TY - GEN
T1 - Spiking neural networks - Algorithms, hardware implementations and applications
AU - Kulkarni, Shruti R.
AU - Babu, Anakha V.
AU - Rajendran, Bipin
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/9/27
Y1 - 2017/9/27
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85034094648&partnerID=8YFLogxK
U2 - 10.1109/MWSCAS.2017.8052951
DO - 10.1109/MWSCAS.2017.8052951
M3 - Conference contribution
AN - SCOPUS:85034094648
T3 - Midwest Symposium on Circuits and Systems
SP - 426
EP - 431
BT - 2017 IEEE 60th International Midwest Symposium on Circuits and Systems, MWSCAS 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 60th IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2017
Y2 - 6 August 2017 through 9 August 2017
ER -