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 obeying stochastic dynamics but without the necessity of detailed balance. The specific case of Ising systems is highlighted, with applications to non-symmetric synaptic neural networks and more general recurrent Boolean networks.