Stability of synchronization under stochastic perturbations in leaky   integrate and fire neural networks of finite size
  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. 
Pierre Guiraud and Etienne Tanr\'e
math.PR q-bio.NC