithm was tested in a voltage clamp simulation and in two different current clamp simulations, yielding the same results as (Markov Chains) MC modeling. Also, the simulation efficiency of this DA method demonstrated considerable superiority over MC methods, except when short time steps or low channel numbers were used." | |
| Simulated light response in rod photoreceptors (Liu and Kourennyi 2004) | |
| We developed a complete computer model of the rod, which accurately reproduced the main features of the light response and allowed us to demonstrate that it was suppression of Kx channels that was essential for slowing SLR and increasing excitability of rods. The results reported in this work further establish the importance of Kx channels in rod photoreceptor function. | |
| Simulations of motor unit discharge patterns (Powers et al. 2011) | |
| " ... To estimate the potential contributions of PIC (Persistent Inward Current) activation and synaptic input patterns to motor unit discharge patterns, we examined the responses of a set of cable motoneuron models to different patterns of excitatory and inhibitory inputs. The models were first tuned to approximate the current- and voltage-clamp responses of low- and medium-threshold spinal motoneurons studied in decerebrate cats and then driven with different patterns of excitatory and inhibitory inputs. The responses of the models to excitatory inputs reproduced a number of features of human motor unit discharge. However, the pattern of rate modulation was strongly influenced by the temporal and spatial pattern of concurrent inhibitory inputs. Thus, even though PIC activation is likely to exert a strong influence on firing rate modulation, PIC activation in combination with different patterns of excitatory and inhibitory synaptic inputs can produce a wide variety of motor unit discharge patterns." | |
| Site of impulse initiation in a neuron (Moore et al 1983) | |
| Examines the effect of temperature, the taper of the axon hillock, and HH channel density on antidromic spike invasion into the soma and spike initiation under dendritic stimulation. | |
| Small world networks of Type I and Type II Excitable Neurons (Bogaard et al. 2009) | |
| Implemented with NEURON 5.9, four model neurons with varying excitability properties affect the spatiotemporal patterning of small world networks of homogeneous and heterogeneous cell population. | |
| Sodium channel mutations causing generalized epilepsy with febrile seizures + (Barela et al. 2006) | |
| A novel mutation, R859C, in the Nav1.1 sodium channel was identified in a 4-generation, 33-member Caucasian family with a clinical presentation consistent with GEFS+. The mutation neutralizes a positively charged arginine in the domain 2 S4 voltage sensor of the Nav1.1 channel Ą subunit. When the mutation was placed in the rat Nav1.1 channel and expressed in Xenopus oocytes, the mutant channel displayed a positive shift in the voltage-dependence of sodium channel activation, slower recovery from slow inactivation, and lower levels of current compared to the wild-type channel. Computational analysis suggests that neurons expressing the mutant channel have higher thresholds for firing a single action potential and for firing multiple action potentials, along with decreased repetitive firing. Therefore, this mutation should lead to decreased neuronal excitability, in contrast to most previous GEFS+ sodium channel mutations that have changes predicted to increase neuronal firing. | |
| Sodium currents activate without a delay (Baranauskas and Martina 2006) | |
| Hodgkin and Huxley established that sodium currents in the squid giant axons activate after a delay, which is explained by the model of a channel with three identical independent gates that all have to open before the channel can pass current (the HH model). It is assumed that this model can adequately describe the sodium current activation time course in all mammalian central neurons, although there is no experimental evidence to support such a conjecture. We performed high temporal resolution studies of sodium currents gating in three types of central neurons. ... These results can be explained by a model with two closed states and one open state. ... This model captures all major properties of the sodium current activation. In addition, the proposed model reproduces the observed action potential shape more accurately than the traditional HH model. See paper for more and details. | |
| Sodium potassium ATPase pump (Chapman et al. 1983) | |
| The electrochemical properties of a widely accepted six-step reaction scheme for the Na,K-ATPase have been studied by computer simulation. | |
| Space clamp problems in neurons with voltage-gated conductances (Bar-Yehuda and Korngreen 2008) | |
| " ... using numerical simulations, we show that the distortions of voltage-gated K+ and Ca2+ currents are substantial even in neurons with short dendrites. The simulations also demonstrate that passive cable theory cannot be used to justify voltage-clamping of neurons, due to significant shunting to the reversal potential of the voltage-gated conductance during channel activation. ... " | |
| Spatial gridding and temporal accuracy in NEURON (Hines and Carnevale 2001) | |
| A heuristic for compartmentalization based on the space constant at 100 Hz is proposed. The paper also discusses spatio/temporal accuracy and the use of CVODE. | |
| Spike Initiation in Neocortical Pyramidal Neurons (Mainen et al 1995) | |
| This model reproduces figure 3A from the paper Mainen ZF, Joerges J, Huguenard JR, Sejnowski TJ (1995). Please see the paper for detail whose full text is available at http://www.cnl.salk.edu/~zach/methods.html Email Zach Mainen for questions: mainen@cshl.org | |
| Spike exchange methods for a Blue Gene/P supercomputer (Hines et al., 2011) | |
| Tests several spike exchange methods on a Blue Gene/P supercomputer on up to 64K cores. | |
| Spike frequency adaptation in spinal sensory neurones (Melnick et al 2004) | |
| Using tight-seal recordings from rat spinal cord slices, intracellular labelling and computer simulation, we analysed the mechanisms of spike frequency adaptation in substantia gelatinosa (SG) neurones. Adapting-firing neurones (AFNs) generated short bursts of spikes during sustained depolarization and were mostly found in lateral SG. ... Ca2 + -dependent conductances do not contribute to adapting firing. Transient KA current was small and completely inactivated at resting potential suggesting that adapting firing was mainly generated by voltage-gated Na+ and delayed-rectifier K+ (KDR ) currents. ... Computer simulation has further revealed that down-regulation of Na+ conductance represents an effective mechanism for the induction of firing adaptation. It is suggested that the cell-specific regulation of Na+ channel expression can be an important factor underlying the diversity of firing patterns in SG neurones. See paper for more and details. | |
| Spike propagation in dendrites with stochastic ion channels (Diba et al. 2006)) | |
| "We investigate the effects of the stochastic nature of ion channels on the faithfulness, precision and reproducibility of electrical signal transmission in weakly active, dendritic membrane under in vitro conditions. ... We numerically simulate the effects of stochastic ion channels on the forward and backward propagation of dendritic spikes in Monte-Carlo simulations on a reconstructed layer 5 pyramidal neuron. We report that in most instances there is little variation in timing or amplitude for a single BPAP, while variable backpropagation can occur for trains of action potentials. Additionally, we find that the generation and forward propagation of dendritic Ca2+ spikes are susceptible to channel variability. This indicates limitations on computations that depend on the precise timing of Ca2+ spikes." | |
| Spike repolarization in axon collaterals (Foust et al. 2011) | |
| Voltage sensing dye experiments and simulations characterize the location and re-polarizing function of Kv1 channels in cortical neurons. "... (the papers) results indicate that action potential-induced synaptic transmission may operate through a mix of analog–digital transmission owing to the properties of Kv1 channels in axon collaterals and presynaptic boutons." | |
| Spinal Motor Neuron (Dodge, Cooley 1973) | |
| Dodge & Cooley (1973) "Action Potential of the Motorneuron" IBM J. Res. Develop. May 219--229 | |
| Spinal Motor Neuron (McIntyre et al 2002) | |
| Simulation of peripheral nervous system (PNS) mylelinated axon. This model is described in detail in: McIntyre CC, Richardson AG, and Grill WM.(2002) | |
| Spinal Motor Neuron: Na, K_A, and K_DR currents (Safronov, Vogel 1995) | |
| NEURON mod files for the Na, K-A, and K-DR currents from the paper: Safronov, B.V. and Vogel,W. Single voltage-activated Na+ and K+ channels in the somata of rat motorneurons. Journal of Physiology 487.1:91-106 (1995). See the readme.txt file for more information. | |
| Spine fusion and branching effects synaptic response (Rusakov et al 1996, 1997) | |
| This compartmental model of a hippocampal granule cell has spinous synapses placed on the second-order dendrites. Changes in shape and connectivity of the spines usually does not effect the synaptic response of the cell unless active conductances are incorporated into the spine membrane (e.g. voltage-dependent Ca2+ channels). With active conductances, spines can generate spike-like events. We showed that changes like fusion and branching, or in fact any increase in the equivalent spine neck resistance, could trigger a dramatic increase in the spine's influence on the dendritic shaft potential. | |
| 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. | |
| Spiny neuron model with dopamine-induced bistability (Gruber et al 2003) | |
| These files implement a model of dopaminergic modulation of voltage-gated currents (called kir2 and caL in the original paper). See spinycell.html for details of usage and implementation. For questions about this implementation, contact Ted Carnevale (ted.carnevale@yale.edu) | |
| Spontaneous firing caused by stochastic channel gating (Chow, White 1996) | |
| NEURON implementation of model of stochastic channel gating, resulting in spontaneous firing. Qualitatively reproduces the phenomena described in the reference. | |
| Squid axon (Hodgkin, Huxley 1952) (NEURON) | |
| The classic HH model of squid axon membrane implemented in NEURON. Hodgkin, A.L., Huxley, A.F. (1952) | |
| State and location dependence of action potential metabolic cost (Hallermann et al., 2012) | |
| With this model of a layer 5 pyramidal neuron the state and location dependence of the ATP usage and the metabolic efficiency of action potentials can be analyzed. Model parameters were constrained by direct subcellular recordings at dendritic, somatic and axonal compartments. | |
| 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. | |
| Steady-state Vm distribution of neurons subject to synaptic noise (Rudolph, Destexhe 2005) | |
| This package simulates synaptic background activity similar to in vivo measurements using a model of fluctuating synaptic conductances, and compares the simulations with analytic estimates. The steady-state membrane potential (Vm) distribution is calculated numerically and compared with the "extended" analytic expression provided in the reference (see this paper for details). | |
| Stochastic 3D model of neonatal rat spinal motoneuron (Ostroumov 2007) | |
| " ... Although existing models of motoneurons have indicated the distributed role of certain conductances in regulating firing, it is unclear how the spatial distribution of certain currents is ultimately shaping motoneuron output. Thus, it would be helpful to build a bridge between histological and electrophysiological data. The present report is based on the construction of a 3D motoneuron model based on available parameters applicable to the neonatal spinal cord. ..." | |
| Stochastic Ih and Na-channels in pyramidal neuron dendrites (Kole et al 2006) | |
| The hyperpolarization-activated cation current (Ih) plays an important role in regulating neuronal excitability, yet its native single-channel properties in the brain are essentially unknown. Here we use variance-mean analysis to study the properties of single Ih channels in the apical dendrites of cortical layer 5 pyramidal neurons in vitro. ... In contrast to the uniformly distributed single-channel conductance, Ih channel number increases exponentially with distance, reaching densities as high as approximately 550 channels/microm2 at distal dendritic sites. These high channel densities generate significant membrane voltage noise. By incorporating a stochastic model of Ih single-channel gating into a morphologically realistic model of a layer 5 neuron, we show that this channel noise is higher in distal dendritic compartments and increased threefold with a 10-fold increased single-channel conductance (6.8 pS) but constant Ih current density. ... These data suggest that, in the face of high current densities, the small single-channel conductance of Ih is critical for maintaining the fidelity of action potential output. See paper for more and details. | |
| Storing serial order in intrinsic excitability: a working memory model (Conde-Sousa & Aguiar 2013) | |
| " … Here we present a model for working memory which relies on the modulation of the intrinsic excitability properties of neurons, instead of synaptic plasticity, to retain novel information for periods of seconds to minutes. We show that it is possible to effectively use this mechanism to store the serial order in a sequence of patterns of activity. … The presented model exhibits properties which are in close agreement with experimental results in working memory. ... " | |
| Submyelin Potassium accumulation in Subthalamic neuron (STN) axons (Bellinger et al. 2008) | |
| "To better understand the direct effects of DBS (Deep brain stimulation) on central neurons, a computational model of a myelinated axon has been constructed which includes the effects of K+ accumulation within the peri-axonal space. Using best estimates of anatomic and electrogenic model parameters for in vivo STN axons, the model predicts a functional block along the axon due to K+ accumulation in the submyelin space. ... These results suggest that therapeutic DBS of the STN likely results in a functional block for many STN axons, although a subset of STN axons may also be activated at the stimulating frequency. " | |
| Subthreshold inact. of K channels modulates APs in bitufted interneurons (Korngreen et al 2005) | |
| ... In this study we show that in bitufted interneurones from layer 2/3 of the somatosensory cortex, the height and width of APs recorded at the soma are sensitive to changes in the resting membrane potential, suggesting subthreshold activity of voltage-gated conductances. Attributes of K+ currents examined in nucleated patches revealed a fast subthreshold-inactivating K+ conductance (Kf ) and a slow suprathreshold-inactivating K+ conductance (Ks ). Simulations of these K+ conductances, incorporated into a Hodgkin–Huxley-type model, suggested that during a single AP or during low frequency trains of APs, subthreshold inactivation of Kf was the primary modulator of AP shape, whereas during trains of APs the shape was governed to a larger degree by Ks resulting in the generation of smaller and broader APs. ... Compartmental simulation of the back-propagating AP suggested a mechanism for the modulation of the back-propagating AP height and width by subthreshold activation of Kf . We speculate that this signal may modulate retrograde GABA release and consequently depression of synaptic efficacy of excitatory input from neighbouring pyramidal neurones. | |
| 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. | |
| Synaptic information transfer in computer models of neocortical columns (Neymotin et al. 2010) | |
| "... We sought to measure how the activity of the network alters information flow from inputs to output patterns. Information handling by the network reflected the degree of internal connectivity. ... With greater connectivity strength, the recurrent network translated activity and information due to contribution of activity from intrinsic network dynamics. ... At still higher internal synaptic strength, the network corrupted the external information, producing a state where little external information came through. The association of increased information retrieved from the network with increased gamma power supports the notion of gamma oscillations playing a role in information processing." | |
| Synaptic integration in a model of granule cells (Gabbiani et al 1994) | |
| We have developed a compartmental model of a turtle cerebellar granule cell consisting of 13 compartments that represent the soma and 4 dendrites. We used this model to investigate the synaptic integration of mossy fiber inputs in granule cells. See reference or abstract at PubMed link below for more information. | |
| 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. | |
| 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. | |
| 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. | |
| 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) | |
| Synchrony by synapse location (McTavish et al. 2012) | |
| This model considers synchrony between mitral cells induced via shared granule cell interneurons while taking into account the spatial constraints of the system. In particular, since inhibitory inputs decay passively along the lateral dendrites, this model demonstrates that an optimal arrangement of the inhibitory synapses will be near the cell bodies of the relevant mitral cells. | |
| 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. | |
| T channel currents (Vitko et al 2005) | |
| Computer simulations predict that seven of the SNPs would increase firing of neurons, with three of them inducing oscillations at similar frequencises. 3 representative models from the paper have been submited: a wild-type (WT) recombinant Cav3.2 T-channel, and two of the mutants described in the Vitko et al., 2005 paper (C456S and R788C). See the paper for more and details. | |
| T-type Ca current in thalamic neurons (Wang et al 1991) | |
| A model of the transient, low-threshold voltage-dependent (T-type) Ca2+ current is constructed using whole-cell voltage-clamp data from enzymatically isolated rat thalamocortical relay neurons. The T-type Ca2+ current is described according to the Hodgkin-Huxley scheme, using the m3h format, with rate constants determined from the experimental data. | |
| T-type Calcium currents (McRory et al 2001) | |
| NEURON mod files for CaT currents from the paper McRory et al., J.Biol.Chem. 276:3999 (2001). In this paper, three members (alpha-1G, -1H, and -1I) of the LVA calcium channels family were studied. Kinetic parameters were derived from functional expression in transfected cells. | |
| TTX-R Na+ current effect on cell response (Herzog et al 2001) | |
| "Small dorsal root ganglion (DRG) neurons, which include nociceptors, express multiple voltage-gated sodium currents. In addition to a classical fast inactivating tetrodotoxin-sensitive (TTX-S) sodium current, many of these cells express a TTX-resistant (TTX-R) sodium current that activates near -70 mV and is persistent at negative potentials. To investigate the possible contributions of this TTX-R persistent (TTX-RP) current to neuronal excitability, we carried out computer simulations using the Neuron program with TTX-S and -RP currents, fit by the Hodgkin-Huxley model, that closely matched the currents recorded from small DRG neurons. ..." See paper for more and details. | |
| Temperature-Sensitive conduction at axon branch points (Westerfield et al 1978) | |
| Propagation of impulses through branching regions of squid axons was examined experimentally and with computer simulations. The ratio of postbranch/prebranch diameters at which propagation failed was very sensitive to temperature. | |
| Thalamic Relay Neuron: I-T current (Williams, Stuart 2000) | |
| NEURON mod files for the Ca-T current from the paper: Williams SR, Stuart GJ, Action potential backpropagation and somato-dendritic distribution of ion channels in thalamocortical neurons. J Neurosci. 2000 20:1307-17. Contact michele.migliore@pa.ibf.cnr.it if you have any questions about the implementation of the model. | |
| Thalamic Relay Neuron: I-h (McCormick, Pape 1990) | |
| NEURON mod files for the Ih current from the paper: McCormick DA, Pape HC. Properties of a hyperpolarization-activated cation current and its role in rhythmic oscillation in thalamic relay neurones. J. Physiol. 1990 431:291-318. | |
| Thalamic Reticular Network (Destexhe et al 1994) | |
| Demo for simulating networks of thalamic reticular neurons (reproduces figures from Destexhe A et al 1994) | |
| Thalamic interneuron multicompartment model (Zhu et al. 1999) | |
| this is an attempt to recreate a set of simulations originally performed in 1994 under NEURON version 3 and last tested in 1999. When I ran it now it did not behave exactly the same as previously which I suspect is due to some minor mod file changes on my side rather than due to any differences among versions. After playing around with the parameters a little bit I was able to get something that looks generally like a physiological trace in J Neurophysiol, 81:702--711, 1999, fig. 8b top trace. This sad preface is simply offered in order to encourage anyone who is interested in this model to make and post fixes. I'm happy to help out. Simulation by JJ Zhu To run nrnivmodl nrngui.hoc | |
| 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). | |
| Thalamic reticular neurons: the role of Ca currents (Destexhe et al 1996) | |
| The experiments and modeling reported in this paper show how intrinsic bursting properties of RE cells may be explained by dendritic calcium currents. | |
| Thalamocortical and Thalamic Reticular Network (Destexhe et al 1996) | |
| NEURON model of oscillations in networks of thalamocortical and thalamic reticular neurons in the ferret. (more applications for a model quantitatively identical to previous DLGN model; updated for NEURON v4 and above) | |
| 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. | |
| The virtual slice setup (Lytton et al. 2008) | |
| "In an effort to design a simulation environment that is more similar to that of neurophysiology, we introduce a virtual slice setup in the NEURON simulator. The virtual slice setup runs continuously and permits parameter changes, including changes to synaptic weights and time course and to intrinsic cell properties. The virtual slice setup permits shocks to be applied at chosen locations and activity to be sampled intra- or extracellularly from chosen locations. ..." | |
| Tight junction model of CNS myelinated axons (Devaux and Gow 2008) | |
| Two models are included: 1) a myelinated axon is represented by an equivalent circuit with a double cable design but includes a tight junction in parallel with the myelin membrane RC circuit (called double cable model, DCM). 2) a myelinated axon is represented by an equivalent circuit with a double cable design but includes a tight junction in series with the myelin RC circuit (called tight junction model, TJM). These models have been used to simulate data from compound action potentials measured in mouse optic nerve from Claudin 11-null mice in Fig. 6 of: Devaux, J.J. & Gow, A. (2008) Tight Junctions Potentiate The Insulative Properties Of Small CNS Myelinated Axons. J Cell Biol 183, 909-921. | |
| Tonic firing in substantia gelatinosa neurons (Melnick et al 2004) | |
| Ionic conductances underlying excitability in tonically firing neurons (TFNs) from substantia gelatinosa (SG) were studied by the patch-clamp method in rat spinal cord slices. ... Suppression of Ca2+ and KCA currents ... did not abolish the basic pattern of tonic firing, indicating that it was generated by voltage-gated Na+ and K+ currents. ... on the basis of present data, we created a model of TFN and showed that Na+ and KDR currents are sufficient to generate a basic pattern of tonic firing. It is concluded that the balanced contribution of all ionic conductances described here is important for generation and modulation of tonic firing in SG neurons. See paper for more and details. | |
| Tonic neuron in spinal lamina I: prolongation of subthreshold depol. (Prescott and De Koninck 2005) | |
| Model demonstrates mechanism whereby two kinetically distinct inward currents act synergistically to prolong subthreshold depolarization. The important currents are a persistent Na current (with fast kinetics) and a persistent Ca current (with slower kinetics). Model also includes a slow K current and transient Ca current, in addition to standard HH currents. Model parameters are set to values used in Fig. 8A. Simulation shows prolonged depolarizations in response to two brief stimuli. | |
| Tonic-clonic transitions in a seizure simulation (Lytton and Omurtag 2007) | |
| "... The authors have ... computationally manageable networks of moderate size consisting of 1,000 to 3,000 neurons with multiple intrinsic and synaptic properties. Experiments on these simulations demonstrated the presence of epileptiform behavior in the form of repetitive high-intensity population events (clonic behavior) or latch-up with near maximal activity (tonic behavior). ... Several simulations revealed the importance of random coincident inputs to shift a network from a low-activation to a high-activation epileptiform state. Finally, a simulated anticonvulsant acting on excitability tended to preferentially decrease tonic activity." | |
| Transfer properties of Neuronal Dendrites (Korogod et al 1998) | |
| The somatopetal current transfer was studied in mathematical models of a reconstructed brainstem motoneuron with tonically activated excitatory synaptic inputs uniformly distributed over the dendritic arborization. See paper and below readme.txt for more information. | |
| Translating network models to parallel hardware in NEURON (Hines and Carnevale 2008) | |
| Shows how to move a working network model written in NEURON from a serial processor to a parallel machine in such a way that the final result will produce numerically identical results on either serial or parallel hardware. | |
| Updated Tritonia Swim CPG (Calin-Jagemann et al. 2007) | |
| Model of the 3-cell core CPG (DSI, C2, and VSI-B) mediating escape swimming in Tritonia diomedea. Cells use a hybrid integrate-and-fire scheme pioneered by Peter Getting. Each model cell is reconstructed from extensive physiological measurements to precisely mimic I-F curves, synaptic waveforms, and functional connectivity. | |
| Visual Cortex Neurons: Dendritic computations (Archie, Mel 2000) | |
| Neuron and C program files from Archie, K.A. and Mel, B.W. A model of intradendritic computation of binocular disparity. Nature Neuroscience 3:54-63, 2000 The original files for this model are located at the web site | |
| Visual Cortex Neurons: Dendritic study (Anderson et al 1999) | |
| Neuron mod and hoc files for the paper: Anderson, J.C. Binzegger, T., Kahana, O., Segev, I., and Martin, K.A.C Dendritic asymmetry cannot account for directional responses in visual cortex. Nature Neuroscience 2:820:824, 1999 | |
| Vomeronasal sensory neuron (Shimazaki et al 2006) | |
| NEURON model files from the papers: Shimazaki et al, Chem. Senses, epub ahead of print (2006) Electrophysiological properties and modeling of murine vomeronasal sensory neurons in acute slice preparations. The model reproduces quantitatively the experimentally observed firing rates of these neurons under a wide range of input currents. | |
| Xenopus Myelinated Neuron (Frankenhaeuser, Huxley 1964) | |
| Frankenhaeuser, B. and Huxley, A. F. (1964), The action potential in the myelinated nerve fiber of Xenopus Laevis as computed on the basis of voltage clamp data. J. Physiol. 171: 302-315. See README file for more information. | |
ModelDB Home SenseLab Home Help
Questions, comments, problems? Email the ModelDB Administrator
How to cite ModelDB
This site is Copyright 2012 Shepherd Lab, Yale University