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 language | English (US) |
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| Title of host publication | IEEE International Conference on Neural Networks - Conference Proceedings |
| Publisher | IEEE Computer Society |
| Pages | 1652-1657 |
| Number of pages | 6 |
| Volume | 3 |
| State | Published - 1996 |
| Event | Proceedings of the 1996 IEEE International Conference on Neural Networks, ICNN. Part 1 (of 4) - Washington, DC, USA Duration: Jun 3 1996 → Jun 6 1996 |
Other
| Other | Proceedings of the 1996 IEEE International Conference on Neural Networks, ICNN. Part 1 (of 4) |
|---|---|
| City | Washington, DC, USA |
| Period | 6/3/96 → 6/6/96 |
ASJC Scopus subject areas
- Software