A Subthreshold Spiking Neuron Circuit Based on the Izhikevich Model

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

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

Abstract

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.

Original languageEnglish
Title of host publicationArtificial Neural Networks and Machine Learning – ICANN 2021 - 30th International Conference on Artificial Neural Networks, Proceedings
EditorsIgor Farkaš, Paolo Masulli, Sebastian Otte, Stefan Wermter
PublisherSpringer Science and Business Media Deutschland GmbH
Pages177-181
Number of pages5
ISBN (Print)9783030863821
DOIs
Publication statusPublished - 2021
Event30th International Conference on Artificial Neural Networks, ICANN 2021 - Virtual, Online
Duration: 2021 Sep 142021 Sep 17

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12895 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

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

Keywords

  • Izhikevich model
  • Low-power consumption
  • Subthreshold operation

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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