Sparse Coding in Sparse Winner Networks

Authors:
Janusz A. Starzyk;Yinyin Liu;David Vogel
Affiliations:
School of Electrical Engineering & Computer Science, Ohio University, Athens, OH 45701,;School of Electrical Engineering & Computer Science, Ohio University, Athens, OH 45701,;Ross University School of Medicine, Commonwealth of Dominica,
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
Year:
2007

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Abstract

This paper investigates a mechanism for reliable generation of sparse code in a sparsely connected, hierarchical, learning memory. Activity reduction is accomplished with local competitions that suppress activities of unselected neurons so that costly global competition is avoided. The learning ability and the memory characteristics of the proposed winner-take-all network and an oligarchy-take-all network are demonstrated using experimental results. The proposed models have the features of a learning memory essential to the development of machine intelligence.