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2021 Hopf bifurcation in a Mean-Field model of spiking neurons
Quentin Cormier, Etienne Tanré, Romain Veltz
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Electron. J. Probab. 26: 1-40 (2021). DOI: 10.1214/21-EJP688


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.

Funding Statement

This project/research has received funding from the European Union’s Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement No. 945539 (Human Brain Project SGA3).


The authors thank the anonymous referees for their comments which contributed to clarify the presentation of this work.


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Quentin Cormier. Etienne Tanré. Romain Veltz. "Hopf bifurcation in a Mean-Field model of spiking neurons." Electron. J. Probab. 26 1 - 40, 2021.


Received: 25 August 2020; Accepted: 19 August 2021; Published: 2021
First available in Project Euclid: 23 September 2021

Digital Object Identifier: 10.1214/21-EJP688

Primary: 60K35
Secondary: 35B10 , 35B32 , 60H10

Keywords: Hopf bifurcation , long time behavior , McKean-Vlasov SDE , Mean-field interaction , Piecewise deterministic Markov process , Volterra integral equation

Vol.26 • 2021
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