Noise-induced behaviors in neural mean field dynamics

Jonathan Touboul 1 Geoffroy Hermann 2 Olivier Faugeras 2
CRISAM - Inria Sophia Antipolis - Méditerranée , INRIA Rocquencourt, ENS Paris - École normale supérieure - Paris, UNS - Université Nice Sophia Antipolis, CNRS - Centre National de la Recherche Scientifique : UMR8548
2 NEUROMATHCOMP - Mathematical and Computational Neuroscience
CRISAM - Inria Sophia Antipolis - Méditerranée , JAD - Laboratoire Jean Alexandre Dieudonné : UMR6621
Abstract : The collective behavior of cortical neurons is strongly affected by the presence of noise at the level of individual cells. In order to study these phenomena in large-scale assemblies of neurons, we consider networks of firing-rate neurons with linear intrinsic dynamics and nonlinear coupling, belonging to a few types of cell populations and receiving noisy currents. Asymptotic equations as the number of neurons tends to infinity (mean field equations) are rigorously derived based on a probabilistic approach. These equations are implicit on the probability distribution of the solutions which generally makes their direct analysis difficult. However, in our case, the solutions are Gaussian, and their moments satisfy a closed system of nonlinear ordinary differential equations (ODEs), which are much easier to study than the original stochastic network equations, and the statistics of the empirical process uniformly converge towards the solutions of these ODEs. Based on this description, we analytically and numerically study the influence of noise on the collective behaviors, and compare these asymptotic regimes to simulations of the network. We observe that the mean field equations provide an accurate description of the solutions of the network equations for network sizes as small as a few hundreds of neurons. In particular, we observe that the level of noise in the system qualitatively modifies its collective behavior, producing for instance synchronized oscillations of the whole network, desynchronization of oscillating regimes, and stabilization or destabilization of stationary solutions. These results shed a new light on the role of noise in shaping collective dynamics of neurons, and gives us clues for understanding similar phenomena observed in biological networks.
Type de document :
Article dans une revue
SIAM Journal on Applied Dynamical Systems, Society for Industrial and Applied Mathematics, 2012, 11 (1), pp.49--81
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Contributeur : Pierre Kornprobst <>
Soumis le : mercredi 17 juillet 2013 - 14:14:04
Dernière modification le : jeudi 11 janvier 2018 - 17:05:01


  • HAL Id : hal-00845597, version 1



Jonathan Touboul, Geoffroy Hermann, Olivier Faugeras. Noise-induced behaviors in neural mean field dynamics. SIAM Journal on Applied Dynamical Systems, Society for Industrial and Applied Mathematics, 2012, 11 (1), pp.49--81. 〈hal-00845597〉



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