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Using Spatio-temporal Correlations to Learn Invariant Object Recognition

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Wallis,  GM
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

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.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-EAF6-B
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.