Instability of attractors in auto-associative networks with bio-inspired fast synaptic noise

J. J. Torres, J. M. Cortes and J. Marro. Instability of attractors in auto-associative networks with bio-inspired fast synaptic noise. Lecture Notes in Computer Science 3512: 161-167, 2005 [pdf]
We studied auto–associative 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.