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Capacity for patterns and sequences in Kanerva's SDM as compared to other associative memory modelsThe information capacity of Kanerva's Sparse Distributed Memory (SDM) and Hopfield-type neural networks is investigated. Under the approximations used, it is shown that the total information stored in these systems is proportional to the number connections in the network. The proportionality constant is the same for the SDM and Hopfield-type models independent of the particular model, or the order of the model. The approximations are checked numerically. This same analysis can be used to show that the SDM can store sequences of spatiotemporal patterns, and the addition of time-delayed connections allows the retrieval of context dependent temporal patterns. A minor modification of the SDM can be used to store correlated patterns.
Document ID
19900017206
Acquisition Source
Legacy CDMS
Document Type
Contractor Report (CR)
Authors
Keeler, James D.
(Stanford Univ. CA., United States)
Date Acquired
September 6, 2013
Publication Date
December 1, 1987
Subject Category
Computer Programming And Software
Report/Patent Number
NAS 1.26:180701
NASA-CR-180701
RIACS-TR-87.29
Accession Number
90N26522
Funding Number(s)
CONTRACT_GRANT: NCC2-408
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.
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