Neurobiology & Anatomy, University of Rochester, Box 603, 601 Elmwood Ave., Rochester, NY 14642, USA.
J Neurosci Methods. 2012;206(2):120-31. doi: 10.1016/j.jneumeth.2012.02.013. Epub 2012 Feb 23.
Sorting action potentials (spikes) from tetrode recordings can be time consuming, labor intensive, and inconsistent, depending on the methods used and the experience of the operator. The techniques presented here were designed to address these issues. A feature related to the slope of the spike during repolarization is computed. A small subsample of the features obtained from the tetrode (ca. 10,000-20,000 events) is clustered using a modified version of k-means that uses Mahalanobis distance and a scaling factor related to the cluster size. The cluster-size-based scaling improves the clustering by increasing the separability of close clusters, especially when they are of disparate size. The full data set is then classified from the statistics of the clusters. The technique yields consistent results for a chosen number of clusters. A MATLAB implementation is able to classify more than 5000 spikes per second on a modern workstation.
从四极管记录中对动作电位（尖峰）进行分类可能既耗时又费力，而且结果也不一致，具体取决于所使用的方法和操作人员的经验。这里介绍的技术旨在解决这些问题。计算与复极期间尖峰斜率相关的特征。使用 Mahalanobis 距离和与聚类大小相关的缩放因子，使用经过修改的 k-均值对从四极管获得的特征的一小部分（约 10,000-20,000 个事件）进行聚类。基于聚类大小的缩放通过增加近距离聚类的可分离性来提高聚类效果，特别是当它们的大小不同时。然后，根据聚类的统计信息对整个数据集进行分类。对于选定数量的聚类，该技术可产生一致的结果。MATLAB 实现能够在现代工作站上每秒分类超过 5000 个尖峰。
