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
TypeConference paper
TitleCompetitive learning with spiking nets and spike timing dependent plasticity
AuthorsHuyck, 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 Goals9 Industry, innovation and infrastructure
Middlesex University ThemeCreativity, Culture & Enterprise
ConferenceAI-2022: The Forty-second SGAI International Conference
Page range153-166
EditorsBramer, M. and Stahl, F.
ISSN0302-9743
Electronic1611-3349
ISBN
Hardcover9783031214424
Paperback9783031214400
Electronic9783031214417
PublisherSpringer
Publication dates
Print15 Nov 2022
Online05 Dec 2022
Publication process dates
Deposited19 Oct 2022
Accepted18 Oct 2022
Output statusPublished
Accepted author manuscript
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
LanguageEnglish
Book titleArtificial Intelligence XXXIX: 42nd SGAI International Conference on Artificial Intelligence, AI 2022, Cambridge, UK, December 13–15, 2022, Proceedings
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