Power-law scaling of synchronization robustly reproduced in the hippocampal CA3 slice culture model with small-world topology

Toshikazu Samura, Yasuomi D. Sato, Yuji Ikegaya, Hatsuo Hayashi, Takeshi Aihara

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

Abstract

The hippocampal CA3 is a recurrent network included small-world topology. The percentage of co-active neurons in CA3 slice cultures is approximated by power-law. We show that the power-law scaling of synchronization is reproduced in the CA3 slice culture model where synaptic weights are log-normally distributed and balanced excitation/inhibition regardless of network topologies. However, small-world topology improves the robustness of the reproduction of the power-law scaling in the culture model. Power-law scaling is known as a sign of optimization of a network for information processing. These results suggest that CA3 may be robustly optimized for information processing by excitation/inhibition balance, log-normally distributed synaptic weights and small-world topology.

Original languageEnglish
Title of host publicationNeural Information Processing - 19th International Conference, ICONIP 2012, Proceedings
Pages152-159
Number of pages8
EditionPART 2
DOIs
Publication statusPublished - 2012 Nov 19
Event19th International Conference on Neural Information Processing, ICONIP 2012 - Doha, Qatar
Duration: 2012 Nov 122012 Nov 15

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume7664 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other19th International Conference on Neural Information Processing, ICONIP 2012
CountryQatar
CityDoha
Period12/11/1212/11/15

Keywords

  • Excitation/inhibition balance
  • Hippocampal CA3
  • Log-normal distribution
  • Power-law scaling
  • Small-world topology
  • Synchronization

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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