将网络连接与动力学联系起来：理论神经科学的机遇与挑战。
Relating network connectivity to dynamics: opportunities and challenges for theoretical neuroscience.
机构信息
The Pennsylvania State University, PA 16802, United States.
School of Mathematical Sciences, University of Northern Colorado, Greeley, CO 80639, USA.
出版信息
Curr Opin Neurobiol. 2019 Oct;58:11-20. doi: 10.1016/j.conb.2019.06.003. Epub 2019 Jul 15.
Abstract
We review recent work relating network connectivity to the dynamics of neural activity. While concepts stemming from network science provide a valuable starting point, the interpretation of graph-theoretic structures and measures can be highly dependent on the dynamics associated to the network. Properties that are quite meaningful for linear dynamics, such as random walk and network flow models, may be of limited relevance in the neuroscience setting. Theoretical and computational neuroscience are playing a vital role in understanding the relationship between network connectivity and the nonlinear dynamics associated to neural networks.
摘要
我们回顾了与网络连接与神经活动动力学相关的最近工作。虽然源自网络科学的概念提供了一个有价值的起点，但图论结构和度量的解释可能高度依赖于与网络相关的动力学。对于线性动力学来说非常有意义的性质，如随机游走和网络流模型，在神经科学环境中可能相关性有限。理论和计算神经科学在理解网络连接与神经网络相关的非线性动力学之间的关系方面发挥着至关重要的作用。
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