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

  • Authors:

  • Joaquín J. Torres;Jesús M. Cortés;Joaquín Marro

  • Affiliations:

  • Departamento de Electromagnetismo y Física de la Materia, Facultad de Ciencias, Universidad de Granada, Granada, Spain;Departamento de Electromagnetismo y Física de la Materia, Facultad de Ciencias, Universidad de Granada, Granada, Spain;Departamento de Electromagnetismo y Física de la Materia, Facultad de Ciencias, Universidad de Granada, Granada, Spain

  • Venue:
  • IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
  • Year:
  • 2005

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Abstract

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.