SEISAN KENKYU
Online ISSN : 1881-2058
Print ISSN : 0037-105X
ISSN-L : 0037-105X
Research Review
Deep Learning for Spiking Neural Networks
Yusuke SAKEMIKai MORINO
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JOURNAL FREE ACCESS

2019 Volume 71 Issue 2 Pages 159-167

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Abstract

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.

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© 2019 Institute of Industrial Science The University of Tokyo
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