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View Submission - SDS2022
Title: Bayesian nonparametric analysis of calcium imaging data Authors:  Antonio Canale - University of Padua (Italy) [presenting]
Michele Guindani - University of California, Irvine (United States)
Laura D Angelo - Universita di Milano Bicocca (Italy)
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 will discuss several challenges that this novel and complex type of data pose and how they can be solved by means of flexible Bayesian nonparametric models. The proposed solutions exploit several recent advances in Bayesian nonparametric including nonparametric dependent mixture priors to borrow information between experiments and discover similarities in the distributional patterns of neuronal responses and inner spike-and-slab nonparametric models to jointly model different patterns of neuronal activity or the lack of thereof.