---
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.'
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chapter: ~
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creators_id: []
creators_name:
- family: Spratling
given: Michael
honourific: ''
lineage: ''
date: 1999
date_type: published
datestamp: 2000-11-15
department: ~
dir: disk0/00/00/11/08
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editors_id: []
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eprint_status: archive
eprintid: 1108
fileinfo: /style/images/fileicons/application_postscript.png;/1108/2/factopol.ps
full_text_status: public
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ispublished: pub
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keywords: 'neural networks, lateral inhibition, self-organisation, unsupervised learning, neural coding, factorial coding'
lastmod: 2011-03-11 08:54:27
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longitude: ~
metadata_visibility: show
note: ~
number: 4
pagerange: 285-301
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publication: 'Network: Computation in Neural Systems,'
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refereed: TRUE
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reportno: ~
rev_number: 10
series: ~
source: ~
status_changed: 2007-09-12 16:36:27
subjects:
- comp-sci-neural-nets
- neuro-mod
succeeds: ~
suggestions: ~
sword_depositor: ~
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thesistype: ~
title: Presynaptic lateral inhibition provides a better architecture for self-organising neural networks
type: journalp
userid: 1040
volume: 10