Abstract
The modern technique of calcium imaging is revolutionizing the understanding of the nervous system, thanks to its ability to image the activity of individual neurons over time in freely moving animals. This technology has led to remarkable insights into how neurons process and encode information, both individually and collectively. In this talk, we discuss how Bayesian mixture models can help uncover the complex functioning of neurons in different experimental studies. First, we discuss a nested mixture model to analyze how the activity of an individual cell is affected by visual stimuli that vary over time. Then, we move to a multivariate model to identify groups of co-activating neurons, where the information on the anatomical proximity is introduced to inform the clustering procedure. At the basis of both models is a simple but effective state-space formulation that describes the calcium dynamic and relates it to the latent firing events. Through these examples, we show how the Bayesian nonparametric framework is ideal for flexibly modeling the unobserved neuronal activity, identifying patterns of activity, borrowing information across experimental conditions, and including additional knowledge.