Using spatio-temporal correlations to learn invariant object recognition

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. Copyright (C) 1996 Elsevier Science Ltd