The effect of synaptic depression on stochastic resonance

László Zalányi, Fülöp Bazsó, Péter Érdi

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

Stochastic resonance is a mechanism, where noise plays a beneficial role in amplifying weak signals arriving to some nonlinear system. A possible way to improve the signal-to-noise ratio (SNR) of the output of the system is to use several realizations, and sum and average the output of them in a proper way. In neural systems the different synaptic strengths play important role in the averaging. Earlier studies neglected the effect of the possible rapid synaptic plasticity on stochastic resonance, and tacitly assumed constant synaptic strengths. Here, we systematically examined in a small integrate-and-fire network the interaction between stochasticity and synaptic modifiability, namely synaptic depression. This kind of plasticity mechanism increased the SNR in some parameter regions.

Original languageEnglish
Pages (from-to)459-465
Number of pages7
JournalNeurocomputing
Volume38-40
DOIs
Publication statusPublished - Jun 1 2001

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Keywords

  • Depression synapse
  • Network
  • Stochastic resonance

ASJC Scopus subject areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence

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