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arXiv · 2008.11116

Hopf bifurcation in a Mean-Field model of spiking neurons

Quentin Cormier·Etienne Tanré·Romain Veltz
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Abstract

We study a family of non-linear McKean-Vlasov SDEs driven by a Poisson measure, modelling the mean-field asymptotic of a network of generalized Integrate-and-Fire neurons. We give sufficient conditions to have periodic solutions through a Hopf bifurcation. Our spectral conditions involve the location of the roots of an explicit holomorphic function. The proof relies on two main ingredients. First, we introduce a discrete time Markov Chain modeling the phases of the successive spikes of a neuron. The invariant measure of this Markov Chain is related to the shape of the periodic solutions. Secondly, we use the Lyapunov-Schmidt method to obtain self-consistent oscillations. We illustrate the result with a toy model for which all the spectral conditions can be analytically checked.

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Quentin Cormier, Etienne Tanré, Romain Veltz. 2020-08-25. Hopf bifurcation in a Mean-Field model of spiking neurons. https://doi.org/10.1214/21-ejp688

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