Gaussian and Exponential Architectures in Small-World Associative Memories
Calcraft, L., Adams, R.G. and Davey, N.
(2006)
Gaussian and Exponential Architectures in Small-World Associative Memories.
In:
Procs of the European Symposium on Artificial Neural Networks, ESANN'06 :.
UNSPECIFIED, BEL, pp. 617-622.
ISBN 2-930307-06-4
The performance of sparsely-connected associative memory models built from a set of perceptrons is investigated using different patterns of connectivity. Architectures based on Gaussian and exponential distributions are compared to networks created by progressively rewiring a locally-connected network. It is found that while all three architectures are capable of good pattern-completion performance, the Gaussian and exponential architectures require a significantly lower mean wiring length to achieve the same results. In the case of networks of low connectivity, relatively tight Gaussian and exponential distributions achieve the best overall performance.
Item Type | Book Section |
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Date Deposited | 15 May 2025 16:23 |
Last Modified | 30 May 2025 23:10 |
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