DocumentCode
892653
Title
Information capacity of associative memories
Author
Kuh, Anthony ; Dickinson, Bradley W.
Author_Institution
Dept. of Electr. Eng., Hawaii Univ., Honolulu, HI, USA
Volume
35
Issue
1
fYear
1989
fDate
1/1/1989 12:00:00 AM
Firstpage
59
Lastpage
68
Abstract
Associative memory networks consisting of highly interconnected binary-valued cells have been used to model neural networks. Tight asymptotic bounds have been found for the information capacity of these networks. The authors derive the asymptotic information capacity of these networks using results from normal approximation theory and theorems about exchangeable random variables
Keywords
content-addressable storage; information theory; neural nets; approximation theory; associative memories; binary associative memory network; exchangeable random variables; highly interconnected binary-valued cells; information capacity; neural networks; tight asymptotic bounds; Approximation methods; Associative memory; Biological neural networks; Brain modeling; Hamming distance; Probes; Random variables; Stability;
fLanguage
English
Journal_Title
Information Theory, IEEE Transactions on
Publisher
ieee
ISSN
0018-9448
Type
jour
DOI
10.1109/18.42177
Filename
42177
Link To Document