Stability of synchronization under stochastic perturbations in leaky integrate and fire neural networks of finite size.

Abstract : In the present paper, we study the synchronization in a model of neural network which can be considered as a noisy version of the model of \citet{mirollo1990synchronization}, namely, fully-connected and totally excitatory integrate and fire neural network with Gaussian white noises. Using a large deviation principle, we prove the stability of the synchronized state under stochastic perturbations. Then, we give a lower bound on the probability of synchronization for networks which are not initially synchronized. This bound shows the robustness of the emergence of synchronization in presence of small stochastic perturbations.
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Pré-publication, Document de travail
2016
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https://hal.inria.fr/hal-01370609
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Dernière modification le : mercredi 10 octobre 2018 - 10:08:52
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  • HAL Id : hal-01370609, version 1
  • ARXIV : 1609.07103

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Pierre Guiraud, Etienne Tanré. Stability of synchronization under stochastic perturbations in leaky integrate and fire neural networks of finite size.. 2016. 〈hal-01370609〉

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