任意度分布稀疏连接Hopfield神经网络的瞬态动力学
Transient Dynamics of Sparsely Connected Hopfield Neural Networks with Arbitrary Degree Distributions
摘要：
利用概率方法研究了任意度分布的稀疏连接Hopfield神经网络的瞬态动力学。一个递归的计划，以确定重叠参数的时间演变。作为说明性的例子，网络的动力学二项式，幂律和均匀度分布的显式计算进行。计算结果与大量的数值模拟结果吻合较好。研究表明，在平均度相同的情况下，网络性能随着度分布的尖锐度的增加而逐渐提高，而度分布对模式的全局存储最为有效.
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
