DocumentCode
943138
Title
The capacity of the Hopfield associative memory
Author
Mceliece, Robert J. ; Posner, Edward C. ; Rodemich, Eugene R. ; Venkatesh, Santosh S.
Volume
33
Issue
4
fYear
1987
fDate
7/1/1987 12:00:00 AM
Firstpage
461
Lastpage
482
Abstract
Techniques from coding theory are applied to study rigorously the capacity of the Hopfield associative memory. Such a memory stores
-tuple of
\´s. The components change depending on a hard-limited version of linear functions of all other components. With symmetric connections between components, a stable state is ultimately reached. By building up the connection matrix as a sum-of-outer products of
fundamental memories, one hopes to be able to recover a certain one of the
memories by using an initial
-tuple probe vector less than a Hamming distance
away from the fundamental memory. If
fundamental memories are chosen at random, the maximum asympotic value of
in order that most of the
original memories are exactly recoverable is
. With the added restriction that every one of the
fundamental memories be recoverable exactly,
can be no more than
asymptotically as
approaches infinity. Extensions are also considered, in particular to capacity under quantization of the outer-product connection matrix. This quantized memory capacity problem is closely related to the capacity of the quantized Gaussian channel.
-tuple of
\´s. The components change depending on a hard-limited version of linear functions of all other components. With symmetric connections between components, a stable state is ultimately reached. By building up the connection matrix as a sum-of-outer products of
fundamental memories, one hopes to be able to recover a certain one of the
memories by using an initial
-tuple probe vector less than a Hamming distance
away from the fundamental memory. If
fundamental memories are chosen at random, the maximum asympotic value of
in order that most of the
original memories are exactly recoverable is
. With the added restriction that every one of the
fundamental memories be recoverable exactly,
can be no more than
asymptotically as
approaches infinity. Extensions are also considered, in particular to capacity under quantization of the outer-product connection matrix. This quantized memory capacity problem is closely related to the capacity of the quantized Gaussian channel.Keywords
Associative memories; Coding/decoding; Neural networks; Associative memory; Biological system modeling; Cellular neural networks; Codes; Hopfield neural networks; Neural networks; Neurons; Probes; Propulsion; Space technology;
fLanguage
English
Journal_Title
Information Theory, IEEE Transactions on
Publisher
ieee
ISSN
0018-9448
Type
jour
DOI
10.1109/TIT.1987.1057328
Filename
1057328
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