Spiking Neural Networks for Computational Intelligence: An Overview

Shirin Dora, Nikola Kasabov

Research output: Contribution to journalArticlepeer-review

Abstract

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 la-tency. 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 re-al-world applications of SNNs and identifies steps to accelerate research involving SNNs in the future.


Original languageEnglish
Article numbere67
Pages (from-to)1-12
Number of pages12
JournalBig Data and Cognitive Computing
Volume5
Issue number4
Early online date15 Nov 2021
DOIs
Publication statusE-pub ahead of print - 15 Nov 2021

Keywords

  • spiking neural networks
  • neuromorphic computing
  • brain-inspired learning

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