Speaker: Laura D’Angelo (Dipartimento di Economia, Metodi Quantitativi e Strategie d’Impresa, Università Milano Bicocca)

Title: Modeling grouped data via finite nested mixture models: an application to calcium imaging data

Abstract:

Recent advancements in miniaturized fluorescence microscopy have made it possible to investigate neuronal responses to external stimuli in awake behaving animals through the analysis of intracellular calcium signals. We propose a nested Bayesian finite mixture specification that allows estimating the underlying spiking activity and, simultaneously, reconstructing the distributions of the calcium transient spikes’ amplitudes under different experimental conditions. The proposed model leverages two nested layers of random discrete mixture priors to borrow information between experiments and discover similarities in the distributional patterns of neuronal responses to different stimuli. We show that nested finite mixtures provide a valid alternative to priors based on infinite formulations and can even lead to better performances in some scenarios. We derive several prior properties and compare them with other well-known nonparametric nested models analytically and via simulation.