CHAOS IN RANDOM NEURAL NETWORKS
CHAOS IN RANDOM NEURAL NETWORKS
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DOI：
10.1103/physrevlett.61.259
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发表时间：
1988-07-18
影响因子：
8.6
通讯作者：
SOMMERS, HJ
中科院分区：
文献类型：
--
作者：
SOMPOLINSKY, H;CRISANTI, A;SOMMERS, HJ
研究了N个非线性元件通过随机非对称耦合相互作用的网络的连续时间动力学模型。自洽平均场理论，在N→∞极限下精确，预测在增益参数的临界值处发生从稳定相到混沌相的转变。计算了混沌流的自相关函数和最大李雅普诺夫指数。
A continuous-time dynamic model of a network of N nonlinear elements interacting via random asymmetric couplings is studied. A self-consistent mean-field theory, exact in the N→∞ limit, predicts a transition from a stationary phase to a chaotic phase occurring at a critical value of the gain parameter. The autocorrelations of the chaotic flow as well as the maximal Lyapunov exponent are calculated.