  We study both analytically and numerically the effect of presynaptic noise on
the transmission of information in attractor neural networks. The noise occurs
on a very short-time scale compared to that for the neuron dynamics and it
produces short-time synaptic depression. This is inspired in recent
neurobiological findings that show that synaptic strength may either increase
or decrease on a short-time scale depending on presynaptic activity. We thus
describe a mechanism by which fast presynaptic noise enhances the neural
network sensitivity to an external stimulus. The reason for this is that, in
general, the presynaptic noise induces nonequilibrium behavior and,
consequently, the space of fixed points is qualitatively modified in such a way
that the system can easily scape from the attractor. As a result, the model
shows, in addition to pattern recognition, class identification and
categorization, which may be relevant to the understanding of some of the brain
complex tasks.
