Interchangeable Hebbian and Anti-Hebbian STDP Applied to Supervised Learning in Spiking Neural Network

Che Chia Chang, Pin Chun Chen, Boris Hudec, Po-Tsun Liu, Tuo-Hung Hou

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

This work provides a complete framework, including device, architecture, and algorithm, for implementing bio-inspired supervised spiking neural networks (SNNs) on hardware. An analog synapse with atypical dual bipolar resistive-switching (D-BRS) modes demonstrates interchangeable Hebbian spiking-timing-dependent plasticity (STDP) and anti-Hebbian STDP, and it is capable of implementing supervised ReSuMe SNNs in crossbar arrays. By using an 'exchange' update scheme, accurate supervised learning (∼96% for MNIST) is achieved in a compact network.

Original languageEnglish
Title of host publication2018 IEEE International Electron Devices Meeting, IEDM 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages15.5.1-15.5.4
ISBN (Electronic)9781728119878
DOIs
StatePublished - 16 Jan 2019
Event64th Annual IEEE International Electron Devices Meeting, IEDM 2018 - San Francisco, United States
Duration: 1 Dec 20185 Dec 2018

Publication series

NameTechnical Digest - International Electron Devices Meeting, IEDM
Volume2018-December
ISSN (Print)0163-1918

Conference

Conference64th Annual IEEE International Electron Devices Meeting, IEDM 2018
Country/TerritoryUnited States
CitySan Francisco
Period1/12/185/12/18

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