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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