Learning categories with spiking nets and spike timing dependent plasticity
Conference poster
Huyck, C. 2020. Learning categories with spiking nets and spike timing dependent plasticity. Bramer, M. and Ellis, R. (ed.) 40th SGAI 2020. Cambridge, UK 15 - 17 Dec 2020 Springer. pp. 139-144 https://doi.org/10.1007/978-3-030-63799-6_10
Type | Conference poster |
---|---|
Title | Learning categories with spiking nets and spike timing dependent plasticity |
Authors | Huyck, C. |
Abstract | An exploratory study of learning a neural network for categorisation shows that commonly used leaky integrate and fire neurons and Hebbian learning can be effective. The system learns with a standard spike timing dependent plasticity Hebbian learning rule. A two layer feed forward topology is used with a presentation mechanism of inputs followed by outputs a simulated ms. later to learn Iris flower and Breast Cancer Tumour Malignancy categorisers. An exploration of parameters indicates how this may be applied to other tasks. |
Keywords | Spiking neural network, STDP, Categorisation |
Research Group | Artificial Intelligence group |
Conference | 40th SGAI 2020 |
Page range | 139-144 |
Editors | Bramer, M. and Ellis, R. |
ISSN | 0302-9743 |
Electronic | 1611-3349 |
ISBN | |
Hardcover | 9783030637989 |
Electronic | 9783030637996 |
Publisher | Springer |
Publication dates | |
Online | 08 Dec 2020 |
15 Dec 2020 | |
Publication process dates | |
Deposited | 06 Jan 2021 |
Accepted | 15 Sep 2020 |
Output status | Published |
Accepted author manuscript | |
Copyright Statement | The final authenticated publication is available online at https://doi.org/10.1007/978-3-030-63799-6_10 |
Additional information | Part of the Lecture Notes in Computer Science book series (LNCS, volume 12498). |
Web address (URL) | https://link.springer.com/chapter/10.1007/978-3-030-63799-6_10 |
Digital Object Identifier (DOI) | https://doi.org/10.1007/978-3-030-63799-6_10 |
Language | English |
Book title | Artificial Intelligence XXXVII, 40th SGAI International Conference on Artificial Intelligence, Proceedings |
https://repository.mdx.ac.uk/item/8939y
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