Huyck, C.

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

ISBN
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
Hardcover	9783030637989
Electronic	9783030637996
Publisher	Springer
Publication dates
Online	08 Dec 2020
Print	15 Dec 2020
Publication process dates
Deposited	06 Jan 2021
Accepted	15 Sep 2020
Output status	Published
Accepted author manuscript	shortSpikeLearn.pdf
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). Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, 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

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