Effects of Static and Dynamic Disorder on the Performance of Neural Automata

Simple item page
dc.creatorTorres, J. J.
dc.creatorMarro, J.
dc.creatorGarrido, P. L.
dc.creatorCortes, J. M.
dc.creatorRamos, F.
dc.creatorMunoz, M. A.
dc.date2003-12-09
dc.date.accessioned2026-07-07T02:55:18Z
dc.date.available2026-07-07T02:55:18Z
dc.descriptionWe report on both analytical and numerical results concerning stochastic Hopfield--like neural automata exhibiting the following (biologically inspired) features: (1) Neurons and synapses evolve in time as in contact with respective baths at different temperatures. (2) The connectivity between neurons may be tuned from full connection to high random dilution or to the case of networks with the small--world property and/or scale-free architecture. (3) There is synaptic kinetics simulating repeated scanning of the stored patterns. Though these features may apparently result in additional disorder, the model exhibits, for a wide range of parameter values, an extraordinary computational performance, and some of the qualitative behaviors observed in natural systems. In particular, we illustrate here very efficient and robust associative memory, and jumping between pattern attractors.
dc.description7 pages. 3 figures
dc.identifierhttps://arxiv.org/abs/cond-mat/0312230
dc.identifierhttp://arxiv.org/abs/cond-mat/0312230
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/22892
dc.subjectStatistical Mechanics
dc.titleEffects of Static and Dynamic Disorder on the Performance of Neural Automata
dc.typetext

Files

Collections

arXiv