Self-organization of feed-forward structure and entrainment in excitatory neural networks with spike-timing-dependent plasticity

Yuko K. Takahashi, Hiroshi Kori, Naoki Masuda

Research output: Contribution to journalArticlepeer-review

32 Citations (Scopus)

Abstract

Spike-timing dependent plasticity (STDP) is an organizing principle of biological neural networks. While synchronous firing of neurons is considered to be an important functional block in the brain, how STDP shapes neural networks possibly toward synchrony is not entirely clear. We examine relations between STDP and synchronous firing in spontaneously firing neural populations. Using coupled heterogeneous phase oscillators placed on initial networks, we show numerically that STDP prunes some synapses and promotes formation of a feedforward network. Eventually a pacemaker, which is the neuron with the fastest inherent frequency in our numerical simulations, emerges at the root of the feedforward network. In each oscillatory cycle, a packet of neural activity is propagated from the pacemaker to downstream neurons along layers of the feedforward network. This event occurs above a clear-cut threshold value of the initial synaptic weight. Below the threshold, neurons are self-organized into separate clusters each of which is a feedforward network.

Original languageEnglish
Article number051904
JournalPhysical Review E - Statistical, Nonlinear, and Soft Matter Physics
Volume79
Issue number5
DOIs
Publication statusPublished - 2009 May 11
Externally publishedYes

ASJC Scopus subject areas

  • Statistical and Nonlinear Physics
  • Statistics and Probability
  • Condensed Matter Physics

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