A self-organizing map with homeostatic synaptic scaling

Authors:
Thomas J. Sullivan;Virginia R. de Sa
Affiliations:
Department of Electrical and Computer Engineering, University of California, San Diego, USA;Department of Cognitive Science, University of California, San Diego, USA
Venue:
Neurocomputing
Year:
2006

Citing 2
Cited 2

Topographic receptive fields and patterned lateral interaction in a self-organizing model of the primary visual cortex

Neural Computation
Self-Organizing Maps

Self-Organizing Maps

Homeostatic synaptic scaling in self-organizing maps

Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
Hebbian plasticity and homeostasis in a model of hypercolumn of the visual cortex

Neural Computation

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Abstract

Hebbian learning has been a staple of neural-network models for many years. It is well known that the most straight-forward implementations of this popular learning rule lead to unconstrained weight growth. A newly discovered property of cortical neurons is that they try to maintain a preset average firing rate [G.G. Turrigiano, S.B. Nelson, Homeostatic plasticity in the developing nervous system, Nat. Rev. Neurosci. 5 (2004) 97-107]. We use this property to control the Hebbian learning process in a self-organizing map network. In this article, the practicality of this type of learning rule is expanded by deriving a scaling equation for the learning rates for various network architectures.