Using graph theoretic measures to predict the performance of associative memory models

L. Calcraft, R.G. Adams, W. Chen, N. Davey

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    30 Downloads (Pure)

    Abstract

    We test a selection of associative memory models built with different connection strategies, exploring the relationship between the structural properties of each network and its pattern-completion performance. It is found that the Local Efficiency of the network can be used to predict pattern completion performance for associative memory models built with a range of different connection strategies. This relationship is maintained as the networks are scaled up in size, but breaks down under conditions of very sparse connectivity.
    Original languageEnglish
    Title of host publicationESANN2008: 16th European Symposium on Artificial Neural Networks
    PublisherESANN
    Pages107-112
    ISBN (Print)2-930-307080
    Publication statusPublished - 2008

    Fingerprint

    Dive into the research topics of 'Using graph theoretic measures to predict the performance of associative memory models'. Together they form a unique fingerprint.

    Cite this