Equipe-Destexhe

Dynamique des réseaux thalamocorticaux

Description des axes de recherches actuels, ainsi que des liens vers une information plus détaillée.

Read more: Research themes of the laboratory and overview of publications

The research conducted in this laboratory stands at the interface between several disciplines, such as biophysics, physics and neuroscience. The themes investigated (see below) range from the microscopic (single neurons) to the macroscopic (networks or populations of neurons) aspects of the central nervous system function. We use theoretical methods and computer-based simulation techniques to explore the complex behavior of single neurons and understand their basic integrative properties (see The integrative properties of cortical neurons in vivo, The integrative properties of thalamic neurons). This task requires to integrate details about experimental measurements of these neurons, their morphology, their biophysical properties, as well as the properties of their synaptic inputs (see Biophysical models of synaptic transmission). There is also a need for a constant and continuous exchange with experimentalists recording single cells (intracellular measurements).

At the network level, we try to understand the collective behavior of neuronal populations, which in many cases cannot be simply deduced from single-cell behavior. In cerebral cortex and thalamus, neurons are characterized by complex intrinsic properties (see above) and also influence each-other through many different types of synaptic interactions involving different classes of receptors. These networks are therefore highly complex and computational methods can be particularly pertinent in predicting their behavior. This approach was followed for the case of oscillatory behavior in thalamus and cortex (see Network models of thalamic oscillations and Network models of thalamocortical oscillations). Models can also be used to understand the genesis of pathological behavior such as epileptic seizures (see Network models of epileptic discharges). Here again, a tight relation with experimental data is needed.

A lot of attention has been focused recently on the population level modeling of large cortical networks. To do this, one needs to design population level models, using mean-field techniques (see Mean-field models of neuronal populations). This type of model is appropriate to investigate large scales, such as for example the propagating waves seen over millimeter distances in primary visual cortex, or slow-wave dynamics over centimeter distances in the whole human brain. The mean-field models can integrate biophysical features like the synaptic conductances, spike-frequency adaptation, the different cell types and their excitability, and even the heterogeneity of neurons in cortex. It can also include the differential gain of excitatory and inhibitory neurons, and its emergent properties at the mesoscopic or macroscopic scale.

Finally, another aspect of computational neuroscience is to directly provide methods to analyze experimental data. Single- or multi-electrode recordings often reveal complex behavior which may not be easy to analyze. Such complex signals can be analyzed in many different ways with the help of theoretical approaches (see Spatiotemporal analysis of electrophysiological data). In some cases, the theory can help analyzing complex, apparently random signals. This is the case for intracellular recordings of “synaptic noise”, from which many useful information can be extracted (see Stochastic analysis of synaptic noise).

These different approaches have been summarized in the following review papers by Destexhe’s group

See also the Research Grants page for more details about current funding and on-going research projects, as well as possible PhD or postdoc opportunities.

Read more: Research themes of the laboratory and overview of publications.

[/su_spoiler] [su_spoiler class=”my-custom-spoiler” open=”no” title=”DEMOS” style=”font-size:7px;”] Demos et code source des programmes de simulation qui ont généré certaines des figures des articles publiés. Ces demos sont associées au simulateur NEURON (accès public), PYTHON ou MATLAB. Elles peuvent être utilisées comme tutorials pour apprendre à utiliser NEURON, comme outil didactique, ou comme outil d’enseignement.

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

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).

• 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

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 publically-available 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).

• BRIAN 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.

• BRIAN 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.

• BRIAN 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

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).

• 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

The third part of this database is a series of utilities of general interest, some of which were developed in the laboratory.

• 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)

Database of Computer-generated Animations

Contents

This database of mpeg and avi movies and other files allows one to visualize the models and experimental data described in the articles. The movies are complementary as they describe important aspects of the dynamical behavior not apparent in static figures; they are most of the time directly related to figures of the published papers. In some cases, demo packages for simulations are also available and reproduce figures of the corresponding papers. Please refer to the database of publications for all biological details. These animations can be used by anyone interested – the only condition we ask is to give appropriate citation to the original paper(s).

COMPUTER-GENERATED ANIMATIONS

These computer-generated animations should be playable on any recent LINUX distribution, or Windows (in principle, they do not require any nonstandard codec or other application to be played).

• Forward-propagating dendritic spikes with and without background activity in neocortical pyramidal cells

  These simulations are based on the following papers:

  Michael Rudolph and Alain Destexhe. A fast conducting, stochastic integrative mode for neocortical neurons in vivo. Journal of Neuroscience 23: 2466-2476, 2003.

  Alain Destexhe, Michael Rudolph and Denis Paré. The high-conductance state of neocortical neurons in vivo. Nature Reviews Neuroscience 4: 739-751, 2003.

  Alain Destexhe. High-Conductance State. Scholarpedia 2(11): 1341 (2007).

  These movie files illustrate the effect of synaptic noise on the propagation of dendritic spikes in a simulated neocortical layer VI pyramidal neuron. They are an excellent complement to the figures of the above papers. The somatodendritic distribution of membrane potential is shown by colors in two cases of dendritic action potential generation.

  stim-only.mpg, stim-only.avi

  Forward-propagating action potential in a simulated neocortical layer VI pyramidal neuron. The color codes for the membrane potential, from deep blue (-90 mv) to yellow (-40 mV). Sodium and potassium currents were distributed with low density in soma and dendrites, and higher density in the axon. This simulation shows a dendritic action potential elicited by an excitatory synaptic stimulus in the distal dendrite. The action potential propagated forward, but failed to reach the soma. See Fig. 1C (Quiescent) of J. Neurosci. 23: 2466-2476, 2003..

  stim+background2.mpg, stim+background2.avi

  Forward-propagating action potential in a simulated neocortical layer VI pyramidal neuron. Same simulation as above, but in the presence of synaptic background activity (high-conductance state). In this case, the action potential propagated forward and succeeds to reach the soma. See Fig. 1C (In Vivo-Like) of J. Neurosci. 23: 2466-2476, 2003..

  background-only.mpg, background-only.avi

  Higher resolution movie showing the background activity only in the same layer VI pyramidal neuron as above. This animations shows well the “traffic” of forward- and back-propagating action potentials in dendrites under in vivo-like conditions. See J. Neurosci. 23: 2466-2476, 2003.

• Backpropagating action potentials in neocortical pyramidal cells

  These simulations are based on the papers:

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

  Destexhe A, Lang E and Paré D. Somato-dendritic interactions underlying action potential generation in neocortical pyramidal cells in vivo. In: Computational Neuroscience. Trends in Research (edited by J. Bower), Plenum Press, New York, 1998, pp. 233-238.

  These movie files illustrate the dynamics of membrane potential in soma and dendrites in a simulated neocortical layer V pyramidal neuron. They are an excellent complement to the figures of the paper. The somatodendritic distribution of membrane potential is shown by colors in three cases of action potential generation

  backp_control.mpg

  Backpropagating action potential in a simulated neocortical layer V pyramidal neuron. The color codes for the membrane potential, from deep blue (-90 mv) to yellow (-40 mV). Sodium and potassium currents were distributed with low density in doma and dendrites, and high density in the axon. This simulation shows an action potential elicited by current injection in the soma. The action potential propagated retrogradely into the dendrites. See Fig. 9 of Neuroscience 84: 377-402, 1998 .

  backp_stim_dist.mpg

  Action potential elicited by stimulation of synapses in the distal dendrites. The action potential initiated distally and propagated towards the soma. See Fig. 14 of Neuroscience 84: 377-402, 1998 .

  backp_stim_prox.mpg

  Action potential elicited by stimulation of synapses in soma and proximal dendrites. The action potential initiated proximally but did not back-propagate in more distal dendrites. The amplitude of the action potential, as seen from the soma, was reduced in amplitude and duration. See Fig. 14 of Neuroscience 84: 377-402, 1998 .

• Dendritic calcium currents in thalamic reticular neurons

  These simulations are based on 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

  These movie files illustrate the dynamics of membrane potential in soma and dendrites of thalamic reticular neurons. They are an excellent complement to the figures of the paper. The somatodendritic distribution of membrane potential is shown by colors during a burst of action potentials. In particular, see how distal dendrites are maintained at a depolarized level, “feeding” the soma with current during the burst.

  dendritRE.mpg

  Dendritically-generated burst in a simulated thalamic reticular neuron. The color codes for the membrane potential, from deep blue (-90 mv) to yellow (-40 mV). Distal dendrites contain high densities of T-current and generate most of the calcium spike, “feeding” the soma with depolarizing current during the burst. The soma contained sodium/potassium currents responsible for action potentials and lower densities of T-current. The genesis of the burst by dendrites accounts for many electrophysiological properties of these neurons. (large size movie, also contains a plot of the membrane potential at the soma) See Fig. 8 of J. Neurosci. 16: 169-185, 1996 .

  dendritRE_full.mpg

  (medium size movie)

  dendritRE_short.mpg

  (small size movie)

• Propagating synchronized oscillations in thalamic networks:

  These simulations are based on the paper: 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 ofNeurophysiology 76: 2049-2070, 1996

  spindle.mpg

  9-11 Hz spindle oscillation in a 1-dim network of 50 TC and 50 RE cells with intact connectivity. Extent of axonal projections: 11 cells; 2000 frames with 2ms between frames; 8pixel/frame; t=28 sec to 32 sec. Color scale: -90 (blue) to -40 mV and over (yellow). See Fig. 12 of J. Neurophysiol. 76: 2049-2070, 1996 .

  bicuculline.mpg

  3-4 Hz bicuculline-induced oscillation in a 1-dim network of 50 TC and 50 RE cells following block of GABA(A) receptors. Extent of axonal projections: 11 cells; 2200 frames with 2ms between frames; 8pixel/frame; t=28.6 sec to 33 sec. Color scale: -90 (blue) to -40 mV and over (yellow). See Fig. 13 of J. Neurophysiol. 76: 2049-2070, 1996. These mpeg movies show experimental data from the paper: Contreras, D., Destexhe, A., Sejnowski, T.J. and Steriade, M. Control of spatiotemporal coherence of a thalamic oscillation by corticothalamic feedback. Science 274: 771-774, 1996

  thalamus_intact_fast.mpg

  Spatiotemporal maps of the distribution of electrical activity across the thalamus during spindle oscillations recorded by eight equidistant tungsten electrodes in cats during barbiturate anesthesia. These animations are animated versions of Fig. 2 of the paper. The spatiotemporal maps were constructed as follows: a color was assigned to the value of the local field potential (LFP) at each electrode; the color scale ranged in 10 steps from the baseline (blue) to -100 microvolts (yellow); the LFPs from anterior to posterior are shown from left to right; time was divided in frames each representing a snapshot of 4ms of thalamic activity and arranged in a column from top to bottom. The whole frame is shifted downwards as time evolves (similar to a chart recorder), for better visualization of the spread of activity. In this type of representation, synchronized oscillations appear as vertical stripes. This animation corresponds to multisite recordings in the thalamus with intact cortex and shows the large-scale synchrony of oscillations.

  thalamus_intact_slow.mpg

  Same animation at a slower time scale.

  thalamus_decort_fast.mpg

  Same representation of multisite recordings of thalamic oscillations, after removal of the cortex (decorticate). The electrodes were placed in approximately the same locations. Although each site individually oscillated at the same frequency as with intact cortex, the large-scale synchrony of the oscillations was disrupted following removal of the cortex.

  thalamus_decort_slow.mpg

  Same animation at a slower time scale.

• Simulations of the thalamic reticular nucleus:

  These simulations are based on the paper:

  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

  re100.mpg

  N=100, 3rd neighb, 2ms between frames, 10pixel/frame 1000 frames, from t=5 sec to 6 sec; -90 (white) to -60 mV (black) See Fig. 9 in J. Neurophysiol. 72: 803-818,1994 .

  re400.mpg

  N=400, 3rd neighb, 2ms between frames, 5pixel/frame 1000 frames, from t=4 sec to 5 sec; -90 (white) to -60 mV (black)

  re400_spiral.mpg

  N=400, 1st neighb, 2ms between frames, 5pixel/frame 1000 frames, from t=4 sec to 5 sec; -90 (white) to -60 mV (black) See Fig. 10 in J. Neurophysiol. 72: 803-818, 1994 .

  re1600.mpg

  N=1600, 3rd neighb, 2ms between frames, 5pixel/frame 1000 frames, from t=4 sec to 5 sec; -90 (white) to -60 mV (black)

• Spatiotemporal patterns in networks of excitatory and inhibitorycells:

  These simulations are based on the paper: Oscillations, complex spatiotemporal behavior and information transport in networks of excitatory and inhibitory neurons, by Alain Destexhe, published in Physical Review E 50: 1594-1606, 1994.

  intermittency.mpg

  N=100, M=25, 1st neighb, 1.2ms between frames, 10 pixel/frame, 1000 frames, from t=0 to t=1.2 sec; -80 (white) to +50 mV (black). See Fig.4 in Physical Review E50:1594-1606, 1994.

  spiral.mpg

  N=100, M=25, 1st neighb, 1.2ms between frames, 10 pixel/frame, 1000 frames, from t=0 to t=1.2 sec; -80 (white) to +50 mV (black). See Fig.5 and 6a in Physical Review E50: 1594-1606, 1994.

  chaos.mpg

  N=6400, M=1600, 2nd neighb, 2ms between frames, 2 pixel/frame, 100 frames, from t=0.1 to t=0.2 sec; -80 (white) to +50 mV (black). See Fig.7 in Physical Review E50: 1594-1606, 1994.

Database of Cellular Morphologies

This database provides 3-dim reconstructions of thalamic and neocortical neurons. These cells were reconstructed by Alain Destexhe from 80-micron serial sections using a computerized tracing system (Eutectic Electronics, Raleigh, NC) kindly provided by D. Amaral (University of California, Davis, CA), as well as a Neurolucida (Microbrightfield, Williston, VT) tracing system in Destexhe’s lab. The dendritic morphology and diameters were reconstructed in 3-dim using a X 100 objective and correction for tissue shrinkage was included, leading to a theoretical accuracy of 0.1 microns on dendritic diameters. The morphology and the modeling of these cells is described in detail in the attached references given below for each cell.

The NEURON geometry of these cells are also available. Anyone is welcome to use these geometries in models. If you use them, we kindly ask you to cite the original paper in which these cells were published. The references are attached below, and copies of the papers are available in the present site.

• Layer V Neocortical Pyramidal Neuron

  

  Layer V pyramidal cell from cat association cortex. This cell was stained with neurobiotin and reconstructed using a X100 objective (Neurolucida). The physiology, morphology and modeling of that cell are described in: Rudolph M, Pelletier J-G, Paré D and Destexhe A. (2005) Characterization of synaptic conductances and integrative properties during electrically-induced EEG-activated states in neocortical neurons in vivo. Journal of Neurophysiology 94: 2805-2821. This cell has 4 primary dendritic branches with a total area of 22481 square-microns. The staining was not very sharp, so the reconstruction may have missed parts of the distal dendrites. The geometry was corrected for tissue shrinkage. Some spines were visible in the optical microscope (X100).

  Click here to download the NEURON geometry.

  Click here to get a NEURON file to visualize this neuron.

• Layer V Neocortical Pyramidal Neuron

  

  Layer V pyramidal cell from cat somatosensory cortex. This cell was stained with neurobiotin and reconstructed using a X100 objective. The physiology, morphology and modeling of that cell are described in: Contreras D, Destexhe A and Steriade M (1997) Intracellular and computational characterization of the intracortical inhibitory control of synchronized thalamic inputs in vivo. Journal of Neurophysiology 78: 335-350. This cell has 9 primary dendritic branches with a total dendritic length of 22173 microns and an area of 91620 square-microns. The staining was very sharp and the reconstruction could be made accurately for all dendrites. The total membrane area of this cell is exceptionally large. With a density of 0.6 spines per square micron (Larkman AU, J. Comp. Neurol. 306: 332-343, 1991), this cells probably receives about 50,000 spines. Spines were indeed visible in the optical microscope (X100).

  Click here to download the NEURON geometry.

  Click here to get a NEURON file to visualize this neuron.

• Layer VI Neocortical Pyramidal Neuron

  

  Layer VI pyramidal cell from cat somatosensory cortex. This cell was stained with neurobiotin and reconstructed using a X100 objective. The physiology of that cell and the modeling are described in: Contreras D, Destexhe A and Steriade M (1997) Intracellular and computational characterization of the intracortical inhibitory control of synchronized thalamic inputs in vivo. Journal of Neurophysiology 78: 335-350. This cell has 5 primary dendritic branches with a total dendritic length of 7576 microns and an area of 31225 square-microns. The cell was very sharply stained and the reconstruction could be done accurately. The fact that the apical trunk does not arborize in superficial layers did not appear to be a consequence of the staining or reconstruction procedure. Spines were visible in the optical microscope (X100).

  Click here to download the NEURON geometry.

  Click here to get a NEURON file to visualize this neuron.

• Thalamic Relay Neuron

  

  Thalamocortical relay neuron from rat ventrobasal nucleus. This cell was stained with biocytin. The neuron is described in: Huguenard JR and Prince DA (1992) A novel T-type current underlies prolonged calcium-dependent burst firing in GABAergic neurons of rat thalamic reticular nucleus. Journal of Neuroscience 12: 3804-3817. The morphology, passive cable properties and modeling of this neuron are described in: Destexhe A, Neubig M, Ulrich D and Huguenard JR (1998) Dendritic low-threshold calcium currents in thalamic relay cells. Journal of Neuroscience 18: 3574-3588. This cell has 11 primary dendrites, having a total length of 7095 microns; the total membrane area is 23980.5 square-microns, including 2625 square-microns for the soma which is about 20-25 micron diameter. The tracing of the diameters of some distal dendrites could not be performed precisely due to biocytin artifacts although lengths and branching patterns were accurately reconstructed. In those cases, the diameters were artificially rescaled such as to match the diameter profile of dendritic segments that could be reconstructed accurately. The dendritic arborizations tend to be organized in a bush-like structure, similar to previous morphological observations (Jones EG, The Thalamus, Plenum Press, New York, 1985).

  Click here to download the NEURON geometry.

  Click here to get a NEURON file to visualize this neuron.

• Thalamic Reticular Neuron

  

  Thalamic reticular cell, from the reticular sector of rat ventrobasal nucleus. This cell was stained with biocytin. The neuron is described in: J.R. Huguenard & D.A. Prince, A novel T-type current underlies prolonged calcium-dependent burst firing in GABAergic neurons of rat thalamic reticular nucleus. (1992) J. Neurosci. 12: 3804-3817. The morphology, passive cable properties and modeling of this neuron are described in: Destexhe A, Contreras D, Steriade M, Sejnowski TJ and Huguenard JR (1996) In vivo, in vitro and computational analysis of dendritic calcium currents in thalamic reticular neurons. Journal of Neuroscience 16: 169-185. This cell has 4 primary dendrites, having a total length of 3785 microns; the total membrane area is 15,115 square-microns, including 1760 square-microns for the soma which has a diameter of about 20-25 microns. The dendritic arborizations tends to spread in planes parallel to the long axis of the thalamic reticular nucleus, as described previously (Ramon y Cajal S, Histologie du Syst&ème Nerveux de l’Homme et des Vertébrés, Maloine, Paris, 1909). The staining was very sharp and the reconstruction could be made accurately (X100 objective) for the entire dendritic tree. No spines were visible in the optical microscope.

  Click here to download the NEURON geometry.

  Click here to get a NEURON file to visualize this neuron.

[/su_spoiler] [su_spoiler class=”my-custom-spoiler” open=”no” title=”MUSIQUE NEURONALE” style=”font-size:7px;”] Mélodies crées à partir d’enregistrements de neurones du cortex cérébral. Elles sont comparées à des mélodies obtenues à partir de processus stochastiques de Poisson.

Neuronal Music

This page provides audio files where neuronal activity is “visualized” by creating music. The music is created from neuronal spikes recorded extracellularly with multiple electrodes, either from the parietal cortex of awake and naturally-sleeping cats (taken from Destexhe et al., J. Neurosci, 1999), or from the temporal cortex of awake and naturally sleeping human subjects (taken from Peyrache et al., Proc. Natl. Acad. Sci. USA, 2012). We have made two types of neuronal music, a first “simple” type, consists of a direct translation of spike sequences into note sequences, as detailed in the Neuronal Melodies page. This type of musical animation is available for cat parietal cortex and human temporal cortex.

• Click here for the Neuronal Melodies page

A second attempt was made more recenty, in collaboration with Luc Foubert (CNRS). In the Spikiss Project, we have created more elaborated music based on associations with multiple and sophisticated sounds. We also explained step by step how the conversion to music was made.

• Click here for the Spikiss Project page
• Click here for the Spikiss explanation page

A third direction, is the myWaves Project. Here, we have created elaborated music based on electro-encephalogram (EEG) recordings of sleeping subjects. This makes use of a new technique called NeuroAcoustic Transduction. A startup company was created, myWaves Technologies. 

• Click here for the myWaves Project page

We are presently continuing to work on the Spikiss and myWaves projects, using respectively spike and EEG recordings

. [/su_spoiler] [su_spoiler class=”my-custom-spoiler” open=”no” title=”LIENS” style=”font-size:7px;”] Liens à divers sites de Neurosciences Computationnelles et Neuroinformatique.

Computational Neuroscience & Neuroinformatics Links

For a recent list of links, see the Computational Neuroscience page in Wikipedia

Conferences and Courses

• The Annual Computational Neuroscience (CNS) Meeting
• The CoSyNe Conference
• The Neural Information Processing Systems (NIPS) Conference
• The Advanced Course in Computational Neuroscience
• The NeuroComp Conference (French Computational Neuroscience)

  

Journals

• The Journal of Computational Neuroscience
• Neural Computation
• Neural Networks
• Network: Computation in Neural Systems
• Neurocomputing
• PLOS Computational Biology

Computational Neuroscience Resources

• Scholarpedia (Encyclopedia of Computational Neuroscience)
• The ModelDB database

Computational Neuroscience Projects and Consortia

• The INCF (International Neuroinformatics Coordinating Facility)
• The neuroinf.org website
• The Neuro-IT network
• The Blue Brain Project
• The FACETS Project
• The HR-CORTEX Project

List of Computational Neuroscience Laboratories

• Computational Neuroscience on the Web
• Computational Neuroscience in Europe

See also the ICN department of the NeuroPSI Institute (Neurosciences Intégratives & Computationnelles — Integrative & Computational Neuroscience)

[/su_spoiler] [/su_accordion]

The Journal of Computational Neuroscience

From neurons to behavior: A journal at the interface between experimental and theoretical neuroscience

The Journal of Computational Neuroscience provides a forum for papers that fit the interface between computational and experimental work in the neurosciences. The Journal of Computational Neuroscience publishes full length original papers, rapid communications and review articles describing theoretical and experimental work relevant to computations in the brain and nervous system. Papers that combine theoretical and experimental work are especially encouraged. Primarily, theoretical papers should deal with issues of obvious relevance to biological nervous systems. Experimental papers should have implications for the computational function of the nervous system, and may report results using any of a variety of approaches including anatomy, electrophysiology, biophysics, imaging, and molecular biology. Papers investigating the physiological mechanisms underlying pathologies of the nervous system, or papers that report novel technologies of interest to researchers in computational neuroscience are also welcomed. It is anticipated that all levels of analysis from cognitive to cellular will be represented in the Journal of Computational Neuroscience. Published by Springer Science Business Media B.V., ISSN: 0929-5313 (paper) and 1573-6873 (online).

Links

• General Information about the journal
• Editorial Board
• Online version of the journal
• Electronic Submission

Springer Series in Computational Neuroscience

Editors: Alain Destexhe and Romain Brette

The Springer Series in Computational Neuroscience gathers monographs and edited volumes on all aspects of computational neuroscience, including theoretical and mathematical neuroscience, biophysics of the brain, models of neurons and neural networks, and methods of data analysis (e.g. information theory). The editors welcome suggestions and projects for inclusion in the series.

[/su_spoiler] [su_spoiler class=”my-custom-spoiler” open=”no” title=”PERSONNEL” style=”font-size:7px;”] Liste des membres du laboratoire, permanents, postdocs et étudiants.

Present lab members

• Jules Bouté (PhD student)
• Mallory Carlu (Postdoc Researcher)
• Damien Depannemaeker (Postdoc Researcher)
• Alain Destexhe (CNRS Research director)
• Anton Filipchuk (Postdoc Researcher; co-supervised with Brice Bathellier, ICN)
• Alexis Garcia (Postdoc Researcher)
• Jennifer Goldman (Postdoc Researcher)
• Tomasz Gorski (Postdoc Researcher; co-supervised with Thierry Bal, ICN)
• Domenico Guarino (Postdoc Researcher)
• Sabir Jacquir (Professor at Paris-Saclay University)
• Mathias Peuvrier (PhD student; co-supervised with P. Salin, Univ. Lyon)
• Amelie Soler (Programming Engineer)
• Eduarda Susin (PhD student)
• Federico Tesler (Postdoc Researcher)
• Nuria Tort-Colet (Postdoc Researcher)

Administrative and technical staff

• Yasaman Afshar (EITN secretary)
• Tom Messier (EITN systems manager)
• Julie Mongard (EITN communication manager)

Previous lab members

• Matteo diVolo (Postdoc Researcher)
• Luc Foubert (Postdoc Researcher)
• Melissa Dali (Postdoc Researcher)
• Tony Delobel (Systems Manager)
• Diego Contreras (Visitor, University of Pennsylvania)
• Tara Babaie (Visitor, University of Sydney)
• Miwa Fukino (Visitor, Panasonic)
• Michelle Rudolph-Lilith (CNRS Researcher)
• Kuba Orlowski (PhD student; co-supervised with A. Chaillet, SupElec)
• Maja Telenczuk (Postdoc Researcher)
• Katherine Fregnac (Project Manager)
• Audrey LeReun (Scientific Comm Assistant)
• Morgane Bourdonnais (Scientific Comm Assistant); now working at the ANR
• Tammy Bodin (EITN secretary)
• Remi Girard (Systems Manager)
• Bartosz Telenczuk (Postdoc Researcher); now freelance researcher
• Trang-Anh Nghiem (Master student; now PhD student)
• Cristiano Capone (Postdoc Researcher); now postdoc in Rome
• Anna Bulanova (Postdoc Researcher)
• Zahara Girones (Postdoc Researcher); now postdoc in Eugene (Oregon)
• Romain Cazé (Postdoc Researcher)
• Vicente Medel (Master student)
• Alberto Romagnoni (Postdoc Researcher)
• Davide Forcella (Postdoc Researcher)
• Yann Zerlaut (PhD student); now postdoc at the Italian Institute of Technology (Genova)
• Francesca Barbieri (Postdoc Researcher); now postdoc at Paris Descartes University
• Claude Bedard (CNRS Researcher); now retired
• Marco Brigham (PhD student); now postdoc at Polytechique
• Mathieu Galtier (Postdoc Researcher; now working in a private company)
• Lyle Muller (PhD student; now CNRS researcher in Marseille
• Sarah Goethals (Master student, now PhD student)
• Nima Dehghani (PhD student, now Postdoc at Harvard University, USA)
• Adrien Peyrache (Postdoc Researcher), now Assistant Professor at McGill University (Canada)
• Sami El Boustani (PhD student, now Assistant Professor at Geneva University (Switzerland)
• Pepe Alcami (Master student, now PhD student)
• Romain Brette (Postdoc Researcher, now Faculty member at INSERM, France)
• Mathieu Dubois (Master student, now PhD student)
• Jose Gomez (Postdoc Researcher), now Professor at the University of La Laguna (Spain)
• Alexandre Guerrini (Master student, now PhD student)
• Martin Pospischil (PhD student, now Research Engineer in Heidelberg, Germany)
• Seraphim Rodrigues (Postdoc Researcher, now Assistant Professor at Bilbao University (Spain)
• Quan Zou (PhD student, now Postdoc Researcher at University of Nevada Reno, USA)

[/su_spoiler] [su_spoiler class=”my-custom-spoiler” open=”no” title=”PUBLICATIONS” style=”font-size:7px;”] Publications du laboratoire. Informations à propos des travaux publiés ou sous presse, résumés des articles (abstracts) et copies complètes des articles avec figures.

See the publications database

The references of the articles and abstracts available in this database are listed in an approximate chronological order. This work was done successively at the University of Brussels (Belgium), the Salk Institute (USA), Laval University (Canada) and at CNRS (France). The topics range from biophysical models of synaptic transmission at the main receptor types (AMPA, NMDA, GABAA, GABAB and neuromodulators), single-cell models of the electrophysiological properties of central neurons (thalamus and neocortex), as well as network models (thalamic and cortical networks). [/su_spoiler] [/su_accordion]