CONVERGENCE OF A SELF-ORGANIZING STOCHASTIC NEURAL NETWORK

In this paper, we focus on the convergence of a stochastic neural proc ess. In this process, a "physiologically plausible" Hebb's learning ru le gives rise to a self-organization phenomenon. Some preliminary resu lts concern the asymptotic behaviour of the nework given that the upda te of neurons is either sequential, partially parallel, or massively p arallel. We shall pay attention to the fact that Hebbian learning is c losely linked to the underlying dynamics of the network. Thereafter, w e shall give, within the mathematical framework of stochastic approxim ation, some conditions for convergence of the learning scheme.