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
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, |
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 |
ISBN | |
Hardcover | 9783031214424 |
Paperback | 9783031214400 |
Electronic | 9783031214417 |
Publisher | Springer |
Publication dates | |
15 Nov 2022 | |
Online | 05 Dec 2022 |
Publication process dates | |
Deposited | 19 Oct 2022 |
Accepted | 18 Oct 2022 |
Output status | Published |
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: |
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 |
https://repository.mdx.ac.uk/item/8q1xw
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