Relating network connectivity to dynamics: opportunities and challenges for theoretical neuroscience
Relating network connectivity to dynamics: opportunities and challenges for theoretical neuroscience
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DOI：
10.1016/j.conb.2019.06.003
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发表时间：
2019-10-01
影响因子：
5.7
通讯作者：
Morrison, Katherine
中科院分区：
文献类型：
--
作者：
Curto, Carina;Morrison, Katherine
我们回顾最近的工作有关网络连接的神经活动的动态。虽然源于网络科学的概念提供了一个有价值的起点，但对图论结构和度量的解释可能高度依赖于与网络相关的动态。对线性动力学非常有意义的性质，如随机游走和网络流模型，在神经科学环境中的相关性可能有限。理论和计算神经科学在理解网络连接性和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。
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.