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使用“SpikeSorter”对电生理记录进行分类的可视化指南。

A Visual Guide to Sorting Electrophysiological Recordings Using 'SpikeSorter'.

作者信息

Swindale Nicholas V, Mitelut Catalin, Murphy Timothy H, Spacek Martin A

机构信息

Ophthalmology and Visual Sciences, University of British Columbia;

Ophthalmology and Visual Sciences, University of British Columbia.

出版信息

J Vis Exp. 2017 Feb 10(120):55217. doi: 10.3791/55217.

Abstract

Few stand-alone software applications are available for sorting spikes from recordings made with multi-electrode arrays. Ideally, an application should be user friendly with a graphical user interface, able to read data files in a variety of formats, and provide users with a flexible set of tools giving them the ability to detect and sort extracellular voltage waveforms from different units with some degree of reliability. Previously published spike sorting methods are now available in a software program, SpikeSorter, intended to provide electrophysiologists with a complete set of tools for sorting, starting from raw recorded data file and ending with the export of sorted spikes times. Procedures are automated to the extent this is currently possible. The article explains and illustrates the use of the program. A representative data file is opened, extracellular traces are filtered, events are detected and then clustered. A number of problems that commonly occur during sorting are illustrated, including the artefactual over-splitting of units due to the tendency of some units to fire spikes in pairs where the second spike is significantly smaller than the first, and over-splitting caused by slow variation in spike height over time encountered in some units. The accuracy of SpikeSorter's performance has been tested with surrogate ground truth data and found to be comparable to that of other algorithms in current development.

摘要

用于从多电极阵列记录中筛选尖峰的独立软件应用程序很少。理想情况下,应用程序应具有图形用户界面,便于用户使用,能够读取多种格式的数据文件,并为用户提供一套灵活的工具,使他们能够以一定的可靠性检测和分类来自不同单元的细胞外电压波形。以前发表的尖峰分类方法现在可以在一个名为SpikeSorter的软件程序中使用,该程序旨在为电生理学家提供一套完整的分类工具,从原始记录数据文件开始,到分类后的尖峰时间导出结束。程序在当前可能的范围内实现了自动化。本文解释并说明了该程序的使用方法。打开一个代表性的数据文件,对细胞外轨迹进行滤波,检测事件,然后进行聚类。文中还说明了分类过程中常见的一些问题,包括由于某些单元倾向于成对发放尖峰,其中第二个尖峰明显小于第一个尖峰,导致单元出现人为过度分割,以及某些单元中尖峰高度随时间缓慢变化导致的过度分割。通过替代的地面真值数据测试了SpikeSorter的性能准确性,发现其与当前正在开发的其他算法相当。

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引用本文的文献

本文引用的文献

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Spike sorting for large, dense electrode arrays.用于大型密集电极阵列的尖峰分类
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Validation of neural spike sorting algorithms without ground-truth information.无真实信息情况下神经尖峰分类算法的验证
J Neurosci Methods. 2016 May 1;264:65-77. doi: 10.1016/j.jneumeth.2016.02.022. Epub 2016 Feb 28.
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Spike sorting for polytrodes: a divide and conquer approach.多电极尖峰分类:分而治之的方法。
Front Syst Neurosci. 2014 Feb 10;8:6. doi: 10.3389/fnsys.2014.00006. eCollection 2014.

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