{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/fast-online-deconvolution-of-calcium-imaging","title":"Fast Online Deconvolution of Calcium Imaging Data","arxiv_id":"1609.00639","date":"2017-03-16","proceeding":null,"authors":[],"abstract":"Fluorescent calcium indicators are a popular means for observing the spiking\nactivity of large neuronal populations, but extracting the activity of each\nneuron from raw fluorescence calcium imaging data is a nontrivial problem. We\npresent a fast online active set method to solve this sparse non-negative\ndeconvolution problem. Importantly, the algorithm progresses through each time\nseries sequentially from beginning to end, thus enabling real-time online\nestimation of neural activity during the imaging session. Our algorithm is a\ngeneralization of the pool adjacent violators algorithm (PAVA) for isotonic\nregression and inherits its linear-time computational complexity. We gain\nremarkable increases in processing speed: more than one order of magnitude\ncompared to currently employed state of the art convex solvers relying on\ninterior point methods. Unlike these approaches, our method can exploit warm\nstarts; therefore optimizing model hyperparameters only requires a handful of\npasses through the data. A minor modification can further improve the quality\nof activity inference by imposing a constraint on the minimum spike size. The\nalgorithm enables real-time simultaneous deconvolution of $O(10^5)$ traces of\nwhole-brain larval zebrafish imaging data on a laptop.","url_abs":"http://arxiv.org/abs/1609.00639v3","url_pdf":"http://arxiv.org/pdf/1609.00639v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"fast-online-deconvolution-of-calcium-imaging","repo_url":"https://github.com/j-friedrich/OASIS","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}