Plasticity-Mediated Competitive Learning
Abstract
Differentiation between the nodes of a competitive learning net(cid:173) work is conventionally achieved through competition on the ba(cid:173) sis of neural activity. Simple inhibitory mechanisms are limited to sparse representations, while decorrelation and factorization schemes that support distributed representations are computation(cid:173) ally unattractive. By letting neural plasticity mediate the compet(cid:173) itive interaction instead, we obtain diffuse, nonadaptive alterna(cid:173) tives for fully distributed representations. We use this technique to Simplify and improve our binary information gain optimiza(cid:173) tion algorithm for feature extraction (Schraudolph and Sejnowski, 1993); the same approach could be used to improve other learning algorithms.
Cite
Text
Schraudolph and Sejnowski. "Plasticity-Mediated Competitive Learning." Neural Information Processing Systems, 1994.Markdown
[Schraudolph and Sejnowski. "Plasticity-Mediated Competitive Learning." Neural Information Processing Systems, 1994.](https://mlanthology.org/neurips/1994/schraudolph1994neurips-plasticitymediated/)BibTeX
@inproceedings{schraudolph1994neurips-plasticitymediated,
title = {{Plasticity-Mediated Competitive Learning}},
author = {Schraudolph, Nicol N. and Sejnowski, Terrence J.},
booktitle = {Neural Information Processing Systems},
year = {1994},
pages = {475-480},
url = {https://mlanthology.org/neurips/1994/schraudolph1994neurips-plasticitymediated/}
}