Exponential stability of the stationary distribution of a mean field of spiking neural network - Inria - Institut national de recherche en sciences et technologies du numérique
Journal Articles Journal of Differential Equations Year : 2018

Exponential stability of the stationary distribution of a mean field of spiking neural network

Audric Drogoul
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Romain Veltz

Abstract

In this work, we study the exponential stability of the stationary distribution of a McKean-Vlasov equation, of nonlinear hyperbolic type which was recently derived in \cite{de_masi_hydrodynamic_2015,fournier_toy_2016}. We complement the convergence result proved in \cite{fournier_toy_2016} using tools from dynamical systems theory. Our proof relies on two principal arguments in addition to a Picard-like iteration method. First, the linearized semigroup is positive which allows to precisely pinpoint the spectrum of the infinitesimal generator. Second, we use a time rescaling argument to transform the original quasilinear equation into another one for which the nonlinear flow is differentiable. Interestingly, this convergence result can be interpreted as the existence of a locally exponentially attracting center manifold for a hyperbolic equation.
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Dates and versions

hal-01290264 , version 1 (08-04-2016)
hal-01290264 , version 2 (13-04-2018)

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Audric Drogoul, Romain Veltz. Exponential stability of the stationary distribution of a mean field of spiking neural network. Journal of Differential Equations, 2018, ⟨10.1016/j.jde.2020.08.001⟩. ⟨hal-01290264v2⟩
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