Competitive learning with spiking nets and spike timing dependent plasticity

Chris, Huyck ORCID logoORCID: https://orcid.org/0000-0003-4015-3549 and Orume, Erekpaine (2022) Competitive learning with spiking nets and spike timing dependent plasticity. Bramer, Max and Stahl, Frederic, eds. Artificial Intelligence XXXIX: 42nd SGAI International Conference on Artificial Intelligence, AI 2022, Cambridge, UK, December 13–15, 2022, Proceedings. In: AI-2022: The Forty-second SGAI International Conference, 13-15 Dec 2022, Cambridge, England, UK. ISBN 9783031214424. ISSN 0302-9743 [Conference or Workshop Item] (Accepted/In press)

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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.

Item Type: Conference or Workshop Item (Paper)
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Additional Information: Lecture Notes in Artificial Intelligence
Research Areas: A. > School of Science and Technology > Computer Science
Item ID: 36623
Notes on copyright: 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://link.springer.com/book/9783031214424. 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-manuscript-terms
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Depositing User: Chris Huyck
Date Deposited: 19 Oct 2022 08:17
Last Modified: 19 Oct 2022 12:57
URI: https://eprints.mdx.ac.uk/id/eprint/36623

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