Multichannel Electrophysiological Spike Sorting via Joint Dictionary Learning and Mixture Modeling
Multichannel Electrophysiological Spike Sorting via Joint Dictionary Learning and Mixture Modeling
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DOI：
10.1109/tbme.2013.2275751
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发表时间：
2014-01-01
影响因子：
4.6
通讯作者：
Carin, Lawrence
中科院分区：
文献类型：
--
作者：
Carlson, David E.;Vogelstein, Joshua T.;Carin, Lawrence
我们提出了一种跨多个记录期对多通道细胞外电生理数据进行联合特征学习和聚类的方法，用于动作电位检测和分类（排序）。我们的方法主要在四个方面改进了以前的技术水平。首先，通过跨通道共享信息，我们可以更好地区分单个单位尖峰和伪影。其次，我们提出的“聚焦混合模型”（FMM）处理单位出现，消失，或再现在多个记录天，任何慢性实验的一个重要考虑因素。第三，通过联合学习特征和聚类，我们比以前通过两阶段学习过程进行的尝试提高了性能。第四，通过直接建模尖峰率，我们提高了稀疏放电神经元的检测。此外，我们的贝叶斯方法无缝处理缺失数据。我们提出了最先进的性能，而不需要手动调整超参数，考虑到部分地面真理的公共数据集和新的实验数据集。
We propose a methodology for joint feature learning and clustering of multichannel extracellular electrophysiological data, across multiple recording periods for action potential detection and classification (sorting). Our methodology improves over the previous state of the art principally in four ways. First, via sharing information across channels, we can better distinguish between single-unit spikes and artifacts. Second, our proposed "focused mixture model" (FMM) deals with units appearing, disappearing, or reappearing over multiple recording days, an important consideration for any chronic experiment. Third, by jointly learning features and clusters, we improve performance over previous attempts that proceeded via a two-stage learning process. Fourth, by directly modeling spike rate, we improve the detection of sparsely firing neurons. Moreover, our Bayesian methodology seamlessly handles missing data. We present the state-of-the-art performance without requiring manually tuning hyperparameters, considering both a public dataset with partial ground truth and a new experimental dataset.