By Itôs differential formula and combining the method of inequality analysis

the problem of stochastic asymptotical stability of a class of stochastic neural networks with time-varying delays and parameter uncertainties is investigated.There are two kinds of disturbances which are unavoidable to be considered in neural networks.The parameter uncertainties are time-varying and norm-bounded.The time-delay factors are unknown and time-varying with known bounds.Based on Lyapunov-Krasovskii functional and stochastic analysis approaches

some new stability criteria are presented in terms of linear matrix inequalities (LMIs) to guarantee the delayed neural network to be stochastically asymptotically stable in the mean square for all admissible uncertainties.Numerical examples are given to demonstrate the usefulness of the proposed asymptotical stability criteria.