@inproceedings{acba1d60f67d4ec18a05e8deb59c86df,
title = "Spiking neural networks - Algorithms, hardware implementations and applications",
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.",
author = "Kulkarni, {Shruti R.} and Babu, {Anakha V.} and Bipin Rajendran",
note = "Funding Information: 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. Publisher Copyright: {\textcopyright} 2017 IEEE.; 60th IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2017 ; Conference date: 06-08-2017 Through 09-08-2017",
year = "2017",
month = sep,
day = "27",
doi = "10.1109/MWSCAS.2017.8052951",
language = "English (US)",
series = "Midwest Symposium on Circuits and Systems",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "426--431",
booktitle = "2017 IEEE 60th International Midwest Symposium on Circuits and Systems, MWSCAS 2017",
address = "United States",
}