Published November 1989
| Published
Book Section - Chapter
Open
Sigma-Pi Learning: On Radial Basis Functions and Cortical Associative Learning
- Creators
- Mel, Bartlett W.
- Koch, Christof
- Other:
- Touretzky, David S.
Abstract
The goal in this work has been to identify the neuronal elements of the cortical column that are most likely to support the learning of nonlinear associative maps. We show that a particular style of network learning algorithm based on locally-tuned receptive fields maps naturally onto cortical hardware, and gives coherence to a variety of features of cortical anatomy, physiology, and biophysics whose relations to learning remain poorly understood.
Additional Information
Thanks are due to Ojvind Bernander, Rodney Douglas, Richard Durbin, Kamil Grajski, David Mackay, and John Moody for numerous helpful discussions. We acknowledge support from the Office of Naval Research, the James S. McDonnell Foundation, and the Del Webb Foundation.Attached Files
Published - NIPS-1989-sigma-pi-learning-on-radial-basis-functions-and-cortical-associative-learning-Paper.pdf
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NIPS-1989-sigma-pi-learning-on-radial-basis-functions-and-cortical-associative-learning-Paper.pdf
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Additional details
- Eprint ID
- 121013
- Resolver ID
- CaltechAUTHORS:20230419-191821438
- Office of Naval Research (ONR)
- James S. McDonnell Foundation
- Del Webb Foundation
- Created
-
2023-04-19Created from EPrint's datestamp field
- Updated
-
2023-04-19Created from EPrint's last_modified field
- Caltech groups
- Koch Laboratory (KLAB)