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        Gaussian and Exponential Architectures in Small-World Associative Memories

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        Final Accepted Version (PDF, 384Kb)
        Author
        Calcraft, L.
        Adams, R.G.
        Davey, N.
        Attention
        2299/6803
        Abstract
        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.
        Publication date
        2006
        Published in
        Procs of the European Symposium on Artificial Neural Networks, ESANN'06
        Other links
        http://hdl.handle.net/2299/6803
        Relations
        School of Computer Science
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