Transients, metastability, and neuronal dynamics
Transients, metastability, and neuronal dynamics
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DOI：
10.1006/nimg.1997.0259
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发表时间：
1997-02-01
期刊：
影响因子：
5.7
通讯作者：
Friston, KJ
中科院分区：
文献类型：
--
作者：
Friston, KJ
本文是关于神经元动力学以及如何用非线性动力学来理解它们的特殊复杂性。神经元的相互作用和连接有许多方面，它们产生了大脑动力学的复杂性。在本文中，我们考虑（i）这种复杂性的性质和（ii）它如何依赖于神经元系统之间的连接（例如，神经元群体或皮质区域）。主要结论是，模拟神经系统表现出复杂的行为，让人想起神经元动力学，当这些外在的连接是稀疏的。在这些条件下，获得的活动模式表现出丰富的形式的重复性，并具有定型的瞬态样动力学的自限性表达。尽管这些动态符合单个（复杂）吸引子的事实，但这种亚稳定性给出了动态变化吸引子流形的假象（即，动态在其上展开的变化表面）。这种亚稳定性的特征在于使用一种基于时间序列谱密度熵的度量。(C)北京：科学出版社.
This paper is about neuronal dynamics and how their special complexity can be understood in terms of nonlinear dynamics. There are many aspects of neuronal interactions and connectivity that engender the complexity of brain dynamics. In this paper we consider (i) the nature of this complexity and (ii) how it depends on connections between neuronal systems (e.g., neuronal populations or cortical areas). The main conclusion is that simulated neural systems show complex behaviors, reminiscent of neuronal dynamics, when these extrinsic connections are sparse. The patterns of activity that obtain, under these conditions, show a rich form of intermittency with the recurrent and self-limiting expression of stereotyped transient-like dynamics. Despite the fact that these dynamics conform to a single (complex) attractor this metastability gives the illusion of a dynamically changing attractor manifold (i.e., a changing surface upon which the dynamics unfold). This metastability is characterized using a measure that is based on the entropy of the time series' spectral density. (C) 1997 Academic Press.