Improved storage capacity in correlation matrix memories storing fixed weight codes

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In this paper we introduce an improved binary correlation matrix memory (CMM) with better storage capacity when storing sparse fixed weight codes generated with the algorithm of Baum et al. We outline associative memory, and describe the binary correlation matrix memory- a specific example of a distributed associative memory. The importance of the representation used in a CMM for input and output codes is discussed, with specific regard to sparse fixed weight codes. We present an algorithm for generating of fixed weight codes, originally given by Baum et al. The properties of this algorithm are briefly discussed, including possible thresholding functions which could be used when storing these codes in a CMM; L-max and L-wta. Finally, results generated from a series of simulations are used to demonstrate that the use of L-wta as a thresholding function provides an increase in storage capacity over L-max.
Original languageEnglish
Title of host publicationLecture Notes in Computer Science
Place of PublicationICANN 2009
Number of pages9
EditionPART 1
ISBN (Print)978-3-642-04273-7
Publication statusPublished - 16 Sept 2009

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


  • associative memory, correlation matrix memory, storage capacity, fixed weight codes, pattern recognition

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