Topology and dynamics of attractor neural networks: The role of loopiness

Pan Zhang and Yong Chen

Physica A: Statistical Mechanics and its Applications, 2008, vol. 387, issue 16, 4411-4416

Abstract: We derive an exact representation of the topological effect on the dynamics of sequence processing neural networks within signal-to-noise analysis. A new network structure parameter, loopiness coefficient, is introduced to quantitatively study the loop effect on network dynamics. A large loopiness coefficient means a high probability of finding loops in the networks. We develop recursive equations for the overlap parameters of neural networks in terms of their loopiness. It was found that a large loopiness increases the correlation among the network states at different times and eventually reduces the performance of neural networks. The theory is applied to several network topological structures, including fully-connected, densely-connected random, densely-connected regular and densely-connected small-world, where encouraging results are obtained.

Keywords: Complex networks; Neural networks; Loopiness; Probability theory (search for similar items in EconPapers)
Date: 2008
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:387:y:2008:i:16:p:4411-4416

DOI: 10.1016/j.physa.2008.02.073

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