EQUILIBRIUM DISTRIBUTIONS OF STOCHASTIC NETWORKS WITHOUT DETAILED BALANCE

We present results for the determination of the equilibrium microstate probability distribution of a class of strongly interacting systems o beying stochastic dynamics but without the necessity of detailed balan ce. The specific case of Ising systems is highlighted, with applicatio ns to non-symmetric synaptic neural networks and more general recurren t Boolean networks.