## 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 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 \href{b2hd-nips92}{(Schraudolph \& Sejnowski, 1993)}; the same approach could be used to improve other learning algorithms. }}