Computational toolbox for large scale Calcium Imaging Analysis, including movie handling, motion correction, source extraction, spike deconvolution and result visualization.
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Updated
Aug 21, 2024 - Python
Computational toolbox for large scale Calcium Imaging Analysis, including movie handling, motion correction, source extraction, spike deconvolution and result visualization.
Accurate denoising of voltage imaging data through statistically unbiased prediction, Nature Methods.
High level pandas-based API for batch analysis of Calcium Imaging data using CaImAn
Platform for Calcium Imaging analysis. DEPRECATED.
a Python machine learning library for neurophysiology data
Fast, Simple Calcium Imaging Segmentation with Fully Convolutional Networks (https://arxiv.org/abs/1707.06314)
SamuROI - Structured analysis of multiple user-defined ROIs, an open source Python-based analysis environment for imaging data.
Real-time, interactive exploration of 3D image stacks
HNCcorr algorithm for neuron identification in calcium imaging datasets.
a Python toolbox for reproducible analysis and visualization of NeuroPhysiological data.
Codes related to paper "Automated sleep stage classification of wide-field calcium imaging data via multiplex visibility graphs and deep learning"
Mesmerize plugin for the napari viewer
Experimentlal pure-python API to read Inscopix data
Calcium Imaging Analyzer (CaImAn) consists of a bunch of scripts written in python and put together to speed the analysis of calcium imaging experiments performed with the NIS-Elements Advanced Research software from Nikon.
Calcium Imaging Data ANalysis software
GUI for visualizing CaImAn processed imaging data.
Synthesising Realistic Calcium Imaging Data of Neuronal Populations Using GAN.
Spatio-temporal image restoration with entropy-based regularization for application to low-SNR fluorescence microscopy image series.
A collection of useful methods for handling, processing, and visualizing calcium imaging data.
Acquisition and analysis of Ca++ imaging and behavior video streams without academic opportunism
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