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An online spike detection and spike classification algorithm capable of instantaneous resolution of overlapping spikes

机译：能够瞬时分辨重叠尖峰的在线尖峰检测和尖峰分类算法

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摘要

For the analysis of neuronal cooperativity, simultaneously recorded extracellular signals from neighboring neurons need to be sorted reliably by a spike sorting method. Many algorithms have been developed to this end, however, to date, none of them manages to fulfill a set of demanding requirements. In particular, it is desirable to have an algorithm that operates online, detects and classifies overlapping spikes in real time, and that adapts to non-stationary data. Here, we present a combined spike detection and classification algorithm, which explicitly addresses these issues. Our approach makes use of linear filters to find a new representation of the data and to optimally enhance the signal-to-noise ratio. We introduce a method called “Deconfusion” which de-correlates the filter outputs and provides source separation. Finally, a set of well-defined thresholds is applied and leads to simultaneous spike detection and spike classification. By incorporating a direct feedback, the algorithm adapts to non-stationary data and is, therefore, well suited for acute recordings. We evaluate our method on simulated and experimental data, including simultaneous intra/extra-cellular recordings made in slices of a rat cortex and recordings from the prefrontal cortex of awake behaving macaques. We compare the results to existing spike detection as well as spike sorting methods. We conclude that our algorithm meets all of the mentioned requirements and outperforms other methods under realistic signal-to-noise ratios and in the presence of overlapping spikes.
机译：为了分析神经元的协同性，需要通过尖峰分选方法对来自相邻神经元的同时记录的细胞外信号进行可靠的分选。为此已经开发了许多算法，但是迄今为止，它们都无法满足一组苛刻的要求。特别地，期望具有一种在线操作，实时检测和分类重叠尖峰并且适应于非平稳数据的算法。在这里，我们提出了一种结合的尖峰检测和分类算法，可以明确解决这些问题。我们的方法利用线性滤波器来查找数据的新表示形式，并以最佳方式增强信噪比。我们引入了一种称为“解混淆”的方法，该方法使滤波器输出不相关并提供信号源分离。最终，应用一组定义良好的阈值，并导致同时进行尖峰检测和尖峰分类。通过合并直接反馈，该算法适用于非平稳数据，因此非常适合急性记录。我们在模拟和实验数据上评估我们的方法，包括在大鼠皮质切片中同时进行的细胞内/细胞外记录以及清醒的猕猴前额叶皮质的记录。我们将结果与现有的峰值检测以及峰值排序方法进行比较。我们得出的结论是，在实际信噪比和存在重叠尖峰的情况下，我们的算法满足了所有上述要求，并且优于其他方法。

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