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  Using Spatio-temporal Correlations to Learn Invariant Object Recognition

Wallis, G. (1996). Using Spatio-temporal Correlations to Learn Invariant Object Recognition. Neural networks, 9(9), 1513-1519. doi:10.1016/S0893-6080(96)00041-X.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-EAF6-B Version Permalink: http://hdl.handle.net/21.11116/0000-0005-E6DF-D
Genre: Journal Article

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 Creators:
Wallis, GM1, 2, Author              
Affiliations:
1Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497797              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: A competitive network is described which learns to classify objects on the basis of temporal as well as spatial correlations. This is achieved by using a Hebb-like learning rule which is dependent upon prior as well as current neural activity. The rule is shown to be capable of outperforming a supervised rule on the cross validation test of an invariant character recognition task, given a relatively small training set. It is also shown to outperform the supervised version of Fukushima's Neocognitron (Fukushima, 1980), on a larger training set.

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 Dates: 1996-12
 Publication Status: Published in print
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 Rev. Method: -
 Identifiers: DOI: 10.1016/S0893-6080(96)00041-X
BibTex Citekey: 546
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Title: Neural networks
Source Genre: Journal
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Publ. Info: New York : Pergamon
Pages: - Volume / Issue: 9 (9) Sequence Number: - Start / End Page: 1513 - 1519 Identifier: ISSN: 0893-6080
CoNE: https://pure.mpg.de/cone/journals/resource/954925558496