title: Post and pre-compensatory Hebbian learning for categorisation.
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.
authors.affiliation: Department of Computer Science, Middlesex University, London, UK.
authors.forename: Christian R
authors.initials: CR
authors.lastname: Huyck
authors.affiliation: Department of Computer Science, Middlesex University, London, UK.
authors.forename: Ian G
authors.initials: IG
authors.lastname: Mitchell
keywords: Categorisation; Compensatory Hebbian learning; Neural fatigue; Point neural model; Self-organisation; Spontaneous neural spiking