A Markovian study of recurrent neural networks with stochastic dynamics

Abstract

Recurrent neural networks of binary stochastic units with a general distribution function are studied using Markov chains theory. Sufficient conditions for ergodicity are established and under some assumptions, the stationary distribution is determined. The relation between fixed points and absorbing states is studied both theoretically and through simulations. For numerical studies the notion of almost absorbing state is introduced.