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Bayesian nonparametric analysis for the detection of spikes in noisy calcium imaging data
Authors:Laura D'Angelo  Antonio Canale  Zhaoxia Yu  Michele Guindani
Institution:1. Department of Economics, Management and Statistics, University of Milano-Bicocca, Milan, Italy;2. Department of Statistical Sciences, University of Padova, Padova, Italy;3. Department of Statistics, University of California, Irvine, Irvine, California, USA
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. An ongoing challenge is deconvolving the temporal signals to extract the spike trains from the noisy calcium signals' time series. In this article, we propose a nested Bayesian finite mixture specification that allows 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 dataset from the Allen Brain Observatory show the effectiveness of the method in clustering and detecting neuronal activities.
Keywords:Dirichlet process  mixture of finite mixtures  model-based clustering  nested Dirichlet process  spike and slab
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