标题： 团拓扑揭示神经相关性中的内在几何结构：概述 显示英文标题

标题： Clique Topology Reveals Intrinsic Geometric Structure in Neural Correlations: An Overview

摘要： 此出版物是对团拓扑学的概述——一种用于从包含隐藏非线性的神经活动数据中提取结构特征的新颖矩阵分析技术。 我们强调Gusti等人所做的工作， 该工作引入了团拓扑学，并通过证明大鼠海马中的神经相关性是由海马回路的几何结构决定的，而不是位置编码的结果，从而验证了其在神经特征提取中的适用性。 显示英文摘要

摘要： This publication serves as an overview of clique topology -- a novel matrix analysis technique used to extract structural features from neural activity data that contains hidden nonlinearities. We highlight work done by Gusti et al. which introduces clique topology and verifies its applicability to neural feature extraction by showing that neural correlations in the rat hippocampus are determined by geometric structure of hippocampal circuits, rather than being a consequence of positional coding.