Freeman W J, Kozma R, Werbos P J
Division of Neurobiology, Department of Molecular and Cell Biology, University of California at Berkeley, LSA 142, Berkeley, CA 94720-3200, USA.
Biosystems. 2001 Feb;59(2):109-23. doi: 10.1016/s0303-2647(00)00146-5.
Existing methods of complexity research are capable of describing certain specifics of bio systems over a given narrow range of parameters but often they cannot account for the initial emergence of complex biological systems, their evolution, state changes and sometimes-abrupt state transitions. Chaos tools have the potential of reaching to the essential driving mechanisms that organize matter into living substances. Our basic thesis is that while established chaos tools are useful in describing complexity in physical systems, they lack the power of grasping the essence of the complexity of life. This thesis illustrates sensory perception of vertebrates and the operation of the vertebrate brain. The study of complexity, at the level of biological systems, cannot be completed by the analytical tools, which have been developed for non-living systems. We propose a new approach to chaos research that has the potential of characterizing biological complexity. Our study is biologically motivated and solidly based in the biodynamics of higher brain function. Our biocomplexity model has the following features, (1) it is high-dimensional, but the dimensionality is not rigid, rather it changes dynamically; (2) it is not autonomous and continuously interacts and communicates with individual environments that are selected by the model from the infinitely complex world; (3) as a result, it is adaptive and modifies its internal organization in response to environmental factors by changing them to meet its own goals; (4) it is a distributed object that evolves both in space and time towards goals that is continually re-shaping in the light of cumulative experience stored in memory; (5) it is driven and stabilized by noise of internal origin through self-organizing dynamics. The resulting theory of stochastic dynamical systems is a mathematical field at the interface of dynamical system theory and stochastic differential equations. This paper outlines several possible avenues to analyze these systems. Of special interest are input-induced and noise-generated, or spontaneous state-transitions and related stability issues.
现有的复杂性研究方法能够描述生物系统在给定狭窄参数范围内的某些特性，但它们往往无法解释复杂生物系统的初始出现、其进化、状态变化以及有时突然的状态转变。混沌工具有可能触及将物质组织成生命物质的基本驱动机制。我们的基本论点是，虽然已有的混沌工具在描述物理系统的复杂性方面很有用，但它们缺乏把握生命复杂性本质的能力。本文阐述了脊椎动物的感官知觉和脊椎动物大脑的运作。在生物系统层面上的复杂性研究，无法通过为非生物系统开发的分析工具来完成。我们提出了一种新的混沌研究方法，它有可能表征生物复杂性。我们的研究具有生物学动机，并牢固地基于高等脑功能的生物动力学。我们的生物复杂性模型具有以下特征：（1）它是高维的，但维度并非固定不变，而是动态变化的；（2）它不是自主的，而是与模型从无限复杂的世界中选择的个体环境持续相互作用和交流；（3）因此，它具有适应性，并通过改变自身以响应环境因素来修改其内部组织，以实现自身目标；（4）它是一个分布式对象，在空间和时间上朝着目标演化，这个目标会根据存储在记忆中的累积经验不断重塑；（5）它通过自组织动力学由内部起源的噪声驱动并稳定下来。由此产生的随机动力系统理论是动力系统理论和随机微分方程交叉领域的一个数学分支。本文概述了分析这些系统的几种可能途径。特别值得关注的是输入诱导和噪声产生的，或自发的状态转变以及相关的稳定性问题。
