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Structural Analysis on STDP Neural Networks Using Complex Network Theory

  • Hideyuki Kato
  • Tohru Ikeguchi
  • Kazuyuki Aihara
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5768)

Abstract

Synaptic plasticity is one of essential and central functions for the memory, the learning, and the development of the brains. Triggered by recent physiological experiments, the basic mechanisms of the spike-timing-dependent plasticity (STDP) have been widely analyzed in model studies. In this paper, we analyze complex structures in neural networks evolved by the STDP. In particular, we introduce the complex network theory to analyze spatiotemporal network structures constructed through the STDP. As a result, we show that nonrandom structures emerge in the neural network through the STDP.

Keywords

Synaptic Plasticity Degree Distribution Characteristic Path Length Spatiotemporal Structure Surrogate Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Hideyuki Kato
    • 1
  • Tohru Ikeguchi
    • 1
    • 3
  • Kazuyuki Aihara
    • 2
    • 3
  1. 1.Graduate School of Science and EngineeringSaitama UniversitySaitamaJapan
  2. 2.Graduate School of Information Science and TechnologyThe University of TokyoTokyoJapan
  3. 3.Aihara Complexity Modelling Project, ERATO, JSTTokyoJapan

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