Title :
GST networks: learning emergent spatiotemporal correlations
Author :
Tumuluri, Chaitanya ; Mohan, Chilukuri K. ; Choudhary, Alok N.
Author_Institution :
Syracuse Univ., NY, USA
Abstract :
This paper presents two novel networks-the growing cell structure instantaneous spatio-temporal (GIST) network, and growing cell structure epochal spatio-temporal (GEST) network-which combine unsupervised feature-extraction and Hebbian learning, for tracking emergent correlations in the evolution of spatio-temporal distributions. The networks were successfully tested on the challenging data mapping problem, using an execution driven simulation of their implementation in hardware
Keywords :
Hebbian learning; correlation methods; data handling; feature extraction; self-organising feature maps; unsupervised learning; GCS epochal spatiotemporal network; GCS instantaneous spatiotemporal network; GEST network; GIST network; Hebbian learning; data mapping; emergent spatiotemporal correlations; feature-extraction; growing cell structure network; mapping dynamics; unsupervised learning; Character generation; Data mining; Feature extraction; Hardware; Hebbian theory; Neurons; Production; Quantization; Spatiotemporal phenomena; Testing;
Conference_Titel :
Neural Networks, 1996., IEEE International Conference on
Conference_Location :
Washington, DC
Print_ISBN :
0-7803-3210-5
DOI :
10.1109/ICNN.1996.549148
