Self-organizing neural network for spatio-temporal patterns

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)


A self-organizing neural network for spatiotemporal patterns is proposed which is a nearest neighbor classifier that stores arbitrary-length spatiotemporal patterns. It has at least three layers. Layer-1 and layer-2 classify input spatial patterns one by one and the algorithm is based on the Carpenter/Grossberg net algorithm. Layer-2 and layer-3 classify and memorize the sequence of the patterns classified in layer-2. The connections between layer-2 and layer-3 are adjusted in both weight and time-delay to deal with spatiotemporal patterns. The features of the proposed neural network are its self-organization, classification, and memorization abilities for spatiotemporal patterns. In addition, it can distinguish different sequences composed of the same patterns such as 'ACT' and 'CAT' by unsupervised learning, and can deal with many sequences of different lengths.

Original languageEnglish
Title of host publicationProceedings. IJCNN - International Joint Conference on Neural Networks
Editors Anon
PublisherPubl by IEEE
Number of pages4
ISBN (Print)0780301641
Publication statusPublished - 1992 Jan 1
EventInternational Joint Conference on Neural Networks - IJCNN-91-Seattle - Seattle, WA, USA
Duration: 1991 Jul 81991 Jul 12

Publication series

NameProceedings. IJCNN - International Joint Conference on Neural Networks


OtherInternational Joint Conference on Neural Networks - IJCNN-91-Seattle
CitySeattle, WA, USA

ASJC Scopus subject areas

  • General Engineering


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