A Subthreshold Spiking Neuron Circuit Based on the Izhikevich Model

Shigeo Sato, Satoshi Moriya, Yuka Kanke, Hideaki Yamamoto, Yoshihiko Horio, Yasushi Yuminaka, Jordi Madrenas

研究成果: Conference contribution

抄録

Low-power neuromorphic hardware is indispensable for edge computing. In this study, we report the simulation results of a spiking neuron circuit. The circuit based on the Izhikevich neuron model is designed to reproduce various types of spikes and is optimized for low-voltage operation. Simulation results indicate that the proposed circuit successfully operates in the subthreshold region and can be utilized for reservoir computing.

本文言語English
ホスト出版物のタイトルArtificial Neural Networks and Machine Learning – ICANN 2021 - 30th International Conference on Artificial Neural Networks, Proceedings
編集者Igor Farkaš, Paolo Masulli, Sebastian Otte, Stefan Wermter
出版社Springer Science and Business Media Deutschland GmbH
ページ177-181
ページ数5
ISBN(印刷版)9783030863821
DOI
出版ステータスPublished - 2021
イベント30th International Conference on Artificial Neural Networks, ICANN 2021 - Virtual, Online
継続期間: 2021 9 142021 9 17

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
12895 LNCS
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Conference

Conference30th International Conference on Artificial Neural Networks, ICANN 2021
CityVirtual, Online
Period21/9/1421/9/17

ASJC Scopus subject areas

  • 理論的コンピュータサイエンス
  • コンピュータ サイエンス(全般)

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