A hybrid dynamical system as an automaton on the fractal set

J. Nishikawa, K. Gohara
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引用次数: 0

Abstract

It is well known that a brain consists of many sub-modules, each of which performs a specific role. The experimental results many studies have indicated that prefrontal cortex appropriately switches each module. In this paper, we consider the brain as a hybrid dynamical system, which is composed of a higher module having discrete dynamics and a lower module having continuous dynamics. Two typical systems are investigated from the viewpoint of dynamical systems. When the higher module stochastically switches inputs to the lower module, i.e., a non-feedback system, the dynamics is characterizes by an attractive and invariant fractal set having hierarchical clusters addressed by input sequences. When the higher module switches according to the state of the lower module, i.e., a feedback system, various switching attractors correspond to infinite switching manifolds, which define each feedback control rule at the switching point. The switching attractors in the feedback system are the subsets of the fractal set in the non-feedback system. The system can be considered an automaton, which generated various sequences from the fractal, set by choosing the typical switching manifold.
分形集上作为自动机的混合动力系统
众所周知,大脑由许多子模块组成,每个子模块都扮演着特定的角色。许多研究的实验结果表明,前额皮质适当地切换每个模块。本文认为大脑是一个混合动力系统,它由具有离散动力学的高级模块和具有连续动力学的低级模块组成。从动力系统的角度研究了两个典型的系统。当较高的模块随机切换输入到较低的模块,即,一个非反馈系统,其动力学特征是一个有吸引力的和不变的分形集,具有由输入序列寻址的分层簇。当上模根据下模的状态切换时,即反馈系统,各种切换吸引子对应于无限个切换流形,流形定义了切换点处的每个反馈控制规则。反馈系统中的开关吸引子是非反馈系统中分形集的子集。该系统可以看作是一个自动机,通过选择典型的开关流形,从分形集合中生成各种序列。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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CiteScore
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