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Spiking neural networks for computational intelligence: an overview

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journal contribution
posted on 30.11.2021, 13:44 by Shirin DoraShirin Dora, Nikola Kasabov
Deep neural networks with rate-based neurons have exhibited tremendous progress in the last decade. However, the same level of progress has not been observed in research on spiking neural networks (SNN), despite their capability to handle temporal data, energy-efficiency and low latency. This could be because the benchmarking techniques for SNNs are based on the methods used for evaluating deep neural networks, which do not provide a clear evaluation of the capabilities of SNNs. Particularly, the benchmarking of SNN approaches with regards to energy efficiency and latency requires realization in suitable hardware, which imposes additional temporal and resource constraints upon ongoing projects. This review aims to provide an overview of the current real-world applications of SNNs and identifies steps to accelerate research involving SNNs in the future.

History

School

  • Science

Department

  • Computer Science

Published in

Big Data and Cognitive Computing

Volume

5

Issue

4

Publisher

MDPI

Version

VoR (Version of Record)

Rights holder

© The authors

Publisher statement

This is an Open Access Article. It is published by MDPI under the Creative Commons Attribution 4.0 Unported Licence (CC BY). Full details of this licence are available at: http://creativecommons.org/licenses/by/4.0/

Acceptance date

09/11/2021

Publication date

2021-11-15

Copyright date

2021

eISSN

2504-2289

Language

en

Depositor

Dr Shirin Dora. Deposit date: 29 November 2021

Article number

67