Structural analysis on STDP neural networks using complex network theory

Hideyuki Kato, Tohru Ikeguchi, Kazuyuki Aihara

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationArtificial Neural Networks - ICANN 2009 - 19th International Conference, Proceedings
Pages306-314
Number of pages9
EditionPART 1
DOIs
Publication statusPublished - 6 Nov 2009
Event19th International Conference on Artificial Neural Networks, ICANN 2009 - Limassol, Cyprus
Duration: 14 Sep 200917 Sep 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume5768 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference19th International Conference on Artificial Neural Networks, ICANN 2009
CountryCyprus
CityLimassol
Period14/09/0917/09/09

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Kato, H., Ikeguchi, T., & Aihara, K. (2009). Structural analysis on STDP neural networks using complex network theory. In Artificial Neural Networks - ICANN 2009 - 19th International Conference, Proceedings (PART 1 ed., pp. 306-314). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5768 LNCS, No. PART 1). https://doi.org/10.1007/978-3-642-04274-4_32