Self-Organizing Digital Spike Maps for Learning of Spike-Trains

• OGAWA Takashi

  Hosei University

• SAITO Toshimichi

  Hosei University

Abstract

This paper presents a digital spike map and its learning algorithm of spike-trains. The map is characterized by a swarm of particles on lattice points. As a teacher signal is applied, the algorithm finds a winner particle. The winner and its neighbor particles move in a similar way to the self-organizing maps. A new particle can born and the particle swarm can grow depending on the property of teacher signals. If learning parameters are selected suitably, the map can evolve to approximate a class of teacher signals. Performing basic numerical experiments, the algorithm efficiency is confirmed.

Journal

• IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences

  IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E94-A (12), 2845-2852, 2011

  The Institute of Electronics, Information and Communication Engineers

Keywords

• digital spiking neurons
• cellular automaton
• spike-trains
• supervised learning

Details

• CRID: 1390001206311442944
• NII Article ID: 10030534220
• NII Book ID: AA10826239
• DOI: 10.1587/transfun.e94.a.2845
• ISSN: 17451337
• ISSN: 09168508
• Web Site: http://www.jstage.jst.go.jp/article/transfun/E94.A/12/E94.A_12_2845/_pdf
• Text Lang: en
• Data Source:
  • JaLC
  • Crossref
  • CiNii Articles
  • KAKEN
• Abstract License Flag: Disallowed