Spiking neural networks - Algorithms, hardware implementations and applications

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

doi:10.1109/MWSCAS.2017.8052951

Spiking neural networks - Algorithms, hardware implementations and applications

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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

10 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 language	English
Title of host publication	2017 IEEE 60th International Midwest Symposium on Circuits and Systems, MWSCAS 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	426-431
Number of pages	6
ISBN (Electronic)	9781509063895
DOIs	https://doi.org/10.1109/MWSCAS.2017.8052951
State	Published - Sep 27 2017
Externally published	Yes
Event	60th IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2017 - Boston, United States Duration: Aug 6 2017 → Aug 9 2017

Publication series

Name	Midwest Symposium on Circuits and Systems
Volume	2017-August
ISSN (Print)	1548-3746

Conference

Conference	60th IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2017
Country/Territory	United States
City	Boston
Period	08/6/17 → 08/9/17

Funding

ACKNOWLEDGMENT We gratefully acknowledge the collaborators who were at IBM T. J. Watson Research Center, I.I.T. Bombay, Ecole Centrale Lyon and University of Toledo who contributed to the research projects discussed in this paper. This work was supported in part by a grant from CISCO and the Semiconductor Research Corporation.

Funders	Funder number
Semiconductor Research Corporation
Cisco Systems

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10.1109/MWSCAS.2017.8052951

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Kulkarni, S. R., Babu, A. V., & Rajendran, B. (2017). Spiking neural networks - Algorithms, hardware implementations and applications. In 2017 IEEE 60th International Midwest Symposium on Circuits and Systems, MWSCAS 2017 (pp. 426-431). Article 8052951 (Midwest Symposium on Circuits and Systems; Vol. 2017-August). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/MWSCAS.2017.8052951

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Kulkarni, SR, Babu, AV & Rajendran, B 2017, Spiking neural networks - Algorithms, hardware implementations and applications. in 2017 IEEE 60th International Midwest Symposium on Circuits and Systems, MWSCAS 2017., 8052951, Midwest Symposium on Circuits and Systems, vol. 2017-August, Institute of Electrical and Electronics Engineers Inc., pp. 426-431, 60th IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2017, Boston, United States, 08/6/17. https://doi.org/10.1109/MWSCAS.2017.8052951

Spiking neural networks - Algorithms, hardware implementations and applications. / Kulkarni, Shruti R.; Babu, Anakha V.; Rajendran, Bipin.
2017 IEEE 60th International Midwest Symposium on Circuits and Systems, MWSCAS 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 426-431 8052951 (Midwest Symposium on Circuits and Systems; Vol. 2017-August).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Spiking neural networks - Algorithms, hardware implementations and applications

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