  In the past few years, new technologies in the field of neuroscience have
made it possible to simultaneously image activity in large populations of
neurons at cellular resolution in behaving animals. In mid-2016, a huge
repository of this so-called "calcium imaging" data was made
publicly-available. The availability of this large-scale data resource opens
the door to a host of scientific questions, for which new statistical methods
must be developed.
  In this paper, we consider the first step in the analysis of calcium imaging
data: namely, identifying the neurons in a calcium imaging video. We propose a
dictionary learning approach for this task. First, we perform image
segmentation to develop a dictionary containing a huge number of candidate
neurons. Next, we refine the dictionary using clustering. Finally, we apply the
dictionary in order to select neurons and estimate their corresponding activity
over time, using a sparse group lasso optimization problem. We apply our
proposal to three calcium imaging data sets.
  Our proposed approach is implemented in the R package scalpel, which is
available on CRAN.
