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

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