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Original languageEnglish
Article number14
JournalFrontiers in Computational Neuroscience
Journal publication date2011
Volume5
DOIs
Publication statusPublished - 2011

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.

Notes

Original article can be found at : http://www.frontiersin.org/ "This Document is Protected by copyright and was first published by Frontiers. All rights reserved. It is reproduced with permission." "Copyright : 2011 Chen, Maex, Adams, Steuber, Calcraft and Davey. This is an open-access article subject to a non-exclusive license between the authors and Frontiers Media SA, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and other Frontiers conditions are complied with."

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