GST networks: learning emergent spatiotemporal correlations

Chaitanya Tumuluri, Chilukuri K Mohan, Alok N. Choudhary

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

Abstract

This paper presents two novel (GIST and GEST) networks, which combine unsupervised feature-extraction and Hebbian learning, for tracking emergent correlations in the evolution of spatiotemporal distributions. The networks were successfully tested on the challenging Data Mapping problem, using an execution driven simulation of their implementation in hardware.

Original languageEnglish (US)
Title of host publicationIEEE International Conference on Neural Networks - Conference Proceedings
PublisherIEEE Computer Society
Pages1652-1657
Number of pages6
Volume3
StatePublished - 1996
EventProceedings of the 1996 IEEE International Conference on Neural Networks, ICNN. Part 1 (of 4) - Washington, DC, USA
Duration: Jun 3 1996Jun 6 1996

Other

OtherProceedings of the 1996 IEEE International Conference on Neural Networks, ICNN. Part 1 (of 4)
CityWashington, DC, USA
Period6/3/966/6/96

ASJC Scopus subject areas

  • Software

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  • Cite this

    Tumuluri, C., Mohan, C. K., & Choudhary, A. N. (1996). GST networks: learning emergent spatiotemporal correlations. In IEEE International Conference on Neural Networks - Conference Proceedings (Vol. 3, pp. 1652-1657). IEEE Computer Society.