Brain Institute, Federal University of Rio Grande do Norte, Brazil.
J Neurosci Methods. 2013 Nov 15;220(2):149-66. doi: 10.1016/j.jneumeth.2013.04.010. Epub 2013 Apr 29.
Recent progress in the technology for single unit recordings has given the neuroscientific community the opportunity to record the spiking activity of large neuronal populations. At the same pace, statistical and mathematical tools were developed to deal with high-dimensional datasets typical of such recordings. A major line of research investigates the functional role of subsets of neurons with significant co-firing behavior: the Hebbian cell assemblies. Here we review three linear methods for the detection of cell assemblies in large neuronal populations that rely on principal and independent component analysis. Based on their performance in spike train simulations, we propose a modified framework that incorporates multiple features of these previous methods. We apply the new framework to actual single unit recordings and show the existence of cell assemblies in the rat hippocampus, which typically oscillate at theta frequencies and couple to different phases of the underlying field rhythm.
单细胞记录技术的最新进展使神经科学界有机会记录大量神经元群体的尖峰活动。与此同时，还开发了统计和数学工具来处理这种记录中典型的高维数据集。一个主要的研究方向是研究具有显著共激活行为的神经元亚群的功能作用：赫布细胞集合。在这里，我们回顾了三种基于主成分分析和独立成分分析的线性方法，用于检测大神经元群体中的细胞集合。基于在尖峰序列模拟中的性能，我们提出了一个改进的框架，该框架结合了这些先前方法的多个特征。我们将新框架应用于实际的单细胞记录，并显示了大鼠海马体中细胞集合的存在，这些细胞集合通常以 theta 频率振荡，并与基础场节律的不同相位耦合。
