Learning categories with spiking nets and spike timing dependent plasticity

Huyck, Christian R. ORCID logoORCID: 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)

PDF - Final accepted version (with author's formatting)
Download (155kB) | Preview


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)
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

Actions (login required)

View Item View Item


Activity Overview
6 month trend
6 month trend

Additional statistics are available via IRStats2.