The labile brain. I. Neuronal transients and nonlinear coupling
The labile brain. I. Neuronal transients and nonlinear coupling
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DOI：
10.1098/rstb.2000.0560
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发表时间：
2000-02-29
影响因子：
6.3
通讯作者：
Friston, KJ
中科院分区：
文献类型：
--
作者：
Friston, KJ
在此，三篇论文的第一篇，性质和动机；描述神经元瞬态与表征脑动力学的关系。本文讨论了神经元动力学、相互作用、耦合和隐式神经元编码的一些基本问题。第二篇论文在动态不稳定性和复杂性的背景下发展了神经元瞬态和非线性耦合，并指出不稳定性或不稳定性是自适应自组织的必要条件。最后一篇论文通过信息理论和时空感受野和功能专门化的出现来解决神经元瞬态的作用。通过将大脑视为一个相互连接的动态系统的集合，可以表明对神经元动力学的充分描述包括特定时间的神经元活动及其最近的历史。这段历史构成了神经元瞬变。因此，瞬态是神经元相互作用的基本度量，也是脑系统功能整合的隐含代码。瞬态的本质，在不同的神经元群体中共同表达，反映了群体之间潜在的耦合。这种耦合可以是同步的（也可能是振荡的），也可以是异步的。同步耦合和异步耦合的一个关键区别是，前者本质上是线性的，而后者是非线性的。异步耦合的非线性特性使丰富的、上下文敏感的交互成为真实大脑动力学的特征，这表明它在功能集成中发挥着与同步交互同样重要的作用。线性和非线性耦合之间的区别对神经元相互作用的分析和表征具有根本性的意义，其中大多数是基于线性（同步）耦合（例如互相关图和相干）。利用神经磁数据表明，非线性（异步）耦合实际上比同步耦合更丰富，也更重要。
In this, the first of three papers, the nature of, and motivation for; neuronal transients is described in relation to characterizing brain dynamics. This paper deals with some basic aspects of neuronal dynamics, interactions, coupling and implicit neuronal codes. The second paper develops neuronal transients and nonlinear coupling in the context of dynamic instability and complexity, and suggests that instability or lability is necessary for adaptive self-organization. The final paper addresses the role of neuronal transients through information theory and the emergence of spatio-temporal receptive fields and functional specialization.By considering the brain as an ensemble of connected dynamic systems one can show that a sufficient description of neuronal dynamics comprises neuronal activity at a particular time and its recent history. This history constitutes a neuronal transient. As such, transients represent a fundamental metric of neuronal interactions and, implicitly a code employed in the functional integration of brain systems. The nature of transients, expressed conjointly in distinct neuronal populations, reflects the underlying coupling among populations. This coupling may be synchronous (and possibly oscillatory) or asynchronous. A critical distinction between synchronous and asynchronous coupling is that the former is essentially linear and the latter is nonlinear. The nonlinear nature of asynchronous coupling enables the rich, context-sensitive interactions that characterize real brain dynamics, suggesting that it plays a role in functional integration that may be as important as synchronous interactions. The distinction between linear and nonlinear coupling has fundamental implications for the analysis and characterization of neuronal interactions, most of which are predicated on linear (synchronous) coupling (e.g. cross-correlograms and coherence). Using neuromagnetic data it is shown that nonlinear (asynchronous) coupling is, in fact, more abundant and can be more significant than synchronous coupling.