A model-based spike sorting algorithm for removing correlation artifacts in multi-neuron recordings.
A model-based spike sorting algorithm for removing correlation artifacts in multi-neuron recordings.
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DOI：
10.1371/journal.pone.0062123
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发表时间：
2013
期刊：
影响因子：
3.7
通讯作者：
Simoncelli EP
中科院分区：
文献类型：
--
作者：
Pillow JW;Shlens J;Chichilnisky EJ;Simoncelli EP
我们研究的问题，估计的尖峰列车的多个神经元记录在一个或多个细胞外电极上的电压痕迹。传统的棘波分类方法依赖于记录信号的阈值或聚类来识别棘波。虽然这些方法可以从记录中检测到大部分尖峰，但它们通常无法识别同步或近同步尖峰：多个尖峰重叠的情况。在这里，我们调查的几何故障在传统的排序算法，并记录在灵长类动物视网膜的多电极记录这种错误的流行。然后，我们开发了一种方法，多神经元尖峰排序使用的模型，明确占尖峰波形的叠加。我们将记录的电压轨迹建模为尖峰波形加上相关高斯噪声的随机背景分量的线性组合。将该测量模型与二进制尖峰序列上的伯努利先验相结合，得到了给定记录数据的尖峰的后验分布。我们引入了一个贪婪的算法，以最大限度地提高这个后验，我们称之为“二进制追求”。该算法允许尖峰波形的适度变化，并以比电压采样率更高的精度恢复尖峰时间。该方法基本上校正了传统方法中出现的互相关伪影，并且在真实的和模拟数据上基本上优于聚类方法。最后，我们开发了诊断工具，可用于评估在地面真理的情况下尖峰排序的错误。
We examine the problem of estimating the spike trains of multiple neurons from voltage traces recorded on one or more extracellular electrodes. Traditional spike-sorting methods rely on thresholding or clustering of recorded signals to identify spikes. While these methods can detect a large fraction of the spikes from a recording, they generally fail to identify synchronous or near-synchronous spikes: cases in which multiple spikes overlap. Here we investigate the geometry of failures in traditional sorting algorithms, and document the prevalence of such errors in multi-electrode recordings from primate retina. We then develop a method for multi-neuron spike sorting using a model that explicitly accounts for the superposition of spike waveforms. We model the recorded voltage traces as a linear combination of spike waveforms plus a stochastic background component of correlated Gaussian noise. Combining this measurement model with a Bernoulli prior over binary spike trains yields a posterior distribution for spikes given the recorded data. We introduce a greedy algorithm to maximize this posterior that we call “binary pursuit”. The algorithm allows modest variability in spike waveforms and recovers spike times with higher precision than the voltage sampling rate. This method substantially corrects cross-correlation artifacts that arise with conventional methods, and substantially outperforms clustering methods on both real and simulated data. Finally, we develop diagnostic tools that can be used to assess errors in spike sorting in the absence of ground truth.
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影响因子：
5.5
作者：
Greschner, Martin;Shlens, Jonathon;Chichilnisky, E. J.
通讯作者：
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