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网络控制框架及常见问题解答。

and the network control framework-FAQs.

机构信息

Center for Complex Network Research and Department of Physics, Northeastern University, Boston, MA 02115, USA.

Department of Psychiatry, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge CB2 0SZ, UK.

出版信息

Philos Trans R Soc Lond B Biol Sci. 2018 Sep 10;373(1758):20170372. doi: 10.1098/rstb.2017.0372.

Abstract

Control is essential to the functioning of any neural system. Indeed, under healthy conditions the brain must be able to continuously maintain a tight functional control between the system's inputs and outputs. One may therefore hypothesize that the brain's wiring is predetermined by the need to maintain control across multiple scales, maintaining the stability of key internal variables, and producing behaviour in response to environmental cues. Recent advances in network control have offered a powerful mathematical framework to explore the structure-function relationship in complex biological, social and technological networks, and are beginning to yield important and precise insights on neuronal systems. The network control paradigm promises a predictive, quantitative framework to unite the distinct datasets necessary to fully describe a nervous system, and provide mechanistic explanations for the observed structure and function relationships. Here, we provide a thorough review of the network control framework as applied to (Yan 2017 , 519-523. (doi:10.1038/nature24056)), in the style of Frequently Asked Questions. We present the theoretical, computational and experimental aspects of network control, and discuss its current capabilities and limitations, together with the next likely advances and improvements. We further present the Python code to enable exploration of control principles in a manner specific to this prototypical organism.This article is part of a discussion meeting issue 'Connectome to behaviour: modelling at cellular resolution'.

摘要

控制对于任何神经系统的运作都是至关重要的。事实上,在健康的条件下,大脑必须能够持续地在系统的输入和输出之间保持紧密的功能控制。因此,人们可以假设大脑的连接是由在多个尺度上保持控制的需要所决定的,以维持关键内部变量的稳定性,并对环境线索做出反应。最近在网络控制方面的进展为探索复杂的生物、社会和技术网络中的结构-功能关系提供了一个强大的数学框架,并开始对神经元系统产生重要而精确的见解。网络控制范式有望提供一个预测性的、定量的框架,将描述神经系统所需的不同数据集统一起来,并为观察到的结构和功能关系提供机制解释。在这里,我们以常见问题解答的形式,对应用于(Yan 2017,519-523. (doi:10.1038/nature24056))的网络控制框架进行了全面回顾。我们介绍了网络控制的理论、计算和实验方面,并讨论了它目前的能力和局限性,以及未来可能的进展和改进。我们进一步介绍了 Python 代码,以能够以特定于该原型生物的方式探索控制原理。本文是“从连接组到行为:在细胞分辨率下对进行建模”讨论会议的一部分。

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