摘要翻译：
我们导出了包含度分布对序列处理神经网络瞬态动力学影响的重叠参数的解析演化方程。在全局耦合网络的特殊情况下，得到了精确反演的临界负载比$\alpha_c=N^{-1/2}$,其中$N$是网络大小。在随机网络存在的情况下，我们的理论预测与delta度分布、二项式度分布和幂律度分布的数值实验在定量上一致。
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英文标题：
《Transient dynamics for sequence processing neural networks: effect of
  degree distributions》
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作者：
Yong Chen, Pan Zhang, Lianchun Yu, and Shengli Zhang
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最新提交年份：
2008
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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：
  We derive a analytic evolution equation for overlap parameters including the effect of degree distribution on the transient dynamics of sequence processing neural networks. In the special case of globally coupled networks, the precisely retrieved critical loading ratio $\alpha_c = N ^{-1/2}$ is obtained, where $N$ is the network size. In the presence of random networks, our theoretical predictions agree quantitatively with the numerical experiments for delta, binomial, and power-law degree distributions.
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PDF链接：
https://arxiv.org/pdf/705.3679