Herbst Joshua A, Gammeter Stephan, Ferrero David, Hahnloser Richard H R
Institute of Neuroinformatics UZH/ETH Zurich, Winterthurerstrasse 190, Zurich, Switzerland.
J Neurosci Methods. 2008 Sep 15;174(1):126-34. doi: 10.1016/j.jneumeth.2008.06.011. Epub 2008 Jun 21.
The ability to detect and sort overlapping spike waveforms in extracellular recordings is key to studies of neural coding at high spatial and temporal resolution. Most spike-sorting algorithms are based on initial spike detection (e.g. by a voltage threshold) and subsequent waveform classification. Much effort has been devoted to the clustering step, despite the fact that conservative spike detection is notoriously difficult in low signal-to-noise conditions and often entails many spike misses. Hidden Markov models (HMMs) can serve as generative models for continuous extracellular data records. These models naturally combine the spike detection and classification steps into a single computational procedure. They unify the advantages of independent component analysis (ICA) and overlap-search algorithms because they blindly perform source separation even in cases where several neurons are recorded on a single electrode. We apply HMMs to artificially generated data and to extracellular signals recorded with glass electrodes. We show that in comparison with state-of-art spike-sorting algorithms, HMM-based spike sorting exhibits a comparable number of false positive spike classifications but many fewer spike misses.
在细胞外记录中检测和分类重叠尖峰波形的能力是高时空分辨率下神经编码研究的关键。大多数尖峰分类算法基于初始尖峰检测（例如通过电压阈值）和随后的波形分类。尽管在低信噪比条件下保守的尖峰检测非常困难且常常导致许多尖峰遗漏，但聚类步骤仍投入了大量精力。隐马尔可夫模型（HMM）可以作为连续细胞外数据记录的生成模型。这些模型自然地将尖峰检测和分类步骤合并为一个单一的计算过程。它们结合了独立成分分析（ICA）和重叠搜索算法的优点，因为即使在单个电极上记录了多个神经元的情况下，它们也能盲目地进行源分离。我们将HMM应用于人工生成的数据以及用玻璃电极记录的细胞外信号。我们表明，与当前最先进的尖峰分类算法相比，基于HMM的尖峰分类具有相当数量的误报尖峰分类，但漏检的尖峰要少得多。
