B. Cessac

发表

Mean Field Methods in Neuroscience

B. Cessac, 2015 .

Absolute stability criterion for discrete time neural networks

B. Cessac, 1994 .

A View of Neural Networks as Dynamical Systems

Bruno Cessac, B. Cessac, 2009, Int. J. Bifurc. Chaos.

Increase in Complexity in Random Neural Networks

B. Cessac, 1995 .

Does the complex susceptibility of the Hénon map have a pole in the upper-half plane? A numerical investigation

B. Cessac, 2006, nlin/0609039.

Linear response, susceptibility and resonances in chaotic toy models

B. Cessac, J. Sepulchre, 2006, nlin/0612026.

Occurrence of Chaos and AT Line in Random Neural Networks.

B. Cessac, 1994 .

Mean-field equations, bifurcation map and route to chaos in discrete time neural networks

B. Cessac, M. Samuelides, M. Quoy, 1994 .

A discrete time neural network model with spiking neurons

B. Cessac, 2007, Journal of mathematical biology.

Stable resonances and signal propagation in a chaotic network of coupled units.

B. Cessac, J. Sepulchre, 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

What Can One Learn About Self-Organized Criticality from Dynamical Systems Theory?

B. Cessac, P. Blanchard, T. Krüger, 1999, cond-mat/9912081.

CONTROL OF THE TRANSITION TO CHAOS IN NEURAL NETWORKS WITH RANDOM CONNECTIVITY

B. Cessac, B. Doyon, M. Samuelides, 1993 .

Gibbs distribution analysis of temporal correlations structure in retina ganglion cells

Michael J. Berry, O. Marre, B. Cessac, 2011, Journal of Physiology - Paris.

Random recurrent neural networks dynamics

M. Samuelides, B. Cessac, B. Cessac, 2006, math-ph/0612022.

The Retina as a Dynamical System

B. Cessac, 2020, World Scientific Series on Nonlinear Science Series B.

On the potential role of lateral connectivity in retinal anticipation

B. Cessac, S. Souihel, 2020, Journal of mathematical neuroscience.

Estimating maximum entropy distributions from periodic orbits in spike trains

B. Cessac, R. Cofré, 2013 .

Parametric Estimation of Gibbs distributions as generalized maximum-entropy models for the analysis of spike train statistics.

T. Viéville, B. Cessac, J. Vasquez, 2011 .

Entropy-based parametric estimation of spike train statistics

T. Viéville, B. Cessac, J. Vasquez, 2010, 1003.3157.

LEARNING-INDUCED TOPOLOGICAL EFFECTS ON DYNAMICS IN NEURAL NETWORKS

B. Cessac, B. Siri, M. Q. Etis, 2006 .

Topological and dynamical structures induced by Hebbian learning in random neural networks

B. Cessac, M. Quoy, B. Delord, 2006 .

Effects of Cellular Homeostatic Intrinsic Plasticity on Dynamical and Computational Properties of Biological Recurrent Neural Networks

B. Cessac, Hugues Berry, B. Delord, 2013, The Journal of Neuroscience.

Noise driven broadening of the neural synchronisation transition in stage II retinal waves

B. Cessac, Dora Matzakos-Karvouniari, L. Gil, 2019, 1912.03934.

Pan-retinal characterisation of Light Responses from Ganglion Cells in the Developing Mouse Retina

Pierre Kornprobst, Bruno Cessac, Matthias H. Hennig, 2017, Scientific reports.

Linear response in neuronal networks: From neurons dynamics to collective response.

Bruno Cessac, B. Cessac, 2019, Chaos.

Perspectives on Multi-Level Dynamics

F. Atay, E. Olbrich, B. Cessac, 2016, 1606.05665.

Mean Field Theory for Random Recurrent Spiking Neural Networks

O. Mazet, B. Cessac, M. Samuelides, 2005 .

Mean-field equations, bifurcation map and chaos in discrete time, continuous state, random neural networks

B Cessac, M Samuelides, B. Cessac, 1995, Acta biotheoretica.

Self-Organized Criticality and Thermodynamic Formalism

B. Cessac, B. Cessac, P. Blanchard, 2002, nlin/0209038.

Quantum field theory renormalization group approach to self-organized critical models: The case of random boundaries

B. Cessac, P. Blanchard, D. Volchenkov, 2002 .

Lyapunov exponents and transport in the Zhang model of self-organized criticality.

B Cessac, B. Cessac, P. Blanchard, 2000, Physical review. E, Statistical, nonlinear, and soft matter physics.

Stochastic firing rate models

J. Touboul, O. Faugeras, B. Ermentrout, 2010, 1001.3872.

Properties of Biological Recurrent Neural Networks Effects of Cellular Homeostatic Intrinsic Plasticity on Dynamical and Computational

Albert W. Hamood, E. Marder, W. Senn, 2015 .

Are neuronal networks that vicious ? Or only their models ?

T. Viéville, B. Cessac, 2007 .

Floor the Ceil & Ceil the Floor: Revisiting AIMD Evaluation

B. Cessac, Ashwin Rao, A. Legout, 2012 .

Nav1.7 gating in human iPSC derived sensory neurons: an experimental and computational study

B. Cessac, E. Sernagor, A. Capurro, 2020, bioRxiv.

On the role of Nav1.7 sodium channels in chronic pain: an experimental and computational study

B. Cessac, E. Sernagor, A. Capurro, 2019, bioRxiv.

Computer and biological vision

Rachid Deriche, Pierre Kornprobst, Romain Veltz, 2007 .

Modeling non-standard retinal in/out function using computer vision variational methods

Frédéric Alexandre, Bruno Cessac, Thierry Viéville, 2013 .

Gibbs Distributions and STDP: An study case on recurrent Neural Networks

Bruno Cessac, Horacio Rostro, Thierry Viéville, 2009 .

A new nonconvex variational approach for sensory neurons receptive field estimation

G. Aubert, P. Kornprobst, B. Cessac, 2016 .

Local learning rules and bifurcations in the global dynamics of random recurrent neural networks ∗

B. Cessac, M. Quoy, Hugues Berry, 2007 .

Spatio-Temporal Linear Response of Spiking Neuronal Network Models

B. Cessac, R. Cofré, 2015 .

Random recurrent neural networks dynamics

B. Cessac, M. Samuelides, 2006, math-ph/0612022.

New Results - Single neuron models

Olivier Faugeras, Bruno Cessac, Jonathan Touboul, 2008 .

New Results - Assemblies of neuron models and simulation

Olivier Faugeras, Romain Veltz, Olivier Temam, 2007 .

Ghost Attractors in Spontaneous Brain Activity: Recurrent Excursions Into Functionally-Relevant BOLD Phase-Locking States

Morten L. Kringelbach, Gustavo Deco, Bruno Cessac, 2020, Frontiers in Systems Neuroscience.

Space-time correlations in spike trains and the neural code

Bruno Cessac, Rodrigo Cofre, B. Cessac, 2013 .

A novel approach to the functional classification of retinal ganglion cells

B. Cessac, Gerrit Hilgen, E. Sernagor, 2019, bioRxiv.

Some fractal aspects of Self-Organized Criticality

B. Cessac, 2004, nlin/0409004.

Title: Pan-retinal characterisation of Light Responses from Ganglion Cells in the Developing Mouse Retina Abbreviated Title: Pan-retinal Development of Light Responses

Matthias H Hennig, B. Cessac, Gerrit Hilgen, 2016 .

Random recurrent neural networks dynamics

B. Cessac, M. Samuelides, 2006, math-ph/0612022.

A discrete time neural network model with spiking neurons: II: Dynamics with noise

B Cessac, B. Cessac, 2010, Journal of mathematical biology.

On the ubiquity of Gibbs distributions in spike train statistics

B. Cessac, R. Cofré, Hassan Nasser, 2012 .

How Gibbs Distributions May Naturally Arise from Synaptic Adaptation Mechanisms. A Model-Based Argumentation

T. Viéville, B. Cessac, J. Vasquez, 2008, 0812.3899.

Stable resonances and signal propagation in a chaotic network of coupled units.

B. Cessac, J. Sepulchre, 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

Expectation-Driven Interaction: a Model Based on Luhmann's Contingency Approach

B. Cessac, P. Blanchard, L. Streit, 2006, J. Artif. Soc. Soc. Simul..

A O ] 22 O ct 2 00 8 STATISTICS OF SPIKES TRAINS , SYNAPTIC PLASTICITY AND

T. Viéville, B. Cessac, J. Vasquez, 2008 .

Pan-Retinal Characterisation of Light Responses from Ganglion Cells in the Developing Mouse Retina

Matthias H Hennig, B. Cessac, Gerrit Hilgen, 2016, bioRxiv.

Lyapunov exponents and transport in the Zhang model of self-organized criticality.

B. Cessac, P. Blanchard, T. Krüger, 2000, Physical review. E, Statistical, nonlinear, and soft matter physics.

The non linear dynamics of retinal waves

B. Cessac, Dora Matzakou-Karvouniari, 2022, Physica D: Nonlinear Phenomena.

Self-Organized Criticality and Thermodynamic Formalism

B. Cessac, P. Blanchard, T. Krüger, 2002, nlin/0209038.

What Can One Learn About Self-Organized Criticality from Dynamical Systems Theory?

B. Cessac, P. Blanchard, T. Krüger, 1999, cond-mat/9912081.

Biological and Computer Vision

Rachid Deriche, Christophe Lenglet, Carl-Fredrik Westin, 2005 .

Anticipation in the retina and the primary visual cortex : towards an integrated retino-cortical model for motion processing

Bruno Cessac, Alain Destexhe, Olivier Marre, 2019 .

Linear response, susceptibility and resonances in chaotic toy models

B. Cessac, J. Sepulchre, 2006, nlin/0612026.

Anomalous scaling and Lee-Yang zeros in self-organized criticality.

B. Cessac, J. Meunier, 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

A biophysical model explains the oscillatory behaviour of immature starburst amacrine cells

O. Marre, B. Cessac, S. Picaud, 2017 .

Mathematical and experimental studies on retinal waves

O. Marre, B. Cessac, S. Picaud, 2016 .

Analyzing large-scale spike trains data with spatio-temporal constraints

O. Marre, T. Viéville, B. Cessac, 2012 .

Retinal Processing: Insights from Mathematical Modelling

Bruno Cessac, B. Cessac, 2022, J. Imaging.

Visual Cortical Neurons Long-Term Potentiation of Intrinsic Excitability in LV

W. Senn, J. Pfister, B. Cessac, 2004 .

How Gibbs distributions may naturally arise from synaptic adaptation mechanisms

T. Viéville, B. Cessac, J. Vasquez, 2008, BMC Neuroscience.

Statistics of spikes trains, synaptic plasticity and Gibbs distributions.

B. Cessac, H. Rostro, J. C. Vasquez, 2008, 0810.3983.

A Mathematical Analysis of the Effects of Hebbian Learning Rules on the Dynamics and Structure of Discrete-Time Random Recurrent Neural Networks

Bruno Cessac, Mathias Quoy, Hugues Berry, 2007, Neural Computation.

Indisputable facts when implementing spiking neuron networks

Bruno Cessac, Thierry Vi'eville, H'elene Paugam-Moisy, 2009, 0903.3498.

Introducing numerical bounds to improve event-based neural network simulation pdf

Bruno Cessac, Thierry Viéville, Olivier Rochel, 2008, ArXiv.

Are biological neurons that vicious ? Or only their models ?

B. Cessac, T. Vieville, T. Viéville, 2007, 0709.4367.

Lyapunov exponents and transport in Self-Organized Criticality

Bruno Cessac, Ph. Blanchard, T. Krueger, 2000 .

Title: Pan-retinal Characterization of Light Responses from Ganglion Cells in the Developing 1 Mouse Retina 2

Matthias H Hennig, B. Cessac, Gerrit Hilgen, 2016 .

Parametric estimation of spike train statistics

Bruno Cessac, Thierry Viéville, T. Viéville, 2009, BMC Neuroscience.

Back-engineering of spiking neural networks parameters

Bruno Cessac, Thierry Viéville, Juan Carlos Vasquez, 2009, BMC Neuroscience.

Effects of Hebbian learning on the dynamics and structure of random networks with inhibitory and excitatory neurons

Bruno Cessac, Mathias Quoy, Hugues Berry, 2007, Journal of Physiology-Paris.

On Dynamics of Integrate-and-Fire Neural Networks with Adaptive Conductances

Bruno Cessac, Thierry Viéville, T. Viéville, 2007 .

From neuron to neural networks dynamics

Bruno Cessac, Manuel Samuelides, B. Cessac, 2006, ArXiv.

Self-organization and dynamics reduction in recurrent networks: stimulus presentation and learning

Bruno Cessac, Mathias Quoy, Emmanuel Daucé, 1998, Neural Networks.

Linear Response of General Observables in Spiking Neuronal Network Models

Bruno Cessac, Ignacio Ampuero, Rodrigo Cofré, 2017, Entropy.

Thermodynamic Formalism in Neuronal Dynamics and Spike Train Statistics

Rodrigo Cofre, Cesar Maldonado, Bruno Cessac, 2020, Entropy.

Linear response for spiking neuronal networks with unbounded memory.

Bruno Cessac, Rodrigo Cofre, Ignacio Ampuero, 2017, 1704.05344.

On the Mathematical Consequences of Binning Spike Trains

Bruno Cessac, Arnaud Le Ny, Eva Löcherbach, 2016, Neural Computation.

Parameter Estimation for Spatio-Temporal Maximum Entropy Distributions: Application to Neural Spike Trains

Bruno Cessac, Hassan Nasser, B. Cessac, 2014, Entropy.

Spike train statistics and Gibbs distributions

B. Cessac, R. Cofré, B. Cessac, 2013, Journal of Physiology-Paris.

Dynamics and spike trains statistics in conductance-based Integrate-and-Fire neural networks with chemical and electric synapses

Bruno Cessac, Rodrigo Cofre, B. Cessac, 2012, BMC Neuroscience.

Spike Train Statistics from Empirical Facts to Theory: The Case of the Retina

Bruno Cessac, Adrian G. Palacios, B. Cessac, 2013 .

Statistics of spike trains in conductance-based neural networks: Rigorous results

Bruno Cessac, B. Cessac, 2011, Journal of mathematical neuroscience.

A dynamical system approach to SOC models of Zhang's type

B. Cessac, B. Cessac, P. Blanchard, 1997 .

On Dynamics of Integrate-and-Fire Neural Networks with Conductance Based Synapses

Bruno Cessac, Thierry Viéville, T. Viéville, 2007, Frontiers Comput. Neurosci..

Anomalous scaling and Lee-Yang zeros in self-organized criticality.

B. Cessac, J. Meunier, 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

On bifurcations and chaos in random neural networks

B. Cessac, Manuel Samuelides, Bernard Doyon, 1994 .

Transmitting a signal by amplitude modulation in a chaotic network

Bruno Cessac, Jacques-A. Sepulchre, B. Cessac, 2005, Chaos.

Receptive field estimation in large visual neuron assemblies using a super-resolution approach.

Matthias H Hennig, B. Cessac, Gerrit Hilgen, 2022, Journal of neurophysiology.

Dimensionality Reduction on Spatio-Temporal Maximum Entropy Models of Spiking Networks

Bruno Cessac, Rodrigo Cofre, Maria-Jose Escobar, 2018, bioRxiv.

Overview of facts and issues about neural coding by spikes

Bruno Cessac, Hélène Paugam-Moisy, Thierry Viéville, 2010, Journal of Physiology-Paris.

How Gibbs Distributions May Naturally Arise from Synaptic Adaptation Mechanisms. A Model-Based Argumentation

Bruno Cessac, Horacio Rostro-Gonzalez, Juan C Vasquez, 2008, 0812.3899.

A Constructive Mean-Field Analysis of Multi-Population Neural Networks with Random Synaptic Weights and Stochastic Inputs

Olivier D. Faugeras, Bruno Cessac, Jonathan Touboul, 2008, Front. Comput. Neurosci..

A biophysical model explains the spontaneous bursting behavior in the developing retina

Bruno Cessac, Olivier Marre, Serge Picaud, 2017, Scientific Reports.

On deterministic reservoir computing: network complexity and algorithm

Bruno Cessac, Horacio Rostro, Thierry Viéville, 2009 .

Parameter estimation in spiking neural networks: a reverse-engineering approach

H Rostro-Gonzalez, B Cessac, T Vieville, 2012, Journal of neural engineering.

Parametric estimation of spike train statistics by Gibbs distributions : an application to bio-inspired and experimental data

Bruno Cessac, Juan Carlos Vasquez, Hassan Nasser, 2010 .

Spike trains statistics in Integrate and Fire Models: exact results

Bruno Cessac, Juan Carlos Vasquez, Hassan Nasser, 2010, 1008.5074.

Numerical evidence of linear response violations in chaotic systems

B. Cessac, B. Cessac, 2006 .

Frontiers in Computational Neuroscience

J. Touboul, O. Faugeras, B. Cessac, 2022 .

A computational study of anticipation in the retina

B. Cessac, S. Souihel, 2018 .

Processing various motion features and measuring RGCs pairwise correlations with a 2D retinal model

Bruno Cessac, Olivier Marre, Frédéric Chavane, 2018 .

Inducing periodicity and chaos by negative feedback

B. Cessac, E. Pécou, G. Espinoza, 2007, 0712.0940.

ar X iv : 0 71 2 . 09 40 v 1 [ nl in . C D ] 6 D ec 2 00 7 Inducing periodicity and chaos by negative feedback

B. Cessac, E. Pécou, 2009 .

A constructive mean-field analysis of multi population neural networks with random synaptic weights and stochastic inputs

J. Touboul, O. Faugeras, B. Cessac, 2008, Frontiers Comput. Neurosci..

Reverse-engineering in spiking neural networks parameters: exact deterministic parameters estimation

Bruno Cessac, Thierry Viéville, Horacio Rostro-Gonzalez, 2010 .

Computing with events in the deterministic case

Bruno Cessac, Laurent Bougrain, Horacio Rostro, 2012 .

Spatio-temporal spike train analysis for large scale networks using the maximum entropy principle and Monte Carlo method

Bruno Cessac, Hassan Nasser, Olivier Marre, 2012, 1209.3886.

PRANAS: A New Platform for Retinal Analysis and Simulation

Pierre Kornprobst, Bruno Cessac, Thierry Viéville, 2017, Front. Neuroinform..

Large visual neuron assemblies receptive fields estimation using a super-resolution approach

Matthias H Hennig, B. Cessac, Gerrit Hilgen, 2023 .

Temporal pattern recognition in retinal ganglion cells is mediated by dynamical inhibitory synapses

O. Marre, B. Cessac, Thomas Buffet, 2023, bioRxiv.

Parametric estimation of spike train statistics by Gibbs distributions : an application to bio-inspired and experimental data

T. Viéville, B. Cessac, J. Vasquez, 2010 .

New Results - Alternative computing models/Spatial computing

Marc Schoenauer, Olivier Temam, Bruno Cessac, 2008 .