Journal
PLOS COMPUTATIONAL BIOLOGY
Volume 13, Issue 3, Pages -Publisher
PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pcbi.1005423
Keywords
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Funding
- Swiss National Science Foundation [P300P2_158428]
- National Institutes of Health (NIH) [2R01MH064537, R90DA023426]
- Simons Foundation [325171, 365002]
- Army Research Office (ARO) [MURI W911NF-12-1-0594]
- NIH BRAIN Initiative [R01 EB22913, R21 EY027592]
- Defense Advanced Research Projects Agency (DARPA) [N66001-15-C-4032]
- Google Faculty Research award
- Intelligence Advanced Research Projects Activity (IARPA) via Department of Interior/Interior Business Center (DoI/IBC) [D16PC00003, D16PC00008, D16PC00007]
- Swiss National Science Foundation (SNF) [P300P2_158428] Funding Source: Swiss National Science Foundation (SNF)
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Fluorescent calcium indicators are a popular means for observing the spiking activity of large neuronal populations, but extracting the activity of each neuron from raw fluorescence calcium imaging data is a nontrivial problem. We present a fast online active set method to solve this sparse non-negative deconvolution problem. Importantly, the algorithm progresses through each time series sequentially from beginning to end, thus enabling real-time online estimation of neural activity during the imaging session. Our algorithm is a generalization of the pool adjacent violators algorithm (PAVA) for isotonic regression and inherits its linear-time computational complexity. We gain remarkable increases in processing speed: more than one order of magnitude compared to currently employed state of the art convex solvers relying on interior point methods. Unlike these approaches, our method can exploit warm starts; therefore optimizing model hyperparameters only requires a handful of passes through the data. A minor modification can further improve the quality of activity inference by imposing a constraint on the minimum spike size. The algorithm enables realtime simultaneous deconvolution of O(10(5)) traces of whole-brain larval zebrafish imaging data on a laptop.
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