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Biological Cybernetics

, Volume 101, Issue 2, pp 103–114 | Cite as

Emergence of network structure due to spike-timing-dependent plasticity in recurrent neuronal networks. II. Input selectivity—symmetry breaking

  • Matthieu GilsonEmail author
  • Anthony N. Burkitt
  • David B. Grayden
  • Doreen A. Thomas
  • J. Leo van Hemmen
Original Paper

Abstract

Spike-timing-dependent plasticity (STDP) is believed to structure neuronal networks by slowly changing the strengths (or weights) of the synaptic connections between neurons depending upon their spiking activity, which in turn modifies the neuronal firing dynamics. In this paper, we investigate the change in synaptic weights induced by STDP in a recurrently connected network in which the input weights are plastic but the recurrent weights are fixed. The inputs are divided into two pools with identical constant firing rates and equal within-pool spike-time correlations, but with no between-pool correlations. Our analysis uses the Poisson neuron model in order to predict the evolution of the input synaptic weights and focuses on the asymptotic weight distribution that emerges due to STDP. The learning dynamics induces a symmetry breaking for the individual neurons, namely for sufficiently strong within-pool spike-time correlation each neuron specializes to one of the input pools. We show that the presence of fixed excitatory recurrent connections between neurons induces a group symmetry-breaking effect, in which neurons tend to specialize to the same input pool. Consequently STDP generates a functional structure on the input connections of the network.

Keywords

Learning STDP Recurrent neuronal network Spike-time correlation Symmetry breaking 

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Copyright information

© Springer-Verlag 2009

Authors and Affiliations

  • Matthieu Gilson
    • 1
    • 2
    • 3
    Email author
  • Anthony N. Burkitt
    • 1
    • 2
    • 3
  • David B. Grayden
    • 1
    • 2
    • 3
  • Doreen A. Thomas
    • 1
    • 3
  • J. Leo van Hemmen
    • 4
  1. 1.Department of Electrical and Electronic EngineeringUniversity of MelbourneMelbourneAustralia
  2. 2.The Bionic Ear InstituteEast MelbourneAustralia
  3. 3.NICTAVictoria Research LabMelbourneAustralia
  4. 4.Physik Department (T35), BCCN MunichTechnische Universität MünchenGarching bei MünchenGermany

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