Nonequilibrium neural network with competing dynamics

A stochastic neural-network model with superposition of neuron and synapse kinetics with a priori probabilities p and 1−p, respectively, is presented. We report on exact and approximate results for p→0. This includes Hopfield-like models in which synaptic intensities locally fluctuate around Hebbian values. It is illustrated how fluctuations influence the phase diagram and, in particular, that an appropriate choice of details may significantly improve the retrieval process.