Dynamical networks: finding, measuring, and tracking neural population activity using network science
Dynamical networks: finding, measuring, and tracking neural population activity using network science
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动态网络：利用网络科学发现、测量和跟踪神经群体活动
DOI：
10.1101/115485
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发表时间：
2017
期刊：
影响因子：
--
通讯作者：
Humphries M
中科院分区：
文献类型：
--
作者：
Humphries M
系统神经科学急于在同一时间记录尽可能多的神经元。由于大脑使用神经元群进行计算和编码，人们希望这些数据将揭示神经计算的基本原理。但是，同时记录数百、数千或更多的神经元，就会出现不可避免的问题，即如何可视化、描述和量化它们的相互作用。在这里，我认为网络科学提供了一套可扩展的分析工具，这些工具已经解决了这些问题。通过将神经元视为节点，将它们的相互作用视为链接，单个网络可以可视化和描述任意大的记录。我表明，通过这种描述，我们可以量化操纵神经回路的效果，跟踪种群动态随时间的变化，并定量定义神经种群的理论概念，如细胞集合。因此，将网络科学作为分析人口记录的核心部分，将为我们对神经计算的理解提供定性和定量的进步。
Systems neuroscience is in a headlong rush to record from as many neurons at the same time as possible. As the brain computes and codes using neuron populations, it is hoped these data will uncover the fundamentals of neural computation. But with hundreds, thousands, or more simultaneously recorded neurons come the inescapable problems of visualizing, describing, and quantifying their interactions. Here I argue that network science provides a set of scalable, analytical tools that already solve these problems. By treating neurons as nodes and their interactions as links, a single network can visualize and describe an arbitrarily large recording. I show that with this description we can quantify the effects of manipulating a neural circuit, track changes in population dynamics over time, and quantitatively define theoretical concepts of neural populations such as cell assemblies. Using network science as a core part of analyzing population recordings will thus provide both qualitative and quantitative advances to our understanding of neural computation.