Bayesian Spike Sorting: Parametric and Nonparametric Multivariate Gaussian Mixture Models

2020 ◽  
pp. 215-227
Author(s):  
Nicole White ◽  
Zoé van Havre ◽  
Judith Rousseau ◽  
Kerrie L. Mengersen
Keyword(s):  
Mixture Models ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Spike Sorting ◽  
Multivariate Gaussian

scholarly journals Spike sorting with Gaussian mixture models

2018 ◽  
Author(s):  
Bryan C. Souza ◽  
Vítor Lopes-dos-Santos ◽  
João Bacelo ◽  
Adriano B. L. Tort
Keyword(s):  
Mixture Models ◽  
Principal Components ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Spike Sorting ◽  
Main Challenge ◽  
Manual Adjustment ◽  
Cluster Properties ◽  
Spike Waveforms ◽  
Friendly Graphical User Interface

AbstractThe shape of extracellularly recorded action potentials is a product of several variables, such as the biophysical and anatomical properties of the neuron and the relative position of the electrode. This allows for isolating spikes of different neurons recorded in the same channel into clusters based on waveform features. However, correctly classifying spike waveforms into their underlying neuronal sources remains a main challenge. This process, called spike sorting, typically consists of two steps: (1) extracting relevant waveform features (e.g., height, width), and (2) clustering them into non-overlapping groups believed to correspond to different neurons. In this study, we explored the performance of Gaussian mixture models (GMMs) in these two steps. We extracted relevant waveform features using a combination of common techniques (e.g., principal components and wavelets) and GMM fitting parameters (e.g., standard deviations and peak distances). Then, we developed an approach to perform unsupervised clustering using GMMs, which estimates cluster properties in a data-driven way. Our results show that the proposed GMM-based framework outperforms previously established methods when using realistic simulations of extracellular spikes and actual extracellular recordings to evaluate sorting performance. We also discuss potentially better techniques for feature extraction than the widely used principal components. Finally, we provide a friendly graphical user interface in MATLAB to run our algorithm, which allows for manual adjustment of the automatic results.

scholarly journals Spike sorting with Gaussian mixture models

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Bryan C. Souza ◽  
Vítor Lopes-dos-Santos ◽  
João Bacelo ◽  
Adriano B. L. Tort
Keyword(s):  
Mixture Models ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Spike Sorting

scholarly journals Sleep spindle detection using multivariate Gaussian mixture models

2017 ◽  
Vol 27 (4) ◽  
pp. e12614 ◽  
Author(s):  
Chanakya Reddy Patti ◽  
Thomas Penzel ◽  
Dean Cvetkovic
Keyword(s):  
Mixture Models ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Sleep Spindle ◽  
Multivariate Gaussian
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