Huyck, C.; Orume, E.

Competitive learning with spiking nets and spike timing dependent plasticity

Conference paper

Huyck, C. and Orume, E. 2022. Competitive learning with spiking nets and spike timing dependent plasticity. Bramer, M. and Stahl, F. (ed.) AI-2022: The Forty-second SGAI International Conference. Cambridge, England, UK 13 - 15 Dec 2022 Springer. pp. 153-166 https://doi.org/10.1007/978-3-031-21441-7_11

ISBN
Type	Conference paper
Title	Competitive learning with spiking nets and spike timing dependent plasticity
Authors	Huyck, C. and Orume, E.
Abstract	This paper explores machine learning using biologically plausible neurons and learning rules. Two systems are developed. The first, for student performance categorisation, uses a two layer system and explores data encoding mechanisms. The second, for digit categorisation, explores competitive behaviour between categorisation neurons using a three layer system with an inhibitory layer. Both are successful. The competitive mechanism from the second system is more plausible biologically, and, by using one neuron per input feature, uses fewer neurons.
Sustainable Development Goals	9 Industry, innovation and infrastructure
Middlesex University Theme	Creativity, Culture & Enterprise
Conference	AI-2022: The Forty-second SGAI International Conference
Page range	153-166
Editors	Bramer, M. and Stahl, F.
ISSN	0302-9743
Electronic	1611-3349
Hardcover	9783031214424
Paperback	9783031214400
Electronic	9783031214417
Publisher	Springer
Publication dates
Print	15 Nov 2022
Online	05 Dec 2022
Publication process dates
Deposited	19 Oct 2022
Accepted	18 Oct 2022
Output status	Published
Accepted author manuscript	compSpikingLearn.pdf
Copyright Statement	This version of the contribution has been accepted for publication, after peer review (when applicable) but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/978-3-031-21441-7_11. Use of this Accepted Version is subject to the publisher’s Accepted Manuscript terms of use https://www.springernature.com/gp/open-research/policies/accepted-ma...
Additional information	Lecture Notes in Artificial Intelligence (LNAI, volume 13652)
Web address (URL)	https://link.springer.com/chapter/10.1007/978-3-031-21441-7_11
Digital Object Identifier (DOI)	https://doi.org/10.1007/978-3-031-21441-7_11
Language	English
Book title	Artificial Intelligence XXXIX: 42nd SGAI International Conference on Artificial Intelligence, AI 2022, Cambridge, UK, December 13–15, 2022, Proceedings

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