Sigma-Pi Learning: On Radial Basis Functions and Cortical Associative Learning

Part of Advances in Neural Information Processing Systems 2 (NIPS 1989)

Bibtex Metadata Paper

Authors

Bartlett Mel, Christof Koch

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.