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摘要翻译：
利用概率方法研究了任意次分布下稀疏连通Hopfield神经网络的瞬态动力学。发展了一个递归格式来确定重叠参数的时间演化。作为例子，对具有二项式、幂律和均匀度分布的网络进行了动力学显式计算。计算结果与大量数值模拟结果吻合较好。结果表明，在平均度相同的情况下，网络性能随着度分布的增加而逐渐提高，对于模式全局存储最有效的度分布是delta函数。
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英文标题：
《Transient Dynamics of Sparsely Connected Hopfield Neural Networks with
Arbitrary Degree Distributions》
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作者：
Pan Zhang and Yong Chen
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最新提交年份：
2007
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分类信息：
一级分类：Physics 物理学
二级分类：Disordered Systems and Neural Networks 无序系统与神经网络
分类描述：Glasses and spin glasses; properties of random, aperiodic and quasiperiodic systems; transport in disordered media; localization; phenomena mediated by defects and disorder; neural networks
眼镜和旋转眼镜；随机、非周期和准周期系统的性质；无序介质中的传输；本地化；由缺陷和无序介导的现象；神经网络
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一级分类：Physics 物理学
二级分类：Statistical Mechanics 统计力学
分类描述：Phase transitions, thermodynamics, field theory, non-equilibrium phenomena, renormalization group and scaling, integrable models, turbulence
相变，热力学，场论，非平衡现象，重整化群和标度，可积模型，湍流
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英文摘要：
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.
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PDF链接：
https://arxiv.org/pdf/704.1007
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