Top-Down Control of Learning in Biological Self-Organizing Maps

Authors:
Thomas Trappenberg;Pitoyo Hartono;Douglas Rasmusson
Affiliations:
Dalhousie Neuroscience Institute, Halifax, Canada and Faculty of Computer Science, Dalhousie University, Halifax, Canada;Future University, Hakodate, Japan;Dalhousie Neuroscience Institute, Halifax, Canada and Department of Physiology and Biophysics, Dalhousie University, Halifax, Canada
Venue:
WSOM '09 Proceedings of the 7th International Workshop on Advances in Self-Organizing Maps
Year:
2009

Citing 2
Cited 1

Multilayer feedforward networks are universal approximators

Neural Networks
Population Coding with Correlation and an Unfaithful Model

Neural Computation

Self-Organizing Map Formation with a Selectively Refractory Neighborhood

Neural Processing Letters

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper discusses biological aspects of self-organising maps (SOMs) which includes a brief review of neurophysiological findings and classical models of neurophysiological SOMs. We then discuss some simulation studies on the role of topographic map representation for training mapping networks and on top-down control of map plasticity.