Spiking neural networks - Algorithms, hardware implementations and applications
Document Type
Conference Proceeding
Publication Date
9-27-2017
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.
Identifier
85034094648 (Scopus)
ISBN
[9781509063895]
Publication Title
Midwest Symposium on Circuits and Systems
External Full Text Location
https://doi.org/10.1109/MWSCAS.2017.8052951
ISSN
15483746
First Page
426
Last Page
431
Volume
2017-August
Fund Ref
Semiconductor Research Corporation
Recommended Citation
Kulkarni, Shruti R.; Babu, Anakha V.; and Rajendran, Bipin, "Spiking neural networks - Algorithms, hardware implementations and applications" (2017). Faculty Publications. 9297.
https://digitalcommons.njit.edu/fac_pubs/9297
