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
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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.


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