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        Clustering predicts memory performance in networks of spiking and non-spiking neurons

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        Author
        Chen, W.
        Maex, R.
        Adams, R.G.
        Steuber, Volker
        Calcraft, L.
        Davey, N.
        Attention
        2299/9606
        Abstract
        The problem we address in this paper is that of finding effective and parsimonious patterns of connectivity in sparse associative memories. This problem must be addressed in real neuronal systems, so that results in artificial systems could throw light on real systems. We show that there are efficient patterns of connectivity and that these patterns are effective in models with either spiking or non-spiking neurons. This suggests that there may be some underlying general principles governing good connectivity in such networks. We also show that the clustering of the network, measured by Clustering Coefficient, has a strong negative linear correlation to the performance of associative memory. This result is important since a purely static measure of network connectivity appears to determine an important dynamic property of the network.
        Publication date
        2011
        Published in
        Frontiers in Computational Neuroscience
        Published version
        https://doi.org/10.3389/fncom.2011.00014
        Other links
        http://hdl.handle.net/2299/9606
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        School of Computer Science
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