@article{
 title = {Fast online deconvolution of calcium imaging data},
 type = {article},
 year = {2017},
 identifiers = {[object Object]},
 pages = {e1005423},
 volume = {13},
 websites = {http://dx.plos.org/10.1371/journal.pcbi.1005423},
 month = {3},
 publisher = {Public Library of Science},
 day = {14},
 editors = {[object Object]},
 id = {65dbb482-19de-3465-8019-1017cf8fdf5f},
 created = {2018-06-07T08:10:35.550Z},
 accessed = {2018-04-25},
 file_attached = {true},
 profile_id = {e787ec00-39cf-30cd-a68c-ccd643c8d986},
 group_id = {b270a220-fc7f-33a3-904e-6f597737971c},
 last_modified = {2018-06-07T08:10:35.853Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {false},
 hidden = {false},
 folder_uuids = {60c56f25-72bb-4cf6-9eab-e1c39e52d95c},
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 abstract = {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 3progresses 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 real-time simultaneous deconvolution of O(105) traces of whole-brain larval zebrafish imaging data on a laptop.},
 bibtype = {article},
 author = {Friedrich, Johannes and Zhou, Pengcheng and Paninski, Liam},
 journal = {PLOS Computational Biology},
 number = {3}
}

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