Journal
INFORMATION PROCESSING LETTERS
Volume 95, Issue 6, Pages 537-544Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.ipl.2005.05.021
Keywords
spiking associative memory; neural modeling; algorithms; information retrieval; fault tolerance
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This work concisely reviews and unifies the analysis of different variants of neural associative networks consisting of binary neurons and synapses (Willshaw model). We compute storage capacity, fault tolerance, and retrieval efficiency and point out problems of the classical Willshaw model such as limited fault tolerance and restriction to logarithmically sparse random patterns. Then we suggest possible solutions employing spiking neurons, compression of the memory structures, and additional cell layers. Finally, we discuss from a technical perspective whether distributed neural associative memories have any practical advantage over localized storage, e.g., in compressed look-up tables. (c) 2005 Elsevier B.V. All rights reserved.
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