Sigma-Pi Learning: On Radial Basis Functions and Cortical Associative Learning
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.
Cite
Text
Mel and Koch. "Sigma-Pi Learning: On Radial Basis Functions and Cortical Associative Learning." Neural Information Processing Systems, 1989.Markdown
[Mel and Koch. "Sigma-Pi Learning: On Radial Basis Functions and Cortical Associative Learning." Neural Information Processing Systems, 1989.](https://mlanthology.org/neurips/1989/mel1989neurips-sigmapi/)BibTeX
@inproceedings{mel1989neurips-sigmapi,
title = {{Sigma-Pi Learning: On Radial Basis Functions and Cortical Associative Learning}},
author = {Mel, Bartlett W. and Koch, Christof},
booktitle = {Neural Information Processing Systems},
year = {1989},
pages = {474-481},
url = {https://mlanthology.org/neurips/1989/mel1989neurips-sigmapi/}
}