Exponential stability of stochastic, retarded neural networks

The stability analysis of neural networks is important in the applications and has been studied by many authors. However, only recently has the stability of stochastic models of neural networks been investigated. In this paper we analyse the global asymptotic stability of a class of neural networks described by a stochastic delay differential equation. It can be argued that such a model is as comprehensive as one would like to be when studying perturbations of neural networks since delay siganalling and noise are accounted for. We present a convergence theorem and discuss some examples of its use