Oshima Hiraku, Odagaki Takashi
Department of Physics, Kyushu University, Fukuoka 812-8581, Japan.
Phys Rev E Stat Nonlin Soft Matter Phys. 2007 Sep;76(3 Pt 2):036114. doi: 10.1103/PhysRevE.76.036114. Epub 2007 Sep 26.
To understand the influence of structure on the function of neural networks, we study the storage capacity and the retrieval time of Hopfield-type neural networks for four network structures: regular, small world, random networks generated by the Watts-Strogatz (WS) model, and the same network as the neural network of the nematode Caenorhabditis elegans. Using computer simulations, we find that (1) as the randomness of network is increased, its storage capacity is enhanced; (2) the retrieval time of WS networks does not depend on the network structure, but the retrieval time of C. elegans's neural network is longer than that of WS networks; (3) the storage capacity of the C. elegans network is smaller than that of networks generated by the WS model, though the neural network of C. elegans is considered to be a small-world network.
为了理解结构对神经网络功能的影响,我们研究了四种网络结构的霍普菲尔德型神经网络的存储容量和检索时间:规则网络、小世界网络、由瓦茨-斯托加茨(WS)模型生成的随机网络,以及与线虫秀丽隐杆线虫神经网络相同的网络。通过计算机模拟,我们发现:(1)随着网络随机性的增加,其存储容量增强;(2)WS网络的检索时间不依赖于网络结构,但秀丽隐杆线虫神经网络的检索时间比WS网络长;(3)尽管秀丽隐杆线虫的神经网络被认为是小世界网络,但其存储容量小于由WS模型生成的网络。