## Plasticity-Mediated Competitive Learning

N. N. Schraudolph and T. J. Sejnowski. Plasticity-Mediated Competitive Learning. In Advances in Neural Information Processing Systems (NIPS), pp. 475–480, The MIT Press, Cambridge, MA, 1995.
In Ph.D. thesis

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### Abstract

Differentiation between the nodes of a competitive learning network is conventionally achieved through competition on the basis of neural activity. Simple inhibitory mechanisms are limited to sparse representations, while decorrelation and factorization schemes that support distributed representations are computationally unattractive. By letting neural plasticity mediate the competitive interaction instead, we obtain diffuse, nonadaptive alternatives for fully distributed representations. We use this technique to simplify and improve our binary information gain optimization algorithm for feature extraction (Schraudolph & Sejnowski, 1993); the same approach could be used to improve other learning algorithms.

### BibTeX Entry

@inproceedings{SchSej95,
author = {Nicol N. Schraudolph and Terrence J. Sejnowski},
title = {\href{http://nic.schraudolph.org/pubs/SchSej95.pdf}{
Plasticity-Mediated Competitive Learning}},
pages = {475--480},
editor = {Gerald Tesauro and David S. Touretzky and Todd K. Leen},
booktitle =  nips,
publisher = {The {MIT} Press, Cambridge, MA},
volume =  7,
year =  1995,
b2h_type = {Top Conferences},
b2h_topic = {>Competitive Learning},
b2h_note = {In <a href="b2hd-Schraudolph95">Ph.D. thesis</a>},
abstract = {
Differentiation between the nodes of a competitive learning network is
conventionally achieved through competition on the basis of neural
activity.  Simple inhibitory mechanisms are limited to sparse
representations, while decorrelation and factorization schemes that
support distributed representations are computationally unattractive.
By letting neural {\em plasticity}\/ mediate the competitive interaction
representations.  We use this technique to simplify and improve our binary
information gain optimization algorithm for feature extraction
\href{b2hd-nips92}{(Schraudolph \& Sejnowski, 1993)}; the same
approach could be used to improve other learning algorithms.
}}


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