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: 195230, 1994).
NEURON demos
The first part of this database is a series of NEURON demo programs related to various cellular and network models that were developed in the laboratory. Each demo reproduces figures of articles published in the literature, in which the models are described in detail, as well as the biological background. Some of these models also appear in the ModelDB database at Yale University. Note: the models described below were simulated using the NEURON simulator written by Michael Hines. The simulations will run straightforwardly provided the Interviews version of NEURON is installed properly. NEURON is publically available on internet via (see the NEURON homepage). For more informations about how to get NEURON and how to install it, please refer to the NEURON home page, or to Michael Hines directly. These demos can be used by anyone interested – the only condition we ask is to give appropriate citation to the original paper(s).

This package shows singlecompartment models of different classes of cortical neurons, such as the “regularspiking” (RS), “fastspiking” (FS), “intrinsically bursting” (IB), “repetitive bursting” (RB) and “lowthreshold spike” (LTS) neurons. The mechanisms included are the Na+ and K+ currents for generating action potentials (INa, IKd), the highthreshold Ltype calcium current (ICaL), the lowthreshold Ttype calcium current (ICaT), and a slow voltagedependent K+ current (IM).
All details are given in the following publication: Martin Pospischil, Maria ToledoRodriguez, Cyril Monier, Zuzanna Piwkowska, Thierry Bal, Yves Frégnac, Henry Markram and Alain Destexhe. Minimal HodgkinHuxley type models for different classes of cortical and thalamic neurons. Biol. Cybernetics 99: 427441, 2008. More instructions are provided in a README file.

This package contains the ionic mechanisms and programs necessary to simulate the model of hyperpolarizationactivated 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 highthreshold calcium current (instead of the lowthreshold calcium current in thalamic neurons). All details are given in the following paper: Winograd M, Destexhe A and SanchezVives MV. Hyperpolarizationactivated graded persistent activity in the prefrontal cortex. Proc. Natl. Acad. Sci. USA 105: 72987303, 2008.

This package simulates a biophysical model of spiketiming dependent plasticity (STDP), which is a form of associative synaptic modification which depends on the respective timing of pre and postsynaptic 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 spiketiming dependent plasticity. International Journal of Neural Systems 16: 7997, 2006.

This package compares different analytic expressions for the steadystate 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 randomlychoosen 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 18:29172922, 2006.

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 steadystate 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 conductancebased synaptic noise.Neural Computation 17: 23012315, 2005.

This demo simulates a model of local field potentials (LFP) with variable resistivity. This model reproduces the lowpass 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 nonconstant 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 frequencyfiltering properties of extracellular space. Biophysical Journal 86:18291842, 2004. More instructions are provided in a README file.

This package simulates synaptic background activity similar to in vivo measurements using a model of fluctuating synaptic conductances. This “pointconductance” model recreates invivolike membrane parameters, such as the depolarized level, the low input resistance, highamplitude membrane potential fluctuations and irregular firing activity. This model is fast enough to be simulated in real time, and has been used to recreate invivolike activity in real neurons in vitro, using dynamicclamp (see details in paper below). The mechanisms included are the Na+ and K+ currents for generating action potentials (INa, IKd), the slow voltagedependent K+ current (IM) and the fluctuating synaptic conductances (Gfluct). All details are given in the following paper: Alain Destexhe, Michael Rudolph, JeanMarc fellous and Terrence J. Sejnowski. Fluctuating synaptic conductances recreate invivolike activity in neocortical neurons. Neuroscience 107: 1324, 2001. More instructions are provided in a README file.

This package shows singlecompartment models of different classes of cortical neurons, such as the “regularspiking”, “fastspiking” and “bursting” (LTS) neurons. The mechanisms included are the Na+ and K+ currents for generating action potentials (INa, IKd), the Ttype calcium current (ICaT), and a slow voltagedependent 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: 9991016, 1998,
Alain Destexhe, Diego Contreras and Mircea Steriade. LTS cells in cerebral cortex and their role in generating spikeandwave oscillations. Neurocomputing 38:555563, 2001
More instructions are provided in a README file.

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 Ltype calcium current (ICaL), a slow voltagedependent K+ current (IM), a slow calciumdependent 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: 14881496, 2000
Alain Destexhe and Denis Paré. Impact of network activity on the integrative properties of neocortical pyramidal neurons in vivo. Journal of Neurophysiology 81: 15311547, 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: 377402, 1998

This package contains the NEURON (.mod) files necessary to simulate conductancebased integrateandfire 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 pulsebased approximation of the HodgkinHuxley model. All details are given in the following paper: Alain Destexhe, Conductancebasedintegrate and fire models. Neural Computation 9: 503514, 1997

This package shows how to implement multicompartment models with active dendritic currents using NEURON. Both detailed (200compartment) and simplified (3compartment) 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 lowthreshold calcium currents in thalamic relay cells. Journal of Neuroscience 18: 35743588, 1998 in which all details are given. More instructions are provided in a README file.

This package shows how to implement multicompartment models with active dendritic currents using NEURON. Both detailed (80compartment) and simplified (3compartment) 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: 169185, 1996 in which all details are given. More instructions are provided in a README file.

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. 125 in which all details are given. More instructions are provided in a README file.

This package is a tutorial for implementing network simulations using the objectoriented 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 HodgkinHuxley 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: 803818, 1994, in which all the details are given. There are also instructions in the README file.

This package is a tutorial for implementing simulations of thalamic networks using the objectoriented 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 HodgkinHuxley type of models whereas synaptic currents are represented by kinetic models (see above). All can be implemented easily in NEURON. The models for cells, voltagedependent currents, calciumdependent 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: 20492070, 1996, in which all the details are given. There are also instructions in the README file.

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: 1418, 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: 195230, 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
The second part of this database consists of PYTHON demos of some of the models and analysis procedures developed in the laboratory. PYTHON is a publicallyavailable package in the standard LINUX distribution and is also available for Windows and Mac. These demos can be used by anyone interested – the only condition we ask is to give appropriate citation to the original paper(s).

This PYTHON package simulates a meanfield model of networks of excitatory and inhibitory neurons, with conductancebased synaptic interactions and single neurons described by the HodgkinHuxley (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 meanfield approach to the dynamics of networks of complex neurons, from nonlinear IntegrateandFire to HodgkinHuxley models. Journal of Neurophysiology 123:10421051, 2020.

This PYTHON package simulates a “biologically realistic” meanfield model of networks of excitatory and inhibitory neurons, with conductancebased synaptic interactions and spikefrequency 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 meanfield models of conductancebased networks of spiking neurons with adaptation. Neural Computation 31: 653680, 2019.

This PYTHON package simulates a meanfield model of networks of excitatory and inhibitory neurons, with conductancebased 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 meanfield model of conductancebased networks of adaptive exponential integrateandfire neurons. Journal of Computational Neuroscience 44: 4561, 2018.

This PYTHON package simulates model networks of excitatory and inhibitory neurons, with conductancebased synaptic interactions and single neurons described by the Adaptive Exponential (AdEx) integrate and fire model. The code is written using the simulatorindependent language PyNN (see neuralensemble.org/trac/PyNN) and can run on any PyNNcompatible 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. Selfsustained asynchronous irregular states and Up/Down states in thalamic, cortical and thalamocortical networks of nonlinear integrateandfire neurons. Journal of Computational Neuroscience 27: 493506, 2009.

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 dynamicclamp 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: 545552, 2009.

This PYTHON package contains the files necessary to implement the STA method to extract spiketriggered 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 eventtriggered average synaptic conductances from the membrane potential. J.Neurophysiol. 97: 25442552, 2007.
MATLAB demos
The third part of this database consists of MATLAB demos of some of the analysis procedures developed in the laboratory. MATLAB is a commercial software produced by Mathworks and which is available for LINUX, Windows and Mac. These demos can be used by anyone interested – the only condition we ask is to give appropriate citation to the original paper(s).
Various Utilities
The third part of this database is a series of utilities of general interest, some of which were developed in the laboratory.

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: 169185, 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.

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 lowthreshold calcium currents in thalamic relay cells. Journal of Neuroscience 18: 35743588, 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 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)
