CaImAn: open source scalable algorithms for calcium and voltage imaging data

Advances in fluorescence microscopy enable monitoring larger brain areas in-vivo with finer time resolution. The resulting data rates require reproducible analysis pipelines that are reliable, fully automated, and scalable to datasets generated over the course of months. We present CaImAn, an open-source library for calcium and voltage imaging data analysis. CaImAn provides automatic and scalable methods to address problems common to pre-processing, including motion correction, neural activity identification, and registration across different sessions of data collection. It does this while requiring minimal user intervention, with good scalability on computers ranging from laptops to high-performance computing clusters. CaImAn is suitable for two-photon and one-photon imaging, and also enables real-time analysis on streaming data.

Project Author(s)

Andrea Giovannucci; Eftychios Pnevmatikakis; Johannes Friedrich; Pat Gunn; Changjia Cai; Cynthia Dong; Mitya Chklovskii

Project Video

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This post was automatically generated by Andrea Giovannucci

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