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)

Preview

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

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.

Actions (login required)

View Item