The dynamic brain: from spiking neurons to neural masses and cortical fields.
The dynamic brain: from spiking neurons to neural masses and cortical fields.
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DOI：
10.1371/journal.pcbi.1000092
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发表时间：
2008-08-29
影响因子：
4.3
通讯作者：
Friston, Karl J.
中科院分区：
文献类型：
--
作者：
Deco, Gustavo;Jirsa, Viktor K.;Robinson, Peter A.;Breakspear, Michael;Friston, Karl J.
大脑皮层是一个复杂的系统，以其动力学和结构为特征，构成了许多功能的基础，如动作、感知、学习、语言和认知。它的结构体系已经被研究了一百多年；然而，对它的动力学问题的研究要彻底得多。在这篇文章中，我们回顾和整合，在一个统一的框架，各种计算方法已经被用来表征皮质的动力学，证明在不同的测量水平。不同时空尺度的计算模型帮助我们理解支撑神经过程的基本机制，并将这些过程与神经科学数据联系起来。在单个神经元层面上进行建模是必要的，因为这是大脑的计算元素(神经元)之间交换信息的层面。介观模型告诉我们神经元素如何相互作用，在微柱和皮质柱水平上产生紧急行为。宏观模型可以告诉我们整个大脑的动态和大规模神经系统之间的相互作用，如皮质区域、丘脑和脑干。每个级别的描述都与神经科学数据唯一相关，从单单元记录到局部场电位到功能磁共振成像(FMRI)、脑电(EEG)和脑磁图(MEG)。大脑皮层的模型可以确定哪些类型的大规模神经网络可以执行计算并表征它们的紧急特性。平均场和相关的动力学公式也扮演着重要和补充的角色，因为正演模型可以在给定经验数据的情况下进行倒置。这使得动态模型在整合理论和实验方面至关重要。我们认为，阐述有原则的和知情的模型是将经验神经科学建立在一个令人信服的理论框架中的先决条件，这与物理科学的成就相称。
The cortex is a complex system, characterized by its dynamics and architecture, which underlie many functions such as action, perception, learning, language, and cognition. Its structural architecture has been studied for more than a hundred years; however, its dynamics have been addressed much less thoroughly. In this paper, we review and integrate, in a unifying framework, a variety of computational approaches that have been used to characterize the dynamics of the cortex, as evidenced at different levels of measurement. Computational models at different space–time scales help us understand the fundamental mechanisms that underpin neural processes and relate these processes to neuroscience data. Modeling at the single neuron level is necessary because this is the level at which information is exchanged between the computing elements of the brain; the neurons. Mesoscopic models tell us how neural elements interact to yield emergent behavior at the level of microcolumns and cortical columns. Macroscopic models can inform us about whole brain dynamics and interactions between large-scale neural systems such as cortical regions, the thalamus, and brain stem. Each level of description relates uniquely to neuroscience data, from single-unit recordings, through local field potentials to functional magnetic resonance imaging (fMRI), electroencephalogram (EEG), and magnetoencephalogram (MEG). Models of the cortex can establish which types of large-scale neuronal networks can perform computations and characterize their emergent properties. Mean-field and related formulations of dynamics also play an essential and complementary role as forward models that can be inverted given empirical data. This makes dynamic models critical in integrating theory and experiments. We argue that elaborating principled and informed models is a prerequisite for grounding empirical neuroscience in a cogent theoretical framework, commensurate with the achievements in the physical sciences.
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