  We studied autoassociative networks in which synapses are noisy on a time
scale much shorter that the one for the neuron dynamics. In our model a
presynaptic noise causes postsynaptic depression as recently observed in
neurobiological systems. This results in a nonequilibrium condition in which
the network sensitivity to an external stimulus is enhanced. In particular, the
fixed points are qualitatively modified, and the system may easily scape from
the attractors. As a result, in addition to pattern recognition, the model is
useful for class identification and categorization.
