Huyck, C.

Quasi Biologically Plausible Category Learning

Conference paper

Huyck, C. 2024. Quasi Biologically Plausible Category Learning. 44th SGAI International Conference on Artificial Intelligence, AI 2024. Cambridge, UK 17 - 19 Dec 2024 Springer.

Publication dates
Type	Conference paper
Title	Quasi Biologically Plausible Category Learning
Authors	Huyck, C.
Abstract	This paper explores machine learning using adaptive spiking neurons and spike timing dependent plasticity (STDP). This is shown to work on two categorisation tasks. It is neuro-biologically flawed but works with a small number of point neurons, and is much closer to biology than multi layer perceptrons. The work is derived from mathematical exploration and the portion of the parameter space where categorisation works is small. This is just a proof of concept that categorisation can be done by these spiking competitive nets with STDP. The parameter space could be further explored to find better results, or how to apply this to new categorisation tasks. This work provides support for further exploration of neurobiologically plausible category learning.
Keywords	Spiking Neurons; Spike Timing Dependent Plasticity; Categorisation; MNIST
Sustainable Development Goals	9 Industry, innovation and infrastructure
Middlesex University Theme	Creativity, Culture & Enterprise
Research Group	Artificial Intelligence group
Conference	44th SGAI International Conference on Artificial Intelligence, AI 2024
Proceedings Title	Artificial Intelligence XLI: 44th SGAI International Conference on Artificial Intelligence, AI 2024, Cambridge, UK, December 17–19, 2024, Proceedings
Series	Lecture Notes in Computer Science
ISSN	0302-9743
Electronic	1611-3349
Publisher	Springer
Print	17 Dec 2024
Publication process dates
Accepted	30 Aug 2024
Deposited	25 Sep 2024
Output status	Accepted
Accepted author manuscript	File Access Level Open
Copyright Statement	This version of the contribution has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use (https://www.springernature.com/gp/open-research/policies/accepted-ma...), 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:

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