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A review of learning in biologically plausible spiking neural networks

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journal contribution
posted on 08.11.2019, 09:26 by Aboozar Taherkhani, Ammar Belatreche, Yuhua Li, Georgina CosmaGeorgina Cosma, Liam P Maguire, TM McGinnity
Artificial neural networks have been used as a powerful processing tool in various areas such as pattern recognition, control, robotics, and bioinformatics. Their wide applicability has encouraged researchers to improve artificial neural networks by investigating the biological brain. Neurological research has significantly progressed in recent years and continues to reveal new characteristics of biological neurons. New technologies can now capture temporal changes in the internal activity of the brain in more detail and help clarify the relationship between brain activity and the perception of a given stimulus. This new knowledge has led to a new type of artificial neural network, the Spiking Neural Network (SNN), that draws more faithfully on biological properties to provide higher processing abilities. A review of recent developments in learning of spiking neurons is presented in this paper. First the biological background of SNN learning algorithms is reviewed. The important elements of a learning algorithm such as the neuron model, synaptic plasticity, information encoding and SNN topologies are then presented. Then, a critical review of the state-of-the-art learning algorithms for SNNs using single and multiple spikes is presented. Additionally, deep spiking neural networks are reviewed, and challenges and opportunities in the SNN field are discussed.

Funding

The Leverhulme Trust (Research Project Grant No: RPG-2016-252, Title: Novel Approaches for Constructing Optimised Multimodal Data Spaces).

History

School

  • Science

Department

  • Computer Science

Published in

Neural Networks

Volume

122

Pages

253 - 272

Publisher

Elsevier BV

Version

AM (Accepted Manuscript)

Rights holder

© Elsevier

Publisher statement

This paper was accepted for publication in the journal Neural Networks and the definitive published version is available at https://doi.org/10.1016/j.neunet.2019.09.036

Acceptance date

23/09/2019

Publication date

2019-10-11

Copyright date

2019

ISSN

0893-6080

Language

en

Depositor

Dr Georgina Cosma Deposit date: 7 November 2019