Firing synchronization of learning neuronal networks with small-world connectivity

F. Han, Q. S. Lu, M. Wiercigroch, J. A. Fang, Z. J. Wang

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

The properties of firing synchronization of learning neuronal networks, electrically and chemically coupled ones, with small-world connectivity are studied. First, the variation properties of synaptic weights are examined. Next the effects of the synaptic learning rate on the properties of firing rate and synchronization are investigated. The influences of the coupling strength and the shortcut probability on synchronization are also explored. It is shown that synaptic learning suppresses over-excitement for the networks, helps synchronization for the electrically coupled neuronal network but destroys synchronization for the chemically coupled one. Both introducing shortcuts and increasing the coupling strength are helpful in improving synchronization of the neuronal networks. The spatio-temporal patterns illustrate and confirm the above results.
Original languageEnglish
Pages (from-to)1161-1166
Number of pages6
JournalInternational Journal of Non-Linear Mechanics
Volume47
Issue number10
Early online date8 Sep 2011
DOIs
Publication statusPublished - Dec 2012

Keywords

  • Firing rate
  • synchronization
  • Learning
  • Neuronal networks
  • small world

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