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 here, 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
19890041660
Acquisition Source
Legacy CDMS
Document Type
Conference Paper
Authors
Keeler, James D. (NASA Ames Research Center Moffett Field; Stanford University, CA, United States)
Date Acquired
August 14, 2013
Publication Date
January 1, 1988
Subject Category
Cybernetics
Meeting Information
Meeting: IEEE Conference on Neural Information Processing Systems