A Fully Automated Approach to Spike Sorting.,Neuron

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A Fully Automated Approach to Spike Sorting.
Neuron ( IF 14.7 ) Pub Date : 2017-Sep-13 , DOI: 10.1016/j.neuron.2017.08.030
Jason E Chung ₁ , Jeremy F Magland ₂ , Alex H Barnett ₃ , Vanessa M Tolosa ₄ , Angela C Tooker ₄ , Kye Y Lee ₄ , Kedar G Shah ₄ , Sarah H Felix ₄ , Loren M Frank ₅ , Leslie F Greengard ₆

Affiliation

Understanding the detailed dynamics of neuronal networks will require the simultaneous measurement of spike trains from hundreds of neurons (or more). Currently, approaches to extracting spike times and labels from raw data are time consuming, lack standardization, and involve manual intervention, making it difficult to maintain data provenance and assess the quality of scientific results. Here, we describe an automated clustering approach and associated software package that addresses these problems and provides novel cluster quality metrics. We show that our approach has accuracy comparable to or exceeding that achieved using manual or semi-manual techniques with desktop central processing unit (CPU) runtimes faster than acquisition time for up to hundreds of electrodes. Moreover, a single choice of parameters in the algorithm is effective for a variety of electrode geometries and across multiple brain regions. This algorithm has the potential to enable reproducible and automated spike sorting of larger scale recordings than is currently possible.

中文翻译：

一种全自动的尖峰排序方法。

了解神经元网络的详细动态需要同时测量数百个（或更多）神经元的尖峰序列。目前，从原始数据中提取尖峰时间和标签的方法非常耗时，缺乏标准化，并且涉及人工干预，使得维护数据来源和评估科学结果的质量变得困难。在这里，我们描述了一种自动集群方法和相关软件包，可以解决这些问题并提供新颖的集群质量指标。我们表明，我们的方法的精度可与使用手动或半手动技术实现的精度相媲美或超过，并且桌面中央处理单元（CPU）运行时间比多达数百个电极的采集时间更快。此外，算法中参数的单一选择对于各种电极几何形状和跨多个大脑区域都是有效的。该算法有可能实现比目前更大规模的记录的可重复和自动尖峰排序。

更新日期：2017-09-20

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