Learning mechanism for the identification of hidden structures in signal sets

Nathan Intrator*, Bradley S. Seebach

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

Abstract

A neural network is proposed which has the ability to identify signals by recognizing the presence or absence of various signal components, rather than by recognizing input signals as whole units. The presence of rich component structures in naturally-occurring signals suggests that this type of learning is common in human systems. The neural network is built of Hebbian-type neurons with the modification introduced by Bienenstock, Cooper, and Munro (1982), which causes each neuron to become selective for one type of input pattern. These neurons are formed into groups and subgroups. Each group is responsible for detecting one type of input pattern. Every subgroup in a group is responsible for detecting the same input pattern with slightly different parameters.

Original languageEnglish
Pages (from-to)299
Number of pages1
JournalNeural Networks
Volume1
Issue number1 SUPPL
DOIs
StatePublished - 1988
Externally publishedYes
EventInternational Neural Network Society 1988 First Annual Meeting - Boston, MA, USA
Duration: 6 Sep 198810 Sep 1988

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