  Calcium imaging has emerged as a workhorse method in neuroscience to
investigate patterns of neuronal activity. Instrumentation to acquire calcium
imaging movies has rapidly progressed and has become standard across labs.
Still, algorithms to automatically detect and extract activity signals from
calcium imaging movies are highly variable from~lab~to~lab and more advanced
algorithms are continuously being developed. Here we present HNCcorr, a novel
algorithm for cell identification in calcium imaging movies based on
combinatorial optimization. The algorithm identifies cells by finding distinct
groups of highly similar pixels in correlation space, where a pixel is
represented by the vector of correlations to a set of other pixels. The HNCcorr
algorithm achieves the best known results for the cell identification benchmark
of Neurofinder, and guarantees an optimal solution to the underlying
deterministic optimization model resulting in a transparent mapping from input
data to outcome.
