单整流线性类神经元单元的最小混沌网络:三个原型

Y. Horikawa
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引用次数: 0

摘要

在一个非自治系统和两个自治系统的三种原型模型上研究了线性类神经元单元网络中单次整流引起的混沌振荡。第一种是具有周期性输入的耦合神经元系统;第二种是一个由三个耦合神经元组成的系统,有六个耦合;第三种是一个由四个单向耦合神经元和一个反向耦合神经元组成的环。在每个系统中,一个神经元的输出函数为斜坡,其他神经元的输出函数为线性。每个系统都是分段线性的,相空间被一个边界分隔成两个域。严格推导了稳态、周期解和同斜轨道,并用雅可比矩阵的特征值评价了它们的稳定性。三种系统的分岔分析表明,混沌吸引子可以通过周期解的倍周期分岔级联产生。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Minimal Chaotic Networks of Linear Neuron-Like Elements with Single Rectification: Three Prototypes
Chaotic oscillations induced by single rectification in networks of linear neuron-like elements are examined on three prototype models: one nonautonomous system and two autonomous systems. The first is a system of coupled neurons with periodic input; the second is a system of three coupled neurons with six couplings; the third is a ring of four unidirectionally coupled neurons with one reverse coupling. In each system, the output function of one neuron is ramp and that of the others is linear. Each system is piecewise linear and the phase space is separated into two domains by a single border. Steady states, periodic solutions and homoclinic orbits are derived rigorously and their stability is evaluated with the eigenvalues of the Jacobian matrices. The bifurcation analysis of the three systems shows that chaotic attractors could be generated through cascades of period-doubling bifurcations of periodic solutions.
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