Bayesian Spike Sorting: Parametric and Nonparametric Multivariate Gaussian Mixture Models

• Published in: Case Studies in Applied Bayesian Data Science pp. 215 - 227
• Main Authors: White, Nicole, van Havre, Zoé, Rousseau, Judith, Mengersen, Kerrie L.
• Format: Book Chapter
• Language: English
• Published: Cham Springer International Publishing 2020
• Series: Lecture Notes in Mathematics
• Subjects: Classification; Dirichlet process; Mixture model; Spike sorting
• ISBN: 9783030425524; 3030425525
• ISSN: 0075-8434; 1617-9692
• DOI: 10.1007/978-3-030-42553-1_8

The analysis of action potentials is an important task in neuroscience research, which aims to characterise neural activity under different subject conditions. The classification of action potentials, or “spike sorting”, can be formulated as an unsupervised clustering problem, and latent variable models such as mixture models are often used. In this chapter, we compare the performance of two mixture-based approaches when applied to spike sorting: the Overfitted Finite Mixture model (OFM) and the Dirichlet Process Mixture model (DPM). Both of these models can be used to cluster multivariate data when the number of clusters is unknown, however differences in model specification and assumptions may affect resulting statistical inference. Using real datasets obtained from extracellular recordings of the brain, model outputs are compared with respect to the number of identified clusters and classification uncertainty, with the intent of providing guidance on their application in practice.