A net for automatic detection of minimal correlation order in contextual pattern recognition

Abstract

The authors propose a neural net able to recognize input pattern sequences by memorizing only one of the transformed patterns, the prototype forming the sequence. This capacity depends on an automatic control of the minimal correlation order to perform recognition tasks and, in ambiguous cases, on a type of context-dependent memory recalling. The neural net model can use the noise constructively to modify continuously the learned prototype pattern in view of a contextual recognition of input pattern sequences. In such a way, the net is able to deduce, by itself, from the prototype pattern, the hypotheses by which it can recognize highly corrupted static patterns, or sequences of transformed patterns.<>