Deco Gustavo, Jirsa Viktor K, Robinson Peter A, Breakspear Michael, Friston Karl
Institució Catalana de Recerca i Estudis Avançats (ICREA), Universitat Pompeu Fabra, Department of Technology, Computational Neuroscience, Barcelona, Spain.
PLoS Comput Biol. 2008 Aug 29;4(8):e1000092. doi: 10.1371/journal.pcbi.1000092.
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.
皮层是一个复杂的系统，其特点在于其动力学和结构，这些是诸如行动、感知、学习、语言和认知等多种功能的基础。其结构架构已被研究了一百多年；然而，其动力学方面的研究却远没有那么深入。在本文中，我们在一个统一的框架下回顾并整合了多种用于描述皮层动力学的计算方法，这些方法在不同测量层面都有体现。不同时空尺度的计算模型有助于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。单神经元层面的建模是必要的，因为这是大脑计算元件（即神经元）之间信息交换的层面；介观模型告诉我们神经元件如何相互作用以在微柱和皮层柱层面产生涌现行为。宏观模型能让我们了解全脑动力学以及诸如皮层区域、丘脑和脑干等大规模神经系统之间的相互作用。从单单元记录、局部场电位到功能磁共振成像（fMRI）、脑电图（EEG）和脑磁图（MEG），每个描述层面都与神经科学数据有着独特的关联。皮层模型可以确定哪些类型的大规模神经元网络能够执行计算并描述它们的涌现特性。动力学的平均场及相关公式作为正向模型，在给定经验数据时可以求逆，也起着至关重要的补充作用。这使得动力学模型在整合理论与实验方面至关重要。我们认为，精心构建有原则且有依据的模型是将经验神经科学建立在一个与物理科学成就相称的有说服力的理论框架中的先决条件。
