N. Brunel

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What can we learn from synaptic weight distributions?

J. Nadal, B. Barbour, N. Brunel, 2007, Trends in Neurosciences.

Calcium-based plasticity model explains sensitivity of synaptic changes to spike pattern, rate, and dendritic location

N. Brunel, M. Graupner, Michael Graupner, 2012, Proceedings of the National Academy of Sciences.

Plasticity of directional place fields in a model of rodent CA3

N. Brunel, O. Trullier, Olivier Trullier, 1998, Hippocampus.

Retrospective and prospective persistent activity induced by Hebbian learning in a recurrent cortical network

D. Amit, N. Brunel, G. Mongillo, 2003, The European journal of neuroscience.

Role of delays in shaping spatiotemporal dynamics of neuronal activity in large networks.

D. Hansel, N. Brunel, Alex Roxin, 2005, Physical review letters.

Very long transients, irregular firing, and chaotic dynamics in networks of randomly connected inhibitory integrate-and-fire neurons.

D. Hansel, N. Brunel, R. Zillmer, 2009, Physical review. E, Statistical, nonlinear, and soft matter physics.

On the Distribution of Firing Rates in Networks of Cortical Neurons

D. Hansel, C. van Vreeswijk, N. Brunel, 2011, The Journal of Neuroscience.

Quantitative investigations of electrical nerve excitation treated as polarization

M. V. Rossum, N. Brunel, 2007, Biological Cybernetics.

Storage capacity of neural networks: effect of the fluctuations of the number of active neurons per memory

N. Brunel, 1994 .

Synaptic mechanisms and network dynamics underlying spatial working memory in a cortical network model.

P. Goldman-Rakic, Xiao-Jing Wang, X. Wang, 2000, Cerebral cortex.

Model of global spontaneous activity and local structured activity during delay periods in the cerebral cortex.

D. Amit, N. Brunel, 1997, Cerebral cortex.

Firing frequency of leaky intergrate-and-fire neurons with synaptic current dynamics.

N. Brunel, S. Sergi, Nicolas Brunel, 1998, Journal of theoretical biology.

Effects of synaptic noise and filtering on the frequency response of spiking neurons.

Frances S. Chance, L. Abbott, F. Chance, 2001, Physical review letters.

Mutual Information, Fisher Information, and Population Coding

Nicolas Brunel, Jean-Pierre Nadal, J. Nadal, 1998, Neural Computation.

Optimal Information Storage and the Distribution of Synaptic Weights Perceptron versus Purkinje Cell

J. Nadal, B. Barbour, N. Brunel, 2004, Neuron.

How Spike Generation Mechanisms Determine the Neuronal Response to Fluctuating Inputs

D. Hansel, C. van Vreeswijk, N. Brunel, 2003, The Journal of Neuroscience.

Parameter estimation of ODE’s via nonparametric estimators

N. Brunel, 2007, 0710.4190.

Astrocytes: Orchestrating synaptic plasticity?

N. Brunel, A. Volterra, M. Pittà, 2015, Neuroscience.

Firing Rate of the Noisy Quadratic Integrate-and-Fire Neuron

Nicolas Brunel, Peter E. Latham, P. Latham, 2003, Neural Computation.

Can Attractor Network Models Account for the Statistics of Firing During Persistent Activity in Prefrontal Cortex?

Nicolas Brunel, Francesca Barbieri, N. Brunel, 2008, Front. Neurosci..

Persistent activity and the single-cell frequency–current curve in a cortical network model

N. Brunel, 2000, Network.

Irregular persistent activity induced by synaptic excitatory feedback

N. Brunel, F. Barbieri, 2007, BMC Neuroscience.

Mean-Field Theory of Irregularly Spiking Neuronal Populations and Working Memory in Recurrent Cortical Networks

Xiao-Jing Wang, Nicolas Brunel, Alfonso Renart, 2003 .

From subthreshold to firing-rate resonance.

N. Brunel, V. Hakim, M. J. Richardson, 2003, Journal of neurophysiology.

What determines the frequency of fast network oscillations with irregular neural discharges? I. Synaptic dynamics and excitation-inhibition balance.

Xiao-Jing Wang, N. Brunel, 2003, Journal of neurophysiology.

Estimating network parameters from combined dynamics of firing rate and irregularity of single neurons.

A. Riehle, N. Brunel, K. Hamaguchi, 2011, Journal of neurophysiology.

Temporal decorrelation of collective oscillations in neural networks with local inhibition and long-range excitation.

D. Hansel, N. Brunel, Demian Battaglia, 2007, Physical review letters.

Rate Models with Delays and the Dynamics of Large Networks of Spiking Neurons(Oscillation, Chaos and Network Dynamics in Nonlinear Science)

Nicolas Brunel, David Hansel, Alex Roxin, 2006 .

Tracking for parameter and state estimation in possibly misspecified partially observed linear Ordinary Differential Equations

N. Brunel, Q. Clairon, 2019, Journal of Statistical Planning and Inference.

State and Parameter Estimation of Partially Observed Linear Ordinary Differential Equations with Deterministic Optimal Control

N. Brunel, Q. Clairon, 2014, 1410.7558.

Modulation of Synaptic Plasticity by Glutamatergic Gliotransmission: A Modeling Study

N. Brunel, Maurizio De Pittà, 2016, Neural plasticity.

Mechanisms and functional roles of glutamatergic synapse diversity in a cerebellar circuit

Jian K. Liu, N. Brunel, S. Dieudonné, 2016, eLife.

A Tracking Approach to Parameter Estimation in Linear Ordinary Differential Equations

N. Brunel, Q. Clairon, 2014, 1410.7554.

Modeling temporal dependence of spherically invariant random vectors with triplet markov chains

N. Brunel, W. Pieczynski, 2005, IEEE/SP 13th Workshop on Statistical Signal Processing, 2005.

Information capacity of a perceptron

J. Nadal, G. Toulouse, N. Brunel, 1992 .

Inferring learning rules from distribution of firing rates in cortical neurons

David J. Freedman, David L. Sheinberg, D. Sheinberg, 2015, Nature Neuroscience.

Toward a Neurocentric View of Learning

N. Brunel, C. Hansel, H. Titley, 2017, Neuron.

Neuron Perspective Toward a Neurocentric View of Learning

N. Brunel, C. Hansel, H. Titley, 2017 .

Inhibition stabilization is a widespread property of cortical networks

N. Brunel, M. Histed, A. Sanzeni, 2020, eLife.

Neuroscience and computation

J. Nadal, Y. Frégnac, C. Meunier, 2003, Journal of Physiology-Paris.

Bistability and up/down state alternations in inhibition-dominated randomly connected networks of LIF neurons

Elisa M. Tartaglia, N. Brunel, 2017, Scientific Reports.

Contributions of intrinsic membrane dynamics to fast network oscillations with irregular neuronal discharges.

Nicolas Brunel, Xiao-Jing Wang, Caroline Geisler, 2005, Journal of neurophysiology.

How Connectivity, Background Activity, and Synaptic Properties Shape the Cross-Correlation between Spike Trains

Nicolas Brunel, Srdjan Ostojic, N. Brunel, 2009, The Journal of Neuroscience.

Mutual Information, Fisher Information and Population Coding Submitted to Neural Computation

Nicolas Brunel, Jean-Pierre Nadal, J. Nadal, 1998 .

Electrical Coupling Mediates Tunable Low-Frequency Oscillations and Resonance in the Cerebellar Golgi Cell Network

Nicolas Brunel, Stéphane Dieudonné, Eric Schwartz, 2009, Neuron.

Acetylcholine Modulates Cerebellar Granule Cell Spiking by Regulating the Balance of Synaptic Excitation and Inhibition

Benjamin N. Taylor, N. Brunel, Court Hull, 2020, The Journal of Neuroscience.

Acetylcholine Modulates Cerebellar Granule Cell Spiking by Regulating the Balance of Synaptic Excitation and Inhibition

Benjamin N. Taylor, N. Brunel, Court Hull, 2019, The Journal of Neuroscience.

26th Annual Computational Neuroscience Meeting (CNS*2017): Part 2

Minoru Asada, Anders M. Dale, Anna Devor, 2017, BMC Neuroscience.

Motor Learning? Saccade the Cerebellum: A Vectorial Error Signal for Complex Spike Activity in the Oculomotor Vermis of

A. Fuchs, C. Clopath, C. Evinger, 2015 .

A Cerebellar Learning Model of Vestibulo-Ocular Reflex Adaptation in Wild-Type and Mutant Mice

Nicolas Brunel, Claudia Clopath, Chris I. De Zeeuw, 2014, The Journal of Neuroscience.

High-Frequency Organization and Synchrony of Activity in the Purkinje Cell Layer of the Cerebellum

Nicolas Brunel, Boris Barbour, Philippe Isope, 2008, Neuron.

Neuronal Morphology Generates High-Frequency Firing Resonance

Nicolas Brunel, Boris Barbour, Srdjan Ostojic, 2015, The Journal of Neuroscience.

On the relationship between persistent delay activity, repetition enhancement and priming

Elisa M. Tartaglia, N. Brunel, G. Mongillo, 2015, Front. Psychol..

Cellular / Molecular Neuronal Morphology Generates High-Frequency Firing Resonance

B. Barbour, N. Brunel, V. Hakim, 2015 .

Response nonlinearities in networks of spiking neurons

N. Brunel, M. Histed, A. Sanzeni, 2020, PLoS computational biology.

Emergence of Irregular Activity in Networks of Strongly Coupled Conductance-Based Neurons

N. Brunel, M. Histed, A. Sanzeni, 2020, bioRxiv.

Cortical dynamics during naturalistic se and models

N. Logothetis, S. Panzeri, A. Mazzoni, 2011 .

Modeling Synaptic Plasticity in Hippocampus: A Calcium-Based Approach

N. Brunel, M. Graupner, 2018 .

Pyramidal Cells during Neocortical "UP" States Recurrent Synaptic Input and the Timing of Gamma-Frequency-Modulated Firing of

H. Robinson, K. Aihara, N. Brunel, 2008 .

Discharges Network Oscillations With Irregular Neuronal Contributions of Intrinsic Membrane Dynamics to

D. Hansel, Xiao-Jing Wang, N. Brunel, 2005 .

Wang Discharges Network Oscillations With Irregular Neuronal Contributions of Intrinsic Membrane Dynamics to Fast

N. Brunel, C. Geisler, Xiao-jing, 2005 .

Firing rate of the leaky integrate-and-fire neuron with stochastic conductance-based synaptic inputs with short decay times

Timothy D. Oleskiw, Eric T. Shea-Brown, W. Bair, 2020, 2002.11181.

Efficient supervised learning in networks with binary synapses

R. Zecchina, Carlo Baldassi, N. Brunel, 2007, Proceedings of the National Academy of Sciences.

Optimal information storage and the distribution of synaptic weights : Perceptron vs . Purkinje cell Supplemental Material : Computation of perceptron capacity and synaptic weight distribution

J. Nadal, B. Barbour, N. Brunel, 2004 .

Mean curvature and mean shape for multivariate functional data under Frenet-Serret framework.

Juhyun Park, N. Brunel, 2019, 1910.12049.

Patterns of Primate Cortical Neurons Regional and Laminar Differences in In Vivo Firing

H. Tamura, I. Fujita, M. Vinck, 2015 .

Polarimetric Statistical Segmentation for Radar Clutter map based on Pairwise Markov Chains

F. Barbaresco, N. Brunel, N. Brunel, 2005 .

Parametric Estimation of Ordinary Differential Equations

N. Brunel, Q. Clairon, Florence d'Alché-Buc, 2012 .

Constrained Nonlinear and Mixed Effects Integral Differential Equation Models for Dynamic Cell Polarity Signaling

N. Brunel, Zhenbiao Yang, Xinping Cui, 2022, Frontiers in Plant Science.

Unsupervised Learning of Persistent and Sequential Activity

N. Brunel, Ulises Pereira, 2020, Frontiers Comput. Neurosci..

Fokker-Planck Equation

N. Brunel, V. Hakim, 2019, Encyclopedia of Computational Neuroscience.

Analysis of variability in sign language hand trajectories: development of generative model

N. Brunel, P. Chassat, Juhyun Park, 2022, MOCO.

Constrained Nonlinear and Mixed Effects Differential Equation Models for Dynamic Cell Polarity Signaling

N. Brunel, Zhenbiao Yang, Xinping Cui, 2016, 1605.00185.

Bayesian reconstruction of memories stored in neural networks from their connectivity

F. Krzakala, Lenka Zdeborov'a, N. Brunel, 2021, PLoS Comput. Biol..

Consistent Sufficient Explanations and Minimal Local Rules for explaining regression and classification models

Salim I. Amoukou, N. Brunel, 2021, 2111.04658.

Mechanisms of Induction and Maintenance of Spike-Timing Dependent Plasticity in Biophysical Synapse Models

Nicolas Brunel, Michael Graupner, N. Brunel, 2010, Front. Comput. Neurosci..

Optimal Control and Additive Perturbations Help in Estimating Ill-Posed and Uncertain Dynamical Systems

N. Brunel, Q. Clairon, 2018, Journal of the American Statistical Association.

Stochastic Evolution of Distributions - Applications to CDS indices

N. Brunel, G. Bernis, S. Scotti, 2017 .

Synaptic plasticity rules with physiological calcium levels

Nicolas Brunel, Johnatan Aljadeff, Dominique Debanne, 2020, Proceedings of the National Academy of Sciences.

Altered spike timing-dependent plasticity rules in physiological calcium

Nicolas Brunel, Johnatan Aljadeff, Dominique Debanne, 2020, bioRxiv.

Unsupervised Learning of Persistent and Sequential Activity

Ulises Pereira, Nicolas Brunel, N. Brunel, 2018, bioRxiv.

Attractor Dynamics in Networks with Learning Rules Inferred from In Vivo Data

Ulises Pereira, Nicolas Brunel, N. Brunel, 2017, Neuron.

Is cortical connectivity optimized for storing information?

Nicolas Brunel, N. Brunel, 2016, Nature Neuroscience.

Burst-Dependent Bidirectional Plasticity in the Cerebellum Is Driven by Presynaptic NMDA Receptors.

Nicolas Brunel, Boris Barbour, Benjamin Mathieu, 2016, Cell reports.

Storing structured sparse memories in a multi-modular cortical network model

Nicolas Brunel, Alexis M. Dubreuil, N. Brunel, 2016, Journal of Computational Neuroscience.

Stimulus Dependence of Local Field Potential Spectra: Experiment versus Theory

Nicolas Brunel, Nikos K Logothetis, Stefano Panzeri, 2014, The Journal of Neuroscience.

Memory Maintenance in Synapses with Calcium-Based Plasticity in the Presence of Background Activity

Nicolas Brunel, David Higgins, Michael Graupner, 2014, PLoS Comput. Biol..

Analytical approximations of the firing rate of an adaptive exponential integrate-and-fire neuron in the presence of synaptic noise

Nicolas Brunel, Daniel Durstewitz, Loreen Hertäg, 2014, Front. Comput. Neurosci..

Author's Personal Copy Understanding the Relationships between Spike Rate and Delta/gamma Frequency Bands of Lfps and Eegs Using a Local Cortical Network Model

Nicolas Brunel, Stefano Panzeri, Alberto Mazzoni, 2022 .

Neuronal Dynamics

Nicolas Brunel, Vincent Hakim, N. Brunel, 2009, Encyclopedia of Complexity and Systems Science.

Encoding of Naturalistic Stimuli by Local Field Potential Spectra in Networks of Excitatory and Inhibitory Neurons

Nicolas Brunel, Stefano Panzeri, Alberto Mazzoni, 2008, PLoS Comput. Biol..

STDP in a Bistable Synapse Model Based on CaMKII and Associated Signaling Pathways

Nicolas Brunel, Michael Graupner, N. Brunel, 2007, PLoS Comput. Biol..

Erratum to: Effects of neuromodulation in a cortical network model of object working memory dominated by recurrent inhibition

Xiao-Jing Wang, Nicolas Brunel, Xiao-Jing Wang, 2014, Journal of Computational Neuroscience.

Effects of Neuromodulation in a Cortical Network Model of Object Working Memory Dominated by Recurrent Inhibition

Xiao-Jing Wang, N. Brunel, 2004, Journal of Computational Neuroscience.

Correlations between synapses in pairs of neurons slow down dynamics in randomly connected neural networks.

Nicolas Brunel, Srdjan Ostojic, Daniel Martí, 2017, Physical review. E.

Non-monotonic effects of GABAergic synaptic inputs on neuronal firing

N. Brunel, N. Calakos, A. A. Zadeh, 2021, bioRxiv.

Sensory neural codes using multiplexed temporal scales

Nicolas Brunel, Stefano Panzeri, Christoph Kayser, 2010, Trends in Neurosciences.

Mean-field theory of recurrent cortical networks: Working memory circuits with irregularly spiking neurons

Nicolas Brunel, Xiao-Jing Wang, Alfonso Renart, 2003 .

Title : Astrocytes : Orchestrating synaptic plasticity ?

N. Brunel, A. Volterra, M. Pittà, 2022 .

25th Annual Computational Neuroscience Meeting: CNS-2016

Morten L. Kringelbach, Majid Nili Ahmadabadi, Anders M. Dale, 2016, BMC Neuroscience.

Modulation of Network Excitability by Persistent Activity: How Working Memory Affects the Response to Incoming Stimuli

Nicolas Brunel, Gianluigi Mongillo, Elisa M. Tartaglia, 2015, PLoS Comput. Biol..

Repetition suppression/enhancement effects as a result of attractor dynamics in local cortical networks

Elisa M. Tartaglia, N. Brunel, G. Mongillo, 2013, BMC Neuroscience.

Dynamics of networks of randomly connected excitatory and inhibitory spiking neurons

Nicolas Brunel, N. Brunel, 2000, Journal of Physiology-Paris.

Coupled ripple oscillations between the medial temporal lobe and neocortex retrieve human memory

Nicolas Brunel, Alex P Vaz, Sara K Inati, 2019, Science.

Time to detect the difference between two images presented side by side.

N Brunel, J Ninio, N. Brunel, 1997, Brain research. Cognitive brain research.

Lapicque’s 1907 paper: from frogs to integrate-and-fire

Nicolas Brunel, Mark C. W. van Rossum, M. V. Rossum, 2007, Biological Cybernetics.

Dynamics and plasticity of stimulus-selective persistent activity in cortical network models.

Nicolas Brunel, N. Brunel, 2003, Cerebral cortex.

Dynamics of Sparsely Connected Networks of Excitatory and Inhibitory Spiking Neurons

Nicolas Brunel, N. Brunel, 2000, Journal of Computational Neuroscience.

Single neuron dynamics and computation

Nicolas Brunel, Vincent Hakim, Magnus JE Richardson, 2014, Current Opinion in Neurobiology.

Course 10 - Network Models of Memory

Nicolas Brunel, N. Brunel, 2005 .

Semantic Priming in a Cortical Network Model

Nicolas Brunel, Frédéric Lavigne, N. Brunel, 2009, Journal of Cognitive Neuroscience.

Forgetting Leads to Chaos in Attractor Networks

N. Brunel, Johnatan Aljadeff, Ulises Pereira-Obilinovic, 2021, Physical Review X.

Memory capacity of networks with stochastic binary synapses

Yali Amit, Nicolas Brunel, Alexis M. Dubreuil, 2013, BMC Neuroscience.

Memory Capacity of Networks with Stochastic Binary Synapses

Yali Amit, Nicolas Brunel, Alexis M. Dubreuil, 2013, BMC Neuroscience.

A Three-Threshold Learning Rule Approaches the Maximal Capacity of Recurrent Neural Networks

Nicolas Brunel, Carlo Baldassi, Riccardo Zecchina, 2015, PLoS Comput. Biol..

Slow stochastic Hebbian learning of classes of stimuli in a recurrent neural network.

N Brunel, F Carusi, S Fusi, 1998, Network.

Dynamics of a recurrent network of spiking neurons before and following learning

Nicolas Brunel, Daniel J. Amit, D. Amit, 1997 .

Response nonlinearities in networks of spiking neurons

Nicolas Brunel, Mark H. Histed, Alessandro Sanzeni, 2019, bioRxiv.

Dynamics of Neural Networks

Nicolas Brunel, N. Brunel, 2013 .

Dynamics of Networks of Excitatory and Inhibitory Neurons in Response to Time-Dependent Inputs

Nicolas Brunel, Erwan Ledoux, N. Brunel, 2011, Front. Comput. Neurosci..

From Spiking Neuron Models to Linear-Nonlinear Models

Nicolas Brunel, Srdjan Ostojic, N. Brunel, 2011, PLoS Comput. Biol..

Sparsely synchronized neuronal oscillations.

Nicolas Brunel, Vincent Hakim, N. Brunel, 2008, Chaos.

Optimal tuning curves for neurons spiking as a Poisson process

Nicolas Brunel, Jean-Pierre Nadal, J. Nadal, 1997, ESANN.

Storage of Correlated Patterns in Standard and Bistable Purkinje Cell Models

Nicolas Brunel, Claudia Clopath, Jean-Pierre Nadal, 2012, PLoS Comput. Biol..

Cerebellar learning using perturbations

Nicolas Brunel, Claudia Clopath, Boris Barbour, 2016, bioRxiv.

NETWORK MODELS OF MEMORY

Nicolas Brunel, N. Brunel, 2004 .

Dynamic control of sequential retrieval speed in networks with heterogeneous learning rules

N. Brunel, Maxwell Gillett, 2023, bioRxiv.

Humans are able to self-paced constant running accelerations until exhaustion

N. Brunel, V. Billat, S. Labbé, 2018, Physica A: Statistical Mechanics and its Applications.

Long-term constant acceleration can be sustained freely in running via stochastic short-term corrections

Nicolas Brunel, Véronique Billat, Jean-Pierre Koralzstein, 2018 .

From synapse to network: models of information storage and retrieval in neural circuits

Nicolas Brunel, Johnatan Aljadeff, Maxwell Gillett, 2021, Current Opinion in Neurobiology.

How Noise Affects the Synchronization Properties of Recurrent Networks of Inhibitory Neurons

Nicolas Brunel, David Hansel, D. Hansel, 2006, Neural Computation.

Bistability and up/down state alternations in inhibition-dominated randomly connected networks of LIF neurons

Elisa M. Tartaglia, N. Brunel, 2017, Scientific Reports.

Dynamics of the Firing Probability of Noisy Integrate-and-Fire Neurons

Nicolas Brunel, Nicolas Fourcaud-Trocmé, N. Brunel, 2002, Neural Computation.

Dynamics of the Instantaneous Firing Rate in Response to Changes in Input Statistics

Nicolas Brunel, Nicolas Fourcaud-Trocmé, N. Brunel, 2005, Journal of Computational Neuroscience.

Fast Global Oscillations in Networks of Integrate-and-Fire Neurons with Low Firing Rates

Nicolas Brunel, Vincent Hakim, N. Brunel, 1999, Neural Computation.

Accurate Shapley Values for explaining tree-based models

Salim I. Amoukou, N. Brunel, Tangi Salaün, 2021, AISTATS.

Curvature and Torsion estimation of 3D functional data: A geometric approach to build the mean shape under the Frenet Serret framework

Juhyun Park, N. Brunel, P. Chassat, 2022, 2203.02398.

Population Density Models

Nicolas Brunel, Vincent Hakim, N. Brunel, 2014, Encyclopedia of Computational Neuroscience.

Firing-rate resonance in a generalized integrate-and-fire neuron with subthreshold resonance.

Nicolas Brunel, N. Brunel, V. Hakim, 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

Unsupervised signal restoration using hidden Markov chains with copulas

Nicolas Brunel, Wojciech Pieczynski, N. Brunel, 2005, Signal Process..

The Statistics of Repeating Patterns of Cortical Activity Can Be Reproduced by a Model Network of Stochastic Binary Neurons

Nicolas Brunel, Alex Roxin, N. Brunel, 2008, The Journal of Neuroscience.

Optimal Properties of Analog Perceptrons with Excitatory Weights

Nicolas Brunel, Claudia Clopath, C. Clopath, 2013, PLoS Comput. Biol..

Global Spontaneous Activity and Local Structured (learned) Delay Period Activity in Cortex

Nicolas Brunel, Daniel J. Amit, D. Amit, 1996 .

Global Spontaneous Activity and Local Structured (learned) Delay Activity in Cortex

Nicolas Brunel, Jerome Daniel, N. Brunel, 1995 .

Cortical dynamics during naturalistic sensory stimulations: Experiments and models

Nicolas Brunel, Stefano Panzeri, Alberto Mazzoni, 2011, Journal of Physiology-Paris.

Characteristics of sequential activity in networks with temporally asymmetric Hebbian learning

Ulises Pereira, Nicolas Brunel, Maxwell Gillett, 2019, Proceedings of the National Academy of Sciences.

Correlations of cortical Hebbian reverberations: theory versus experiment

N Brunel, D J Amit, M V Tsodyks, 1994, The Journal of neuroscience : the official journal of the Society for Neuroscience.

26th Annual Computational Neuroscience Meeting (CNS*2017): Part 2

Romain D. Cazé, Alfred B. Yu, Derek K. Jones, 2017, BMC Neuroscience.

Phase diagrams of sparsely connected networks of excitatory and inhibitory spiking neurons

Nicolas Brunel, N. Brunel, 2000, Neurocomputing.

Parametric estimation methods of multivariate data for multicomponent image segmentation

W. Pieczynski, Nicolas Brunel, Stéphane Derrode, 2005 .

Learning Interactions in Reaction Diffusion Equations by Neural Networks

N. Brunel, Xinping Cui, Xin Yang, 2023, Entropy.

Multiple forms of working memory emerge from synapse–astrocyte interactions in a neuron–glia network model

N. Brunel, Maurizio De Pittà, 2021, bioRxiv.

Hebbian Learning of Context in Recurrent Neural Networks

Nicolas Brunel, N. Brunel, 1996, Neural Computation.

Parametric Estimation of Ordinary Differential Equations With Orthogonality Conditions

Quentin Clairon, Florence d'Alche-Buc, N. Brunel, 2014, Journal of the American Statistical Association.

Interplay between external inputs and recurrent dynamics during movement preparation and execution in a network model of motor cortex

N. Hatsopoulos, N. Brunel, L. Bachschmid-Romano, 2022, bioRxiv.

Synchronization properties of networks of electrically coupled neurons in the presence of noise and heterogeneities

Nicolas Brunel, Vincent Hakim, Srdjan Ostojic, 2009, Journal of Computational Neuroscience.

Accurate Shapley Values for explaining tree-based models

Salim I. Amoukou, N. Brunel, Tangi Salaün, 2021, AISTATS.

Accurate and robust Shapley Values for explaining predictions and focusing on local important variables pdf

Salim I. Amoukou, Nicolas J.-B. Brunel, Tangi Salaün, 2021, ArXiv.

Dynamics of an attractor neural network converting temporal into spatial correlations Network: Compu

Nicolas Brunel, N. Brunel, 1994 .

Estimating parameters and hidden variables in non-linear state-space models based on ODEs for biological networks inference

N. Brunel, Florence d'Alché-Buc, Minh Quach, 2007, Bioinform..

Storing structured sparse memories in a multi-modular cortical network model

N. Brunel, A. Dubreuil, 2016, Journal of Computational Neuroscience.

Mechanisms underlying reshuffling of visual responses by optogenetic stimulation in mice and monkeys

J. Nassi, J. Reynolds, N. Brunel, 2022, Neuron.

Erratum to: Effects of neuromodulation in a cortical network model of object working memory dominated by recurrent inhibition

Xiao-Jing Wang, N. Brunel, 2014, Journal of Computational Neuroscience.

How Connectivity, Background Activity, and Synaptic Properties Shape the Cross-Correlation between Spike Trains

N. Brunel, V. Hakim, S. Ostojic, 2009, The Journal of Neuroscience.

Effects of Neuromodulation in a Cortical Network Model of Object Working Memory Dominated by Recurrent Inhibition

Xiao-Jing Wang, N. Brunel, 2004, Journal of Computational Neuroscience.

Learning internal representations in an attractor neural network with analogue neurons

Nicolas Brunel, Daniel J. Amit, D. Amit, 1995 .

A Continuous Attractor Network Model Without Recurrent Excitation: Maintenance and Integration in the Head Direction Cell System

Nicolas Brunel, Angelo Arleo, Christian Boucheny, 2005, Journal of Computational Neuroscience.

Single neuron dynamics and computation

N. Brunel, V. Hakim, M. J. Richardson, 2014, Current Opinion in Neurobiology.

Estimating network parameters from combined dynamics of firing rate and irregularity of single neurons.

A. Riehle, N. Brunel, K. Hamaguchi, 2011, Journal of neurophysiology.

Inhibition stabilization is a widespread property of cortical networks

N. Brunel, M. Histed, A. Sanzeni, 2019, bioRxiv.

Copulas in vectorial hidden Markov chains for multicomponent image segmentation

Nicolas Brunel, Stéphane Derrode, Wojciech Pieczynski, 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..

Coupled ripple oscillations between the medial temporal lobe and neocortex retrieve human memory

N. Brunel, K. Zaghloul, S. Inati, 2019, Science.

Cross-Correlations in Sparsely Connected Recurrent Networks of Spiking Neurons

Nicolas Brunel, N. Brunel, 1997, ICANN.

Neuroscience and computation

J. Nadal, Y. Frégnac, C. Meunier, 2003, Journal of Physiology-Paris.

Nonlinear feedforward networks with stochastic outputs: infomax implies redundancy reduction.

J. Nadal, N. Parga, N. Brunel, 1998, Network.

Lapicque’s 1907 paper: from frogs to integrate-and-fire

M. V. Rossum, N. Brunel, 2007, Biological Cybernetics.

26th Annual Computational Neuroscience Meeting (CNS*2017): Part 2

Romain D. Cazé, Alfred B. Yu, Derek K. Jones, 2017, BMC Neuroscience.

Stochastic evolution of distributions and functional Bollinger bands

N. Brunel, G. Bernis, Antoine Kornprobst, 2022, Applied Stochastic Models in Business and Industry.

Author's Personal Copy Understanding the Relationships between Spike Rate and Delta/gamma Frequency Bands of Lfps and Eegs Using a Local Cortical Network Model

N. Logothetis, S. Panzeri, A. Mazzoni, 2022 .