Transient dynamics of sparsely connected Hopfield neural networks with arbitrary degree distributions

Pan Zhang and Yong Chen

Physica A: Statistical Mechanics and its Applications, 2008, vol. 387, issue 4, 1009-1015

Abstract: Using probabilistic approach, the transient dynamics of sparsely connected Hopfield neural networks is studied for arbitrary degree distributions. A recursive scheme is developed to determine the time evolution of overlap parameters. As illustrative examples, the explicit calculations of dynamics for networks with binomial, power-law, and uniform degree distribution are performed. The results are good agreement with the extensive numerical simulations. It indicates that with the same average degree, there is a gradual improvement of network performance with increasing sharpness of its degree distribution, and the most efficient degree distribution for global storage of patterns is the delta function.

Keywords: Neural networks; Complex networks; Degree distribution; 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:4:p:1009-1015

DOI: 10.1016/j.physa.2007.09.047

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