IEICE Trans - Shift-Invariant Associative Memory Based on Homogeneous Neural Networks

Shift-Invariant Associative Memory Based on Homogeneous Neural Networks

Hiromi MIYAJIMA
Noritaka SHIGEI
Shuji YATSUKI

Publication
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E88-A    No.10    pp.2600-2606
Publication Date: 2005/10/01
Online ISSN: 
DOI: 10.1093/ietfec/e88-a.10.2600
Print ISSN: 0916-8508
Type of Manuscript: Special Section PAPER (Special Section on Nonlinear Theory and its Applications)
Category: 
Keyword: 
associative memory,  shift-invariant,  homogeneous neural networks,  memory capacity,  

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Summary: 
This paper proposes homogeneous neural networks (HNNs), in which each neuron has identical weights. HNNs can realize shift-invariant associative memory, that is, HNNs can associate not only a memorized pattern but also its shifted ones. The transition property of HNNs is analyzed by the statistical method. We show the probability that each neuron outputs correctly and the error-correcting ability. Further, we show that HNNs cannot memorize over the number,, of patterns, where m is the number of neurons and k is the order of connections.

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