  Calcium imaging is an essential tool to study the activity of neuronal
populations. However, the high level of background fluorescence in images
hinders the accurate identification of neurons and the extraction of neuronal
activities. While robust principal component analysis (RPCA) is a promising
method that can decompose the foreground and background in such images, its
computational complexity and memory requirement are prohibitively high to
process large-scale calcium imaging data. Here, we propose BEAR, a simple
bilinear neural network for the efficient approximation of RPCA which achieves
an order of magnitude speed improvement with GPU acceleration compared to the
conventional RPCA algorithms. In addition, we show that BEAR can perform
foreground-background separation of calcium imaging data as large as tens of
gigabytes. We also demonstrate that two BEARs can be cascaded to perform
simultaneous RPCA and non-negative matrix factorization for the automated
extraction of spatial and temporal footprints from calcium imaging data. The
source code used in the paper is available at https://github.com/NICALab/BEAR.
