Center for Perceptual Systems, Department of Psychology and Section of Neurobiology, The University of Texas at Austin, Austin, Texas, USA.
PLoS One. 2013 May 3;8(5):e62123. doi: 10.1371/journal.pone.0062123. Print 2013.
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.
我们研究了从一个或多个细胞外电极记录的电压迹线估计多个神经元尖峰的问题。传统的尖峰分类方法依赖于记录信号的阈值或聚类来识别尖峰。虽然这些方法可以检测到记录中很大一部分的尖峰，但它们通常无法识别同步或近同步的尖峰：即多个尖峰重叠的情况。在这里，我们研究了传统分类算法中的故障几何形状，并记录了灵长类视网膜多电极记录中此类错误的普遍性。然后，我们开发了一种使用显式考虑尖峰波形叠加的模型对多神经元尖峰进行分类的方法。我们将记录的电压迹线建模为尖峰波形的线性组合，加上相关高斯噪声的随机背景分量。将此测量模型与二进制尖峰序列的伯努利先验相结合，可得出给定记录数据的尖峰后验分布。我们引入了一种贪婪算法来最大化此后验，我们称之为“二进制追踪”。该算法允许尖峰波形有适度的变化，并以高于电压采样率的精度恢复尖峰时间。与传统方法相比，该方法大大纠正了交叉相关伪影，并在真实和模拟数据上均优于聚类方法。最后，我们开发了诊断工具，可在没有真实数据的情况下评估尖峰分类中的错误。
