Understanding how networks of neurons process information is one of the key challenges in modern neuroscience. A necessary step to achieving this goal is to be able to observe the dynamics of large populations of neurons over a large area of the brain. Light-?eld microscopy (LFM), which uses a type of scanless microscope, is a particularly attractive candidate for high-speed 3D imaging. It captures volumetric information in a single snapshot, allowing volumetric imaging at video frame rates. In this talk, we review fundamental aspects of LFM and then present computational methods based on generalized sampling theory and on physics-inspired deep learning for neuron localization and activity estimation. We also show how the unfolding technique which is an approach that allows embedding priors and models in the neural network architecture can be successfully employed in this context. We conclude by outlining opportunities for the computational imaging community to have an impact in this emerging research ?eld.
