Year: 2017
Type: article
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.
Source: PLoS Computational Biology
Language: English
Subfield: Biophysics
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $3,043
Funders: National Science Foundation, U.S. Department of the Interior, Simons Foundation, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, National Institutes of Health, Defense Advanced Research Projects Agency, Advanced Research Projects Agency, Intelligence Advanced Research Projects Activity, Interior Business Center, Multidisciplinary University Research Initiative, National Institute of Mental Health, National Institute on Drug Abuse, National Eye Institute, National Institute of Biomedical Imaging and Bioengineering, Army Research Office
Awards: G1037014486, G1046215736, G1242600127, G1475064446, G2876363574, G2955376837, Analysis of Nonstationary Point Process Data, G3724729781, G3761563761, G4012432327, Next-Generation Calcium Imaging Analysis Methods, G4530418632, INTERDISCIPLINARY TRAINING IN COMPUTATIONAL NEUROSCIENCE, G6224274980, Large scale approximate Bayesian inference of neural dynamics and connectivity from calcium imaging, G671019192, G6743103672, G6960262275, G7452299184, G756539269, G7714040253, Optimal calcium imaging with shaped excitation, G978781625
OpenAlex ID: W2595012769