Validation of neural spike sorting algorithms without ground-truth information
Validation of neural spike sorting algorithms without ground-truth information
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DOI：
10.1016/j.jneumeth.2016.02.022
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发表时间：
2016-05-01
影响因子：
3
通讯作者：
Greengard, Leslie F.
中科院分区：
文献类型：
--
作者：
Barnett, Alex H.;Magland, Jeremy F.;Greengard, Leslie F.
背景资料：电生理记录的吞吐量正在迅速增长，允许数千个同时通道，并且有越来越多的各种各样的尖峰分选算法被设计用于从这些数据中提取神经放电事件。这就迫切需要标准化的，自动评估的神经元输出的质量由这样的algorithm.New方法：我们引入了一套验证指标，评估一个给定的自动尖峰排序算法应用到一个给定的dataset的可信度。通过重新运行的尖峰分选机两次或更多次，在各种扰动下的数据本身的变化一致的指标测量稳定性，使没有假设的算法的内部工作原理，和最小的假设noise.Results：我们说明了新的标准排序算法适用于在体内和体外记录，包括重叠尖峰的时间序列。我们将这些指标与现有的质量指标以及模拟时间序列中的地面真实准确性进行比较。我们提供一个软件实现。与现有方法的比较：到目前为止，这些方法都依赖于地面实况、模拟数据、内部算法变量（例如聚类分离）或难治性违规。相比之下，通过标准化的接口，我们的指标评估任何自动算法的可靠性，而不参考内部变量（如特征空间）或生理criterions.Conclusions：稳定性是结果再现性的先决条件。这些指标可以减少目前用于验证的大量人力，并应成为大规模自动化尖峰排序和算法系统基准测试的重要组成部分。(C)© 2016 Elsevier B.V.版权所有。
Background: The throughput of electrophysiological recording is growing rapidly, allowing thousands of simultaneous channels, and there is a growing variety of spike sorting algorithms designed to extract neural firing events from such data. This creates an urgent need for standardized, automatic evaluation of the quality of neural units output by such algorithms.New method: We introduce a suite of validation metrics that assess the credibility of a given automatic spike sorting algorithm applied to a given dataset. By rerunning the spike sorter two or more times, the metrics measure stability under various perturbations consistent with variations in the data itself, making no assumptions about the internal workings of the algorithm, and minimal assumptions about the noise.Results: We illustrate the new metrics on standard sorting algorithms applied to both in vivo and ex vivo recordings, including a time series with overlapping spikes. We compare the metrics to existing quality measures, and to ground-truth accuracy in simulated time series. We provide a software implementation. Comparison with existing methods: Metrics have until now relied on ground-truth, simulated data, internal algorithm variables (e.g. cluster separation), or refractory violations. By contrast, by standardizing the interface, our metrics assess the reliability of any automatic algorithm without reference to internal variables (e.g. feature space) or physiological criteria.Conclusions: Stability is a prerequisite for reproducibility of results. Such metrics could reduce the significant human labor currently spent on validation, and should form an essential part of large-scale automated spike sorting and systematic benchmarking of algorithms. (C) 2016 Elsevier B.V. All rights reserved.