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Title: Bayesian nonparametric analysis for the detection of spikes in noisy calcium imaging data Authors:  Michele Guindani - University of California, Irvine (United States) [presenting]
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 intra-cellular calcium signals. An ongoing challenge is deconvoluting the temporal signals to extract the spike trains from the noisy calcium signals' time-series. We propose a nested Bayesian finite mixture specification that allows for the estimation of 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. Furthermore, the spikes' intensity values are also clustered within and between experimental conditions to determine the existence of common (recurring) response amplitudes. Simulation studies and the analysis of a data set from the Allen Brain Observatory show the effectiveness of the method in clustering and detecting neuronal activities.