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Journal of Mathematical Biology

, Volume 78, Issue 6, pp 1771–1820 | Cite as

Synchronization of stochastic mean field networks of Hodgkin–Huxley neurons with noisy channels

  • Mireille Bossy
  • Joaquín Fontbona
  • Héctor OliveroEmail author
Article
  • 154 Downloads

Abstract

In this work we are interested in a mathematical model of the collective behavior of a fully connected network of finitely many neurons, when their number and when time go to infinity. We assume that every neuron follows a stochastic version of the Hodgkin–Huxley model, and that pairs of neurons interact through both electrical and chemical synapses, the global connectivity being of mean field type. When the leak conductance is strictly positive, we prove that if the initial voltages are uniformly bounded and the electrical interaction between neurons is strong enough, then, uniformly in the number of neurons, the whole system synchronizes exponentially fast as time goes to infinity, up to some error controlled by (and vanishing with) the channels noise level. Moreover, we prove that if the random initial condition is exchangeable, on every bounded time interval the propagation of chaos property for this system holds (regardless of the interaction intensities). Combining these results, we deduce that the nonlinear McKean–Vlasov equation describing an infinite network of such neurons concentrates, as time goes to infinity, around the dynamics of a single Hodgkin–Huxley neuron with chemical neurotransmitter channels. Our results are illustrated and complemented with numerical simulations.

Keywords

Hodgkin–Huxley neurons Synchronization of neuron networks Mean-field limits Propagation of chaos Stochastic differential equations 

Mathematics Subject Classification

60H99 60K35 82C22 82C32 92B20 92B25 

Notes

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.INRIA Sophia Antipolis MéditerranéeSophia AntipolisFrance
  2. 2.Department of Mathematical Engineering and Center for Mathematical ModelingUMI(2807) UCHILE-CNRS, University of ChileSantiagoChile
  3. 3.CIMFAV, Facultad de IngenieríaUniversidad de ValparaísoValparaísoChile

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