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• W2035606737 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." @default.
• W2035606737 created "2016-06-24" @default.
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• W2035606737 date "2008-02-01" @default.
• W2035606737 modified "2023-10-17" @default.
• W2035606737 title "Transient dynamics of sparsely connected Hopfield neural networks with arbitrary degree distributions" @default.
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• W2035606737 doi "https://doi.org/10.1016/j.physa.2007.09.047" @default.
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