Demos Database

Contents

Database of NEURON, PYTHON and MATLAB codes, demos and tutorials

Schematic diagram of the kinetic schemes used for modeling ion channels and synaptic transmission. Different processes essential for modeling neuronal behavior can be described by similar type of equations. Voltage dependence, transmitter release, binding and gating of receptors, second messenger action, and neuromodulation can be all described by the same kinetic formalism (see Synthesis of models for excitable membranes, synaptic transmission and neuromodulation using a common kinetic formalism, Journal of Computational Neuroscience 1: 195-230, 1994).

NEURON demos

• Simplified Hodgkin-Huxley models for different types of cortical neurons (zip format)

  This package shows single-compartment models of different classes of cortical neurons, such as the “regular-spiking” (RS), “fast-spiking” (FS), “intrinsically bursting” (IB), “repetitive bursting” (RB) and “low-threshold spike” (LTS) neurons. The mechanisms included are the Na+ and K+ currents for generating action potentials (INa, IKd), the high-threshold L-type calcium current (ICaL), the low-threshold T-type calcium current (ICaT), and a slow voltage-dependent K+ current (IM).

  All details are given in the following publication: Martin Pospischil, Maria Toledo-Rodriguez, Cyril Monier, Zuzanna Piwkowska, Thierry Bal, Yves Frégnac, Henry Markram and Alain Destexhe. Minimal Hodgkin-Huxley type models for different classes of cortical and thalamic neurons. Biol. Cybernetics 99: 427-441, 2008. More instructions are provided in a README file.

• Model of hyperpolarization-activated graded persistent activity (HAGPA) in prefrontal cortex (zip format)

  This package contains the ionic mechanisms and programs necessary to simulate the model of hyperpolarization-activated graded persistent activity (HAGPA) in prefrontal cortical neurons. The mechanism is based on a slow calcium regulation of Ih, similar to that introduced earlier for thalamic neurons (see Destexhe et al., J Neurophysiol. 1996). The main 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). All details are given in the following paper: Winograd M, Destexhe A and Sanchez-Vives MV. Hyperpolarization-activated graded persistent activity in the prefrontal cortex. Proc. Natl. Acad. Sci. USA 105: 7298-7303, 2008.

• Biophysical model of spike-timing dependent plasticity (STDP) (zip format)

  This package simulates a biophysical model of spike-timing dependent plasticity (STDP), which is a form of associative synaptic modification which depends on the respective timing of pre- and post-synaptic spikes. The present biophysical model captures the characteristics of STDP, such as its frequency dependency, and the effects of spike pair or spike triplet interactions. The demo programs reproduce Figures 2 and 3 of the following paper, in which all details are given: Badoual M, Zou Q, Davison AP, Rudolph M, Bal T, Frégnac Y and Destexhe A. Biophysical and phenomenological models of multiple spike interactions in spike-timing dependent plasticity. International Journal of Neural Systems 16: 79-97, 2006.

• Comparison of different analytic expressions for the voltage distribution of neurons subject to conductance-based synaptic noise (zip format)

  This package compares different analytic expressions for the steady-state membrane potential (Vm) distribution of neurons subject to synaptic noise. It contains two parts. First, a scanning program runs the numeric simulations for 10,000 randomly-choosen parameters sets, and writes the results to a data file. Second, an analysis/drawing program reads this data file and creates the histograms shown in the figures of the paper and of the supplementary information. The user can easily change the parameters and verify the simulations shown here, or perform scans in unexplored parameter ranges, and thereby contribute to a more rich analysis of how the different analytic expressions fit numeric simulations. All details are given in the following paper: Rudolph M and Destexhe A. On the use of analytic expressions for the voltage distribution to analyze intracellular recordings. Neural Computation 18:2917-2922, 2006.

• Extended analytic expression for the steady-state membrane potential distribution of conductance-based synaptic noise (zip format)

  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 accompanying paper. To run the demo, unzip this file, compile the mod file mechanism and execute the file “demo.hoc”. All details are given in the following paper: Rudolph M and Destexhe A. An extended analytic expression for the membrane potential distribution of conductance-based synaptic noise.Neural Computation 17: 2301-2315, 2005.

• Simulation of frequency-dependent local field potentials (zip format)

  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 Figure 6 of the paper. The source current is monopolar in this simple case and consists of an EPSP/IPSP sequence followed by an action potential. All details are given in the following paper: Bedard C, Kroger M and Destexhe A. Modeling extracellular field potentials and the frequency-filtering properties of extracellular space. Biophysical Journal 86:1829-1842, 2004. More instructions are provided in a README file.

• Simulations of in-vivo-like activity in neocortical neurons using fluctuating synaptic conductances (zip format)

  This package simulates synaptic background activity similar to in vivo measurements using a model of fluctuating synaptic conductances. This “point-conductance” model recreates in-vivo-like membrane parameters, such as the depolarized level, the low input resistance, high-amplitude membrane potential fluctuations and irregular firing activity. This model is fast enough to be simulated in real time, and has been used to recreate in-vivo-like activity in real neurons in vitro, using dynamic-clamp (see details in paper below). The mechanisms included are the Na+ and K+ currents for generating action potentials (INa, IKd), the slow voltage-dependent K+ current (IM) and the fluctuating synaptic conductances (Gfluct). All details are given in the following paper: Alain Destexhe, Michael Rudolph, Jean-Marc fellous and Terrence J. Sejnowski. Fluctuating synaptic conductances recreate in-vivo-like activity in neocortical neurons. Neuroscience 107: 13-24, 2001. More instructions are provided in a README file.

• Simulations of different classes of cortical neurons (zip format)

  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). All details are given in the following publications: Alain Destexhe and Terrence J. Sejnowski. Thalamocortical Assemblies., Oxford University Press, 2001, Original papers:

  Alain Destexhe, Diego Contreras and Mircea Steriade. Mechanisms underlying the synchronizing action of corticothalamic feedback through inhibition of thalamic relay cells. Journal of Neurophysiology 79: 999-1016, 1998,

  Alain Destexhe, Diego Contreras and Mircea Steriade. LTS cells in cerebral cortex and their role in generating spike-and-wave oscillations. Neurocomputing 38:555-563, 2001

  More instructions are provided in a README file.

• NEURON files for simulating cortical pyramidal neurons (zip format)

  This package contains the NEURON (.mod) files necessary to simulate cortical pyramidal neurons as described in the papers below. The mechanisms included are the Na+ and K+ currents for generating action potentials (INa, IKd), the L-type calcium current (ICaL), a slow voltage-dependent K+ current (IM), a slow calcium-dependent K+ current (IK[Ca]), intracellular calcium, and mechanisms to simulate AMPA, NMDA and GABAa receptors. All details are given in the following papers:

  Nicolas Hô and Alain Destexhe. Synaptic background activity enhances the responsiveness of neocortical pyramidal neurons. Journal of Neurophysiology 84: 1488-1496, 2000

  Alain Destexhe and Denis Paré. Impact of network activity on the integrative properties of neocortical pyramidal neurons in vivo. Journal of Neurophysiology 81: 1531-1547, 1999

  Denis Paré, Erik Lang and Alain Destexhe. Inhibitory control of somatic and dendritic sodium spikes in neocortical pyramidal neurons in vivo: an intracellular and computational study. Neuroscience 84: 377-402, 1998

• NEURON files for simulating conductance-based integrate-and-fire neurons (zip format)

  This package contains the NEURON (.mod) files necessary to simulate conductance-based integrate-and-fire neurons, as described in the paper below. The mechanisms included are the Na+ and K+ currents for generating action potentials (INa, IKd), described by a pulse-based approximation of the Hodgkin-Huxley model. All details are given in the following paper: Alain Destexhe, Conductance-basedintegrate and fire models. Neural Computation 9: 503-514, 1997

• Compartmental models of thalamic relay neurons (zip format)

  This package shows how to implement multicompartment models with active dendritic currents using NEURON. Both detailed (200-compartment) and simplified (3-compartment) models of thalamic relay cells are described in a reference paper. We provide here the complement to simulate the same models using NEURON. The reference paper is: Destexhe, A., Neubig, M., Ulrich, D. and Huguenard, J.R. Dendritic low-threshold calcium currents in thalamic relay cells. Journal of Neuroscience 18: 3574-3588, 1998 in which all details are given. More instructions are provided in a README file.

• Compartmental models of thalamic reticular neurons (zip format)

  This package shows how to implement multicompartment models with active dendritic currents using NEURON. Both detailed (80-compartment) and simplified (3-compartment) models of thalamic reticular cells are described in a reference paper. We provide here the complement to simulate the same models using NEURON. The reference paper is: Destexhe, A., Contreras, D., Steriade, M., Sejnowski, T.J. and Huguenard, J.R. In vivo, in vitro and computational analysis of dendritic calcium currents in thalamic reticular neurons. Journal of Neuroscience 16: 169-185, 1996 in which all details are given. More instructions are provided in a README file.

• Kinetic Models of Synaptic Transmission (zip format)

  This package shows how to implement biophysical models of synaptic interactions using NEURON. Both detailed and simplified models of synaptic currents and most useful types of postsynaptic receptors (AMPA, NMDA, GABA_A, GABA_B, neuromodulators) are described in a reference paper. We provide here the complement to simulate the same models using NEURON. The reference paper is a chapter in the book “Methods in Neuronal Modeling”: Destexhe, A., Mainen, Z.F. and Sejnowski, T.J. Kinetic models of synaptic transmission. In: Methods in Neuronal Modeling , 2nd Edition, Edited by Koch, C. and Segev, I., MIT Press, Cambridge, MA, 1998, p. 1-25 in which all details are given. More instructions are provided in a README file.

• Simulations of Networks of Neurons (zip format)

  This package is a tutorial for implementing network simulations using the object-oriented facilities of NEURON. The example used here is a model of oscillations in networks of thalamic reticular neurons connected with GABAergic synapses. These neurons are bursters and the intrinsic currents are simulated using Hodgkin-Huxley type of models whereas synaptic currents are represented by kinetic models (see above). All can be implemented easily in NEURON. The models for thalamic reticular cells and the synaptic interactions are described in detail in a reference paper. The demo reproduces some figures of that paper. The reference paper is: Destexhe, A., Contreras, D., Sejnowski, T.J. and Steriade, M. A model of spindle rhythmicity in the isolated thalamic reticular nucleus. Journal of Neurophysiology 72: 803-818, 1994, in which all the details are given. There are also instructions in the README file.

• Simulations of Thalamic Oscillations (zip format)

  This package is a tutorial for implementing simulations of thalamic networks using the object-oriented facilities of NEURON. The example used here is amodel of oscillations in networks of thalamocortical and thalamic reticular neurons, interconnected with glutamatergic and GABAergic synapses. These neurons are bursters and the intrinsic currents are simulated using Hodgkin-Huxley type of models whereas synaptic currents are represented by kinetic models (see above). All can be implemented easily in NEURON. The models for cells, voltage-dependent currents, calcium-dependent currents and synaptic currents are described in detail in a reference paper. The demo reproduces some figures of that paper. The reference paper is: Destexhe, A., Bal, T., McCormick, D.A. and Sejnowski, T.J. Ionic mechanisms underlying synchronized oscillations and propagating waves in a model of ferret thalamic slices. Journal of Neurophysiology 76: 2049-2070, 1996, in which all the details are given. There are also instructions in the README file.

• Simulations of Chemical Synapses (tar.gz format)

  This tar file creates a directory containing simple demos for running models of synaptic receptors using the Interviews version of the NEURON simulator. The simulations reproduce figures of the following articles: Destexhe, A., Mainen, Z.F. and Sejnowski,T.J. An efficient method for computing synaptic conductances based on a kinetic model of receptor binding. Neural Computation 6: 14-18, 1994,

  Destexhe, A., Mainen, Z.F. and Sejnowski, T.J. Synthesis of models for excitable membranes, synaptic transmission and neuromodulation using a common kinetic formalism. Journal of Computational Neuroscience 1: 195-230, 1994.

  Please note that this demo is several years old; please download the demo associated with the Methods in Neuronal Modeling chapter (see demo on Kinetic Models of Synaptic Transmission above) for the most recent models of synaptic transmission.

PYTHON demos

• Code to simulate biologically-realistic mean-field models of Hodgkin-Huxley neurons.

  This PYTHON package simulates a mean-field model of networks of excitatory and inhibitory neurons, with conductance-based synaptic interactions and single neurons described by the Hodgkin-Huxley (HH) model. The code is written using the BRIAN simulator (see briansimulator.org). All details are given in the following paper: Carlu, M., Chehab, O., Dalla Porta, L., Depannemaecker, D., Herice, C., Jedynak, M., Koksal Ersoz, E., Muratore, P., Souihel, S., Capone, C., Zerlaut, Y., Destexhe, A. and di Volo, M. A mean-field approach to the dynamics of networks of complex neurons, from nonlinear Integrate-and-Fire to Hodgkin-Huxley models. Journal of Neurophysiology 123:1042-1051, 2020.

• Code to simulate biologically-realistic mean-field models of spiking neurons.

  This PYTHON package simulates a “biologically realistic” mean-field model of networks of excitatory and inhibitory neurons, with conductance-based synaptic interactions and spike-frequency adaptation. Single neurons described by the Adaptive Exponential (AdEx) integrate and fire model. The code is written using the BRIAN simulator (see briansimulator.org). All details are given in the following paper: di Volo, M., Romagnoni, A., Capone, C. and Destexhe, A. Biologically realistic mean-field models of conductance-based networks of spiking neurons with adaptation. Neural Computation 31: 653-680, 2019.

• Code to simulate mean-field models of spiking neurons.

  This PYTHON package simulates a mean-field model of networks of excitatory and inhibitory neurons, with conductance-based synaptic interactions and single neurons described by the Adaptive Exponential (AdEx) integrate and fire model. The code is written using the BRIAN simulator (see briansimulator.org). All details are given in the following paper: Zerlaut, Y., Chemla, S., Chavane, F. and Destexhe, A. Modeling mesoscopic cortical dynamics using a mean-field model of conductance-based networks of adaptive exponential integrate-and-fire neurons. Journal of Computational Neuroscience 44: 45-61, 2018.

• PyNN code to simulate self-sustained asynchronous irregular states and Up/Down states in networks of nonlinear integrate-and-fire neurons (zip format)

  This PYTHON package simulates model networks of excitatory and inhibitory neurons, with conductance-based synaptic interactions and single neurons described by the Adaptive Exponential (AdEx) integrate and fire model. The code is written using the simulator-independent language PyNN (see neuralensemble.org/trac/PyNN) and can run on any PyNN-compatible simulator such as NEURON, BRIAN or NEST. The code was ported to PyNN by Andrew Davison and Lyle Muller. All details are given in the following paper: Destexhe, A. Self-sustained asynchronous irregular states and Up/Down states in thalamic, cortical and thalamocortical networks of nonlinear integrate-and-fire neurons. Journal of Computational Neuroscience 27: 493-506, 2009.

• Method to extract synaptic conductances from single membrane potential traces (zip format)

  This PYTHON package implements a method to estimate synaptic conductances from single membrane potential traces (the “VmT method”), as described in Pospischil et al. (2009). The method uses a maximum likelihood procedure and was successfully tested using models and dynamic-clamp experiments in vitro (see paper for details). All details are given in the following paper: Pospischil, M., Piwkowska, Z., Bal, T. and Destexhe, A. Extracting synaptic conductances from single membrane potential traces. Neuroscience 158: 545-552, 2009.

• Method to calculate spike-triggered average (STA) of conductances from membrane potential traces (zip format)

  This PYTHON package contains the files necessary to implement the STA method to extract spike-triggered average conductance traces from membrane potential recordings. The method is based on a maximum likelihood procedure. All details are given in the following paper: Pospischil M, Piwkowska Z, Rudolph M, Bal T and Destexhe A. Calculating event-triggered average synaptic conductances from the membrane potential. J.Neurophysiol. 97: 2544-2552, 2007.

MATLAB demos

• Extracting conductances from the membrane potential (zip format)

  This MATLAB code implements a method to extract the time course of excitatory and inhibitory conductances from single trial membrane potential recordings. The code is based on the following article: Bédard C, Béhuret S, Deleuze C, Bal T and Destexhe A. Oversampling method to extract excitatory and inhibitory conductances from single-trial membrane potential recordings. J. Neurosci. Meth. 210: 3-14, 2012. The demo reproduces several figures of this paper. It was written by Claude Bedard.

• Method to analyze spatiotemporal correlations from spike trains (zip format)

  This MATLAB code implements a model-based analysis of spike trains. The analysis predicts the occurrence of spatio-temporal patterns of spikes in the data, and is based on a maximum entropy principle by including both spatial and temporal correlations. The approach is applicable to unit recordings from any region of the brain. The code is based on the following article: Marre O, El Boustani S, Fregnac Y and Destexhe A. Prediction of spatio-temporal patterns of neural activity from pairwise correlations. Physical Review Letters 102:138101, 2009. This MATLAB code was written by Sami El Boustani and Olivier Marre.

Various Utilities

• Generation of MPEG, AVI or GIF animations from NEURON (zip format)

  The package illustrates how to create animations from NEURON. The example taken generates MPEG or GIF animations of the spatial distribution of membrane potential during bursting in a model of thalamic reticular neuron, relative to the paper: Destexhe, A., Contreras, D., Steriade, M., Sejnowski, T.J. and Huguenard, J.R. In vivo, in vitro and computational analysis of dendritic calcium currents in thalamic reticular neurons. Journal of Neuroscience 16: 169-185, 1996 in which all biological/modeling details are given. The demo is for LINUX (works with Ubuntu 12.4), and requires several packages to be installed. The principle is to generate a series of GIF frames, and then build a movie file from these frames. Please see the README file for a description of the procedure.

• Utility to collapse a dendritic tree into three equivalent compartments using NEURON (zip format)

  This demo program illustrates how to create a reduced model of a complex morphology using NEURON. The program uses a principle of conservation of the axial resistance. The collapse is made such as the collapsed dendritic structure preserves the axial resistance of the original structure. The algorithm works by merging successive pairs of dendritic branches into an equivalent branch (a branch that preserves the axial resistance of the two original branches). This merging of branches can be done according to different methods selectable in the present code (see README for details). This program has been used in the following article: Destexhe, A., Neubig, M., Ulrich, D. and Huguenard, J.R. Dendritic low-threshold calcium currents in thalamic relay cells. Journal of Neuroscience 18: 3574-3588, 1998 in which details of the method are given. More instructions are provided in a README file.

• NTSCABLE

  This program translates digitized morphological descriptions of a neuron into files which can be used directly by NEURON. NTSCABLE was originally written by J.C. Wathey at the Salk Institute, and was intended to convert data files in the syntax of the Neuron Tracing System (Eutectic Electronics) into CABLE format, the predecessor of NEURON (hence the name “ntscable”). The program is now compatible with NEURON and can convert data files generated by various digitizing systems, including EUTECTIC, Douglas (2D and 3D), Nevin and NEUROLUCDIA (Microbrightfield) format for the last version (NTSCABLE 2.01). This program is public domain, works straightforwardly on UNIX or LINUX workstations and there is a relatively detailed documentation available. To access the documentation on NTSCABLE, click here and to get the last version of this package including code sources, click here.

• SCoP MANUAL

  SCoP is a general tool for solving different types of mathematical problems and is the heart of the NEURON simulator. The NMODL language is based on SCoP, and all SCoP functions and features can be used within NMODL. SCoP features include the ability to solve differential equations, kinetic equations (or diagrams), partial differential equations, algebraic equations and more. There are many utility functions such as curve fitting, probability functions, random number generation, etc. The inclusion of SCoP is one of the features that make NEURON particularly powerful — it can solve problems that go beyond the strict framework of membrane equations (for example diffusion of compounds, etc). Description of the SCoP language (language description, all utility functions are described here) NMODL Language (1991) (please see the NEURON website for more recent versions) Unit checking utility for NMODL (please see the NEURON web site for more recent versions)