Deep Learning for Spiking Neural Networks

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  • SAKEMI Yusuke
    日本電気株式会社 東京大学生産技術研究所　社会課題解決のためのブレインモルフィックAI 社会連携研究部門
  • MORINO Kai
    東京大学生産技術研究所　社会課題解決のためのブレインモルフィックAI 社会連携研究部門

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Other Title
  • スパイキングニューラルネットワークにおける深層学習
  • スパイキングニューラルネットワーク ニ オケル シンソウ ガクシュウ

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Description

<p>A spiking neural network (SNN) is a model that is inspired by information processing in the brains. SNN processes information with action potentials, or spikes. Recently, studies on the deep learning for SNN have been investigated because it could provide us a new powerful information processing tool. Because introducing conventional deep learning algorithms to SNN is mathematically difficult, several techniques that enable those introductions have been proposed. In this review, we introduce several deep learning algorithms in SNN for supervised learning and unsupervised learning. As for supervised learning, the error backpropagation algorithms are explained, while for unsupervised learning algorithms based on spiketime-dependent plasticity are explained.</p>

Journal

  • SEISAN KENKYU

    SEISAN KENKYU 71 (2), 159-167, 2019-03-01

    Institute of Industrial Science The University of Tokyo

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