Dynamics of an auto-associative neural network model with arbitrary connectivity and noise in the threshold
Dynamics of an auto-associative neural network model with arbitrary connectivity and noise in the threshold
复制标题
具有任意连接性和阈值噪声的自关联神经网络模型的动力学
DOI：
10.1088/0954-898x_2_3_005
复制
发表时间：
1991
期刊：
影响因子：
--
通讯作者：
S. Yoshizawa
中科院分区：
文献类型：
--
作者：
H. Yanai;Y. Sawada;S. Yoshizawa
用统计神经动力学方法分析了自联想神经网络模型的检索动力学。该模型具有任意指定的连通性和静态噪声被添加到阈值。该方法仅基于概率和近似计算。神经元之间的连接由赫布规则（相关型规则）的一个版本决定，其中一些被随机删除。研究表明，当阈值中不存在噪声时，网络的每连接容量是连通度的单调递减函数。当阈值内存在噪声时，存在一个最优的连接稀疏度值，使得在一定的噪声水平下，网络的容量最大。此外，与随机删除，即结构化模型，系统删除连接的影响进行了讨论。它表明，广泛的神经网络模型，如双向联想记忆网络或分层网络，是特殊情况下…
The method of statistical neurodynamics is used to analyse retrieval dynamics of an auto-associative neural network model. The model has arbitrarily specified connectivity and static noises are added to threshold values. The method is based only on probability and approximation calculations. Connections between neurons are determined by a version of the Hebb rule (correlation-type rule), and some of them are removed at random. It is shown that the capacity of the network per connection is a monotone decreasing function of connectivity if there are no noises in the threshold. When there are noises in the threshold there exists an optimal value of the sparsity of connections which yields the maximum capacity for a fixed noise level. In addition, effects of systematic removal of connections in contrast with random removal, i.e. structured models, are discussed. It is shown that a wide range of neural network models, such as a bidirectional associative memory network or a layered network, are special cases of...