Post and pre-compensatory Hebbian Learning for categorisation

Article


Huyck, C. and Mitchell, I. 2014. Post and pre-compensatory Hebbian Learning for categorisation. Cognitive Neurodynamics. 8 (4), pp. 299-311. https://doi.org/10.1007/s11571-014-9282-4
TypeArticle
TitlePost and pre-compensatory Hebbian Learning for categorisation
AuthorsHuyck, C. and Mitchell, I.
Abstract

A system with some degree of biological plausibility is developed to categorise items from a widely used machine learning benchmark. The system uses fatiguing leaky integrate and fire neurons, a relatively coarse point model that roughly duplicates biological spiking properties; this allows spontaneous firing based on hypo-fatigue so that neurons not directly stimulated by the environment may be included in the circuit. A novel compensatory Hebbian learning algorithm is used that considers the total synaptic weight coming into a neuron. The network is unsupervised and entirely self-organising. This is relatively effective as a machine learning algorithm, categorising with just neurons, and the performance is comparable with a Kohonen map. However the learning algorithm is not stable, and behaviour decays as length of training increases. Variables including learning rate, inhibition and topology are explored leading to stable systems driven by the environment. The model is thus a reasonable next step toward a full neural memory model.

Research GroupArtificial Intelligence group
LanguageEnglish
PublisherSpringer
JournalCognitive Neurodynamics
ISSN1871-4080
Electronic1871-4099
Publication dates
Online30 Jan 2014
Print31 Aug 2014
Publication process dates
Deposited23 Apr 2015
Accepted22 Jan 2014
Output statusPublished
Digital Object Identifier (DOI)https://doi.org/10.1007/s11571-014-9282-4
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