  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.
