Learning categories with spiking nets and spike timing dependent plasticity
Huyck, Christian R. ORCID: https://orcid.org/0000-0003-4015-3549
(2020)
Learning categories with spiking nets and spike timing dependent plasticity.
Bramer, Max and Ellis, Richard, eds.
Artificial Intelligence XXXVII, 40th SGAI International Conference on Artificial Intelligence, Proceedings.
In: 40th SGAI 2020, 15-17 Dec 2020, Cambridge, UK.
ISBN 9783030637989, e-ISBN 9783030637996.
ISSN 0302-9743
[Conference or Workshop Item]
(doi:10.1007/978-3-030-63799-6_10)
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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.
Item Type: | Conference or Workshop Item (Poster) |
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Additional Information: | Part of the Lecture Notes in Computer Science book series (LNCS, volume 12498).
Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 12498). |
Keywords (uncontrolled): | Spiking neural network, STDP, Categorisation |
Research Areas: | A. > School of Science and Technology > Computer Science > Artificial Intelligence group |
Item ID: | 31785 |
Notes on copyright: | The final authenticated publication is available online at https://doi.org/10.1007/978-3-030-63799-6_10 |
Useful Links: | |
Depositing User: | Chris Huyck |
Date Deposited: | 06 Jan 2021 17:06 |
Last Modified: | 29 Nov 2022 18:06 |
URI: | https://eprints.mdx.ac.uk/id/eprint/31785 |
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