Modular networks of spiking neurons for applications in time-series information processing

• Moriya Satoshi

  Research Institute of Electrical Communication, Tohoku University

• Yamamoto Hideaki

  Research Institute of Electrical Communication, Tohoku University

• Hirano-Iwata Ayumi

  Advanced Institute for Materials Research, Tohoku University

• Kubota Shigeru

  Graduate School of Science and Engineering, Yamagata University

• Sato Shigeo

  Research Institute of Electrical Communication, Tohoku University

Abstract

Spiking neural networks with complex spatiotemporal dynamics support efficient information processing of time-series signals. Here, we investigate the relationship between complexity of network dynamics and modular topology of networks using numerical simulations and discuss their effect on the classification performance of spoken-digit recognition tasks. The results show that modular networks generate spatially complex dynamics in which partially and globally synchronous bursts coexist. The classification rate of the modular reservoir network was approximately 75%, a value of which was comparable to that of a random network. This was caused by the randomly-connection structure between the input-reservoir and reservoir-readout layers, thus appropriate inference methods and asymmetry of connections should be introduced to take advantage of the complex dynamics in modular networks.

Journal

• Nonlinear Theory and Its Applications, IEICE

  Nonlinear Theory and Its Applications, IEICE 11 (4), 590-600, 2020

  The Institute of Electronics, Information and Communication Engineers

Keywords

• neural network
• reservoir computing
• integrate-and-fire model
• modular organization
• synchronous activity

Details

• CRID: 1390567172584122240
• NII Article ID: 130007921436
• DOI: 10.1587/nolta.11.590
• ISSN: 21854106
• Web Site: https://www.jstage.jst.go.jp/article/nolta/11/4/11_590/_pdf
• Text Lang: en
• Data Source:
  • JaLC
  • Crossref
  • CiNii Articles
  • KAKEN
• Abstract License Flag: Disallowed