Spiking neural networks - Algorithms, hardware implementations and applications

Shruti R. Kulkarni, Anakha V. Babu, Bipin Rajendran

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

6 Scopus citations

Abstract

Spiking Neural Networks (SNNs) are the third generation of artificial neural networks that closely mimic the time encoding and information processing aspects of the human brain. It has been postulated that these networks are more efficient for realizing cognitive computing systems compared to second generation networks that are widely used in machine learning algorithms today. In this paper, we review the learning algorithms, hardware demonstrations and potential applications of SNN based learning systems.

Original languageEnglish (US)
Title of host publication2017 IEEE 60th International Midwest Symposium on Circuits and Systems, MWSCAS 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages426-431
Number of pages6
ISBN (Electronic)9781509063895
DOIs
StatePublished - Sep 27 2017
Event60th IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2017 - Boston, United States
Duration: Aug 6 2017Aug 9 2017

Publication series

NameMidwest Symposium on Circuits and Systems
Volume2017-August
ISSN (Print)1548-3746

Other

Other60th IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2017
Country/TerritoryUnited States
CityBoston
Period8/6/178/9/17

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Electrical and Electronic Engineering

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