Clustering action potential spikes: Insights on the use of overfitted finite mixture models and Dirichlet process mixture models

TL;DR

This paper compares overfitted finite mixture models and Dirichlet process mixture models for clustering action potential spikes, providing insights into their suitability for complex spike sorting tasks in neuroscience.

Contribution

It offers a comparative analysis of OFMs and DPMs for spike sorting, highlighting their differences and guiding model choice for complex medical data clustering.

Findings

OFMs and DPMs have different assumptions affecting clustering performance.

The study provides practical insights for selecting models based on data complexity.

Results inform future neuroscience data analysis and spike sorting methods.

Abstract

The modelling of action potentials from extracellular recordings, or spike sorting, is a rich area of neuroscience research in which latent variable models are often used. Two such models, Overfitted Finite Mixture models (OFMs) and Dirichlet Process Mixture models (DPMs) are considered to provide insights for unsupervised clustering of complex, multivariate medical data when the number of clusters is unknown. OFM and DPM are structured in a similar hierarchical fashion but they are based on different philosophies with different underlying assumptions. This study investigates how these differences impact on a real study of spike sorting, for the estimation of multivariate Gaussian location-scale mixture models in the presence of common difficulties arising from complex medical data. The results provide insights allowing the future analyst to choose an approach suited to the situation and goal of the research problem at hand.