CaImAn
A Python toolbox for large-scale calcium and voltage imaging data analysis, including motion correction, source extraction, and spike deconvolution.
What is CaImAn?
CaImAn is an open-source Python toolbox for analyzing large-scale calcium and voltage imaging data. It implements essential computational methods for processing fluorescence microscopy data, including motion correction, source extraction, spike deconvolution, and neuron registration across sessions. The toolbox addresses the challenge of extracting reliable neural activity signals from noisy, high-dimensional imaging datasets.
Neuroscience researchers and computational biologists working with calcium or voltage imaging data from two-photon or one-photon fluorescence microscopy. It's particularly valuable for labs needing scalable, reproducible analysis pipelines for large datasets.
CaImAn provides a unified, well-documented implementation of state-of-the-art algorithms published in leading neuroscience journals. Its scalability handles large datasets efficiently, supports both offline and online analysis modes, and offers multiple algorithm variants optimized for different experimental conditions and noise levels.
Overview
Computational toolbox for large scale Calcium Imaging Analysis, including movie handling, motion correction, source extraction, spike deconvolution and result visualization.
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