Guiraud, PierrePierreGuiraudEtienne Tanré2025-12-072025-12-072019-01-0110.3934/dcdsb.20190562-s2.0-85072561171https://cris-uv-2.scimago.es/handle/123456789/7678WOS:000475398100025We study the synchronization of fully-connected and totally excitatory\nintegrate and fire neural networks in presence of Gaussian white noises. Using\na large deviation principle, we prove the stability of the synchronized state\nunder stochastic perturbations. Then, we give a lower bound on the probability\nof synchronization for networks which are not initially synchronized. This\nbound shows the robustness of the emergence of synchronization in presence of\nsmall stochastic perturbations.\nenacceso abiertoApplied MathematicsDiscrete Mathematics And CombinatoricsMathematics, Appliedarticle