Abstract
The combination of two-photon microscopy recordings and powerful calcium-dependent fluorescent sensors enables simultaneous recording of unprecedentedly large populations of neurons. While these sensors have matured over several generations of development, computational methods to process their fluorescence remain inefficient and the results hard to interpret. Here, we introduce a set of practical methods based on novel clustering algorithms, and provide a complete pipeline from raw image data to neuronal calcium traces to inferred spike times. We formulate a generative model of the fluorescence image, incorporating spike times and a spatially smooth neuropil signal, and solve the inference and learning problems using a fast algorithm. This implementation scales linearly with the number of recorded cells, and the complete pipeline runs in approximately one hour for typical two-hour long recordings, on commodity GPUs. Furthermore, this method recovers twice as many cells as a previous standard method. This allowed us to routinely record and detect ~10,000 cells simultaneously from the visual cortex of awake mice using standard two-photon resonant-scanning microscopes. The software is publicly available at github.com/cortex-lab/Suite2P, together with a graphical user interface that allows rapid manual curation of the results.
