[2103.07818] Quantifying uncertainty in spikes estimated from calcium imaging data Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Statistics > Methodology arXiv:2103.07818 (stat) [Submitted on 14 Mar 2021 ( v1 ), last revised 30 Jul 2021 (this version, v2)] Title: Quantifying uncertainty in spikes estimated from calcium imaging data Authors: Yiqun T. Chen , Sean W. Jewell , Daniela M. Witten View a PDF of the paper titled Quantifying uncertainty in spikes estimated from calcium imaging data, by Yiqun T. Chen and 2 other authors View PDF HTML (experimental) Abstract: In recent years, a number of methods have been proposed to estimate the times at which a neuron spikes on the basis of calcium imaging data. However, quantifying the uncertainty associated with these estimated spikes remains an open problem. We consider a simple and well-studied model for calcium imaging data, which states that calcium decays exponentially in the absence of a spike, and instantaneously increases when a spike occurs. We wish to test the null hypothesis that the neuron did not spike -- i.e., that there was no increase in calcium -- at a particular timepoint at which a spike was estimated. In this setting, classical hypothesis tests lead to inflated Type I error, because the spike was estimated on the same data used for testing. To overcome this problem, we propose a selective inference approach. We describe an efficient algorithm to compute finite-sample p-values that control selective Type I error, and confidence intervals with correct selective coverage, for spikes estimated using a recent proposal from the literature. We apply our proposal in simulation and on calcium imaging data from the spikefinder challenge. Comments: 52 pages, 12 Figures Subjects: Methodology (stat.ME) ; Applications (stat.AP) Cite as: arXiv:2103.07818 [stat.ME] (or arXiv:2103.07818v2 [stat.ME] for this version) https://doi.org/10.48550/arXiv.2103.07818 Focus to learn more arXiv-issued DOI via DataCite Related DOI : https://doi.org/10.1093/biostatistics/kxab034 Focus to learn more DOI(s) linking to related resources Submission history From: Yiqun Chen [ view email ] [v1] Sun, 14 Mar 2021 00:03:56 UTC (8,763 KB) [v2] Fri, 30 Jul 2021 00:02:34 UTC (16,660 KB) Full-text links: Access Paper: View a PDF of the paper titled Quantifying uncertainty in spikes estimated from calcium imaging data, by Yiqun T. Chen and 2 other authors View PDF HTML (experimental) TeX Source view license Current browse context: stat.ME < prev | next > new | recent | 2021-03 Change to browse by: stat stat.AP References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from