Instability of attractors in autoassociative networks with bioinspired fast synaptic noise

dc.creatorTorres, J. J.
dc.creatorCortes, J. M.
dc.creatorMarro, J.
dc.date2006-04-16
dc.date.accessioned2026-07-07T07:11:45Z
dc.date.available2026-07-07T07:11:45Z
dc.descriptionWe 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.
dc.description6 pages, 2 figures
dc.identifierhttps://arxiv.org/abs/q-bio/0604021
dc.identifierhttp://arxiv.org/abs/q-bio/0604021
dc.identifierLNCS 3512: 161-167, 2005
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/111877
dc.subjectNeurons and Cognition
dc.titleInstability of attractors in autoassociative networks with bioinspired fast synaptic noise
dc.typetext

Files

Collections