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 home
page). 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, Conductancebased
integrate 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 a
model 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 https://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
https://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
https://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
http://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 web
site for more recent versions)
Unit checking
utility for NMODL (please see the NEURON web site for more recent
versions)
For more information, please contact:
Department of Integrative and Computational Neuroscience (ICN),
ParisSaclay Institute of Neuroscience (NeuroPSI),
CNRS, Bat 33,
1 Avenue de la Terrasse,
91198 GifsurYvette, France.
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