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Presynaptic lateral inhibition provides a better architecture for self-organising neural networks

Spratling, Michael (1999) Presynaptic lateral inhibition provides a better architecture for self-organising neural networks. [Journal (Paginated)]

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Abstract

Unsupervised learning is an important property of the brain and of many artificial neural networks. A large variety of unsupervised learning algorithms have been proposed. This paper takes a different approach in considering the architecture of the neural network rather than the learning algorithm. It is shown that a self-organising neural network architecture using pre-synaptic lateral inhibition enables a single learning algorithm to find distributed, local, and topological representations as appropriate to the structure of the input data received. It is argued that such an architecture not only has computational advantages but is a better model of cortical self-organisation.

Item Type:Journal (Paginated)
Keywords:neural networks, lateral inhibition, self-organisation, unsupervised learning, neural coding, factorial coding
Subjects:Computer Science > Neural Nets
Neuroscience > Neural Modelling
ID Code:1108
Deposited By:Spratling, Dr Michael
Deposited On:15 Nov 2000
Last Modified:11 Mar 2011 08:54

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