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理解脑电图神经动力学的非线性视角。

A nonlinear perspective in understanding the neurodynamics of EEG.

作者信息

Pradhan N, Dutt D N

机构信息

Department of Psychopharmacology, National Institute of Mental Health & Neurosciences, Bangalore, India.

出版信息

Comput Biol Med. 1993 Nov;23(6):425-42. doi: 10.1016/0010-4825(93)90091-e.

Abstract

The developments in nonlinear dynamics and the theory of chaos have considerably altered our perception and analysis of many complex systems, including the brain. This paper reviews the physical and dynamical aspect of brain's electrical activity from this new perspective and indicates possible future directions. The importance of emerging trends of nonlinear dynamics and chaos to neurobiology has been discussed in the context of various states of consciousness and behaviour. In the past, EEG analysis has been confined to descriptive stochastic statistics and any understanding of the transitional process of brain activities was either nonexistent or not amenable for investigation. With the developments in nonlinear dynamics, the chaotic dynamical parameters and trajectory behaviour will find their use as feature detection techniques in EEG. Furthermore, nonlinear dynamics provides a model for EEG generation and temporal prediction which will help in determining the nature of neuronal processes governing various states of brain activity. The formalism of globally coupled dynamic systems will find applications in modelling the transitional states of EEG.

摘要

非线性动力学和混沌理论的发展极大地改变了我们对包括大脑在内的许多复杂系统的认知和分析。本文从这一新视角回顾了大脑电活动的物理和动力学方面,并指出了未来可能的发展方向。非线性动力学和混沌的新兴趋势对神经生物学的重要性已在各种意识和行为状态的背景下进行了讨论。过去,脑电图(EEG)分析局限于描述性随机统计,对大脑活动过渡过程的任何理解要么不存在,要么难以进行研究。随着非线性动力学的发展,混沌动力学参数和轨迹行为将作为脑电图中的特征检测技术得到应用。此外,非线性动力学为脑电图生成和时间预测提供了一个模型,这将有助于确定控制大脑活动各种状态的神经元过程的性质。全局耦合动态系统的形式主义将在脑电图过渡状态建模中得到应用。

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