  We study the synchronization of fully-connected and totally excitatory
integrate and fire neural networks in presence of 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.
