Neuronal Assembly Detection and Cell Membership Specification by Principal Component Analysis

In 1949, Donald Hebb postulated that assemblies of synchronously activated neurons are the elementary units of information processing in the brain. Despite being one of the most influential theories in neuroscience, Hebb's cell assembly hypothesis only started to become testable in the past two decades due to technological advances. However, while the technology for the simultaneous recording of large neuronal populations undergoes fast development, there is still a paucity of analytical methods that can properly detect and track the activity of cell assemblies. Here we describe a principal component-based method that is able to (1) identify all cell assemblies present in the neuronal population investigated, (2) determine the number of neurons involved in ensemble activity, (3) specify the precise identity of the neurons pertaining to each cell assembly, and (4) unravel the time course of the individual activity of multiple assemblies. Application of the method to multielectrode recordings of awake and behaving rats revealed that assemblies detected in the cerebral cortex and hippocampus typically contain overlapping neurons. The results indicate that the PCA method presented here is able to properly detect, track and specify neuronal assemblies, irrespective of overlapping membership.

1949年，唐纳德·赫布（Donald Hebb）提出，同步激活的神经元集群是大脑信息处理的基本单元。尽管赫布细胞集群假说（Hebb's cell assembly hypothesis）是神经科学领域最具影响力的理论之一，但得益于技术进步，其在近二十年来才得以实现可验证性。尽管大规模神经元群体同步记录技术正快速发展，但目前仍缺乏能够精准检测并追踪细胞集群活动的分析方法。本文介绍一种基于主成分分析（Principal Component Analysis, PCA）的方法，该方法可实现以下四项功能：(1) 识别所研究神经元群体中存在的全部细胞集群；(2) 确定参与集群活动的神经元数量；(3) 明确隶属于每个细胞集群的神经元的精准身份；(4) 解析多个集群各自的活动时间进程。将该方法应用于清醒活动大鼠的多电极记录数据后发现，在大脑皮层与海马体中检测到的细胞集群通常存在神经元重叠现象。研究结果表明，本文提出的PCA方法能够精准检测、追踪并界定神经元集群，且不受成员重叠的影响。