DYNAMIC PATTERN GENERATION IN BEHAVIORAL AND NEURAL SYSTEMS
DYNAMIC PATTERN GENERATION IN BEHAVIORAL AND NEURAL SYSTEMS
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DOI：
10.1126/science.3281253
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发表时间：
1988-03-25
期刊：
影响因子：
56.9
通讯作者：
KELSO, JAS
中科院分区：
文献类型：
--
作者：
SCHONER, G;KELSO, JAS
在探索复杂生物系统中模式生成原理的过程中，提出了一种理论和实验相结合的可操作性方法。非平衡系统中自组织的核心数学概念(包括序参数动力学、稳定性、涨落和时间尺度)被用来展示如何将大量经验观察到的时间模式的特征映射到简单的低维(随机的、非线性的)动力学规律，这些规律可以从较低的描述水平推导出来。该理论框架提供了一种语言和一种策略，并伴随着新的观察数据，可以在几个分析尺度上(包括行为模式、神经网络和单个神经元)理解动态模式以及它们之间的联系。
In the search for principles of pattern generation in complex biological systems, an operational approach is presented that embraces both theory and experiment. The central mathematical concepts of self-organization in nonequilibrium systems (including order parameter dynamics, stability, fluctuations, and time scales) are used to show how a large number of empirically observed features of temporal patterns can be mapped onto simple low-dimensional (stochastic, nonlinear) dynamical laws that are derivable from lower levels of description. The theoretical framework provides a language and a strategy, accompanied by new observables, that may afford an understanding of dynamic patterns at several scales of analysis (including behavioral patterns, neural networks, and individual neurons) and the linkage among them.