High Capacity Associative Memories and Small World Networks

N. Davey; B. Christianson; R.G. Adams

High Capacity Associative Memories and Small World Networks

N. Davey, B. Christianson, R.G. Adams

Centre for Computer Science and Informatics Research

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

14 Citations (Scopus)

30 Downloads (Pure)

Abstract

Models of associative memory usually have full connectivity or if diluted, random symmetric connectivity. In contrast biological neural systems have predominantly local, non-symmetric connectivity. Here we investigate sparse networks of threshold units, trained with the perception learning rule. The units are arranged in a small world network, with short path-lengths but cliquish connectivity. The connectivity may be symmetric or non-symmetric. The results show that the small-world networks with non-symmetric weights perform well as associative memories. It is also shown that in highly dilute networks with random connectivity, it is symmetry of the weights, rather than symmetry of the connectivity matrix, that causes poor performance.

Original language	English
Title of host publication	. Proceedings of the IEEE International Joint Conference on Neural Networks IJCNN04 1
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	177-182
Publication status	Published - Jul 2004

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@inproceedings{e33d85c5040e42a8873d7f31af5d8e2c,

title = "High Capacity Associative Memories and Small World Networks",

abstract = "Models of associative memory usually have full connectivity or if diluted, random symmetric connectivity. In contrast biological neural systems have predominantly local, non-symmetric connectivity. Here we investigate sparse networks of threshold units, trained with the perception learning rule. The units are arranged in a small world network, with short path-lengths but cliquish connectivity. The connectivity may be symmetric or non-symmetric. The results show that the small-world networks with non-symmetric weights perform well as associative memories. It is also shown that in highly dilute networks with random connectivity, it is symmetry of the weights, rather than symmetry of the connectivity matrix, that causes poor performance.",

author = "N. Davey and B. Christianson and R.G. Adams",

note = "Copyright IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.",

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T1 - High Capacity Associative Memories and Small World Networks

AU - Davey, N.

AU - Christianson, B.

AU - Adams, R.G.

N1 - Copyright IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

PY - 2004/7

Y1 - 2004/7

N2 - Models of associative memory usually have full connectivity or if diluted, random symmetric connectivity. In contrast biological neural systems have predominantly local, non-symmetric connectivity. Here we investigate sparse networks of threshold units, trained with the perception learning rule. The units are arranged in a small world network, with short path-lengths but cliquish connectivity. The connectivity may be symmetric or non-symmetric. The results show that the small-world networks with non-symmetric weights perform well as associative memories. It is also shown that in highly dilute networks with random connectivity, it is symmetry of the weights, rather than symmetry of the connectivity matrix, that causes poor performance.

AB - Models of associative memory usually have full connectivity or if diluted, random symmetric connectivity. In contrast biological neural systems have predominantly local, non-symmetric connectivity. Here we investigate sparse networks of threshold units, trained with the perception learning rule. The units are arranged in a small world network, with short path-lengths but cliquish connectivity. The connectivity may be symmetric or non-symmetric. The results show that the small-world networks with non-symmetric weights perform well as associative memories. It is also shown that in highly dilute networks with random connectivity, it is symmetry of the weights, rather than symmetry of the connectivity matrix, that causes poor performance.

M3 - Conference contribution

SP - 177

EP - 182

BT - . Proceedings of the IEEE International Joint Conference on Neural Networks IJCNN04 1

PB - Institute of Electrical and Electronics Engineers (IEEE)

ER -

High Capacity Associative Memories and Small World Networks

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