One major challenge of neuroscience is identifying structure in seemingly disorganized neural activity. Different types of structure have different computational implications that can help neuroscientists understand the functional role of a particular brain area. Here, we outline a unified approach to characterize structure by inspecting the representational geometry and the modularity properties of the recorded activity and show that a similar approach can also reveal structure in connectivity. We start by setting up a general framework for determining geometry and modularity in activity and connectivity and relating these properties with computations performed by the network. We then use this framework to review the types of structure found in recent studies of model networks performing three classes of computations.

中文翻译：

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结构在神经活动和连接中的计算作用


神经科学的一大挑战是识别看似无序的神经活动的结构。不同类型的结构具有不同的计算含义，可以帮助神经科学家了解特定大脑区域的功能作用。在这里，我们概述了一种通过检查记录活动的表征几何和模块化属性来表征结构的统一方法，并表明类似的方法也可以揭示连通性中的结构。我们首先建立一个通用框架，用于确定活动和连接中的几何形状和模块化，并将这些属性与网络执行的计算相关联。然后，我们使用这个框架来回顾最近在执行三类计算的模型网络研究中发现的结构类型。