Asymptotic behaviors of stochastic Cohen–Grossberg neural networks with mixed time-delays

Asymptotic behaviors of stochastic Cohen–Grossberg neural networks with mixed time-delays are investigated, where the mixed time-delays comprise both the discrete time-varying delays and the distributed time-delays. The theory of stochastic functional differential equations is applied to establish two sets of novel criteria on asymptotic stability and ultimate boundedness. Finally, two examples are given to illustrate our theoretical results and to indicate that two sets of criteria do not include each other.