Oscillations and spiking pairs: behavior of a neuronal model with STDP learning

Xi Shen, Xiaobin Lin, Philippe De Wilde

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

In a biologically plausible but computationally simplified integrate-and-fire neuronal population, it is observed that transient synchronized spikes can occur repeatedly. However, groups with different properties exhibit different periods and different patterns of synchrony. We include learning mechanisms in these models. The effects of spike-timing- dependent plasticity have been known to play a distinct role in information processing in the central nervous system for several years. In this letter, neuronal models with dynamical synapses are constructed, and we analyze the effect of STDP on collective network behavior, such as oscillatory activity, weight distribution, and spike timing precision. We comment on how information is encoded by the neuronal signaling, when synchrony groups may appear, and what could contribute to the uncertainty in decision making. © 2008 Massachusetts Institute of Technology.

Original languageEnglish
Pages (from-to)2037-2069
Number of pages33
JournalNeural Computation
Volume20
Issue number8
DOIs
Publication statusPublished - Aug 2008

Keywords

  • DEPENDENT SYNAPTIC PLASTICITY
  • RABBIT FOLLOWING STIMULATION
  • LONG-LASTING POTENTIATION
  • CORTICAL-NEURONS
  • NEURAL-NETWORKS
  • PERFORANT PATH
  • DENTATE AREA
  • SYNCHRONIZATION
  • TRANSMISSION
  • POPULATIONS

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