Model-based spike sorting with a mixture of drifting t-distributions

• Published in: Journal of neuroscience methods 2017-08, Vol.288, p.82-98
• Main Authors: Shan, Kevin Q., Lubenov, Evgueniy V., Siapas, Athanassios G.
• Format: Article
• Language: English
• Subjects: Action Potentials - physiology; Animals; Chronic recording; Cluster Analysis; Cluster drift; Clustering; Computer Simulation; Heavy tails; Humans; Models, Neurological; Neurons - physiology; Signal Processing, Computer-Assisted; Spike overlap; Unit isolation metrics
• ISSN: 0165-0270; 1872-678X
• DOI: 10.1016/j.jneumeth.2017.06.017

•A nonstationary generative model for spike sorting is proposed.•This model tracks unit drift in chronic recordings and is robust to outliers.•It offers improved estimates of single unit isolation in empirical data.•An efficient software implementation is provided for fitting the model. Chronic extracellular recordings are a powerful tool for systems neuroscience, but spike sorting remains a challenge. A common approach is to fit a generative model, such as a mixture of Gaussians, to the observed spike data. Even if non-parametric methods are used for spike sorting, such generative models provide a quantitative measure of unit isolation quality, which is crucial for subsequent interpretation of the sorted spike trains. We present a spike sorting strategy that models the data as a mixture of drifting t-distributions. This model captures two important features of chronic extracellular recordings—cluster drift over time and heavy tails in the distribution of spikes—and offers improved robustness to outliers. We evaluate this model on several thousand hours of chronic tetrode recordings and show that it fits the empirical data substantially better than a mixture of Gaussians. We also provide a software implementation that can re-fit long datasets in a few seconds, enabling interactive clustering of chronic recordings. We identify three common failure modes of spike sorting methods that assume stationarity and evaluate their impact given the empirically-observed cluster drift in chronic recordings. Using hybrid ground truth datasets, we also demonstrate that our model-based estimate of misclassification error is more accurate than previous unit isolation metrics. The mixture of drifting t-distributions model enables efficient spike sorting of long datasets and provides an accurate measure of unit isolation quality over a wide range of conditions.