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
Oct 8, 2025 - Python
Computational toolbox for large scale Calcium Imaging Analysis, including movie handling, motion correction, source extraction, spike deconvolution and result visualization.
Complete Matlab pipeline for large scale calcium imaging data analysis
Matlab routines for online non-rigid motion correction of calcium imaging data
Calibrated inference of spiking from calcium ΔF/F data using deep networks
CIAtah (pronounced cheetah): a software package for calcium imaging analysis of one- and two-photon imaging datasets. Documentation: https://git.io/ciatah_docs. Formerly known as calciumImagingAnalysis (ciapkg).
High level pandas-based API for batch analysis of Calcium Imaging data using CaImAn
Accurate denoising of voltage imaging data through statistically unbiased prediction, Nature Methods.
Platform for Calcium Imaging analysis. DEPRECATED.
The Zebrafish Activity Prediction Benchmark measures progress on the problem of predicting cellular-resolution neural activity throughout an entire vertebrate brain.
utility functions for analyzing (fly) calcium imaging data
Fast, Simple Calcium Imaging Segmentation with Fully Convolutional Networks (https://arxiv.org/abs/1707.06314)
a Python machine learning library for neurophysiology data
Seeded Iterative Demixing (SID) of neuronal activity recordings made using Light Field Microscopy (LFM)
SamuROI - Structured analysis of multiple user-defined ROIs, an open source Python-based analysis environment for imaging data.
Code and tutorials for fitting generalized linear models (GLM) in Tensorflow 2
Bonsai package for controlling headborne, calcium imaging miniscopes
Official code for BEAR. "Efficient neural network approximation of robust PCA for automated analysis of calcium imaging data", MICCAI 2021.
Flow-Registration toolbox for 2P motion compensation
Analysis and characterisation of cells within the gut wall using deep learning models. The current focus is on studying enteric neurons and enteric glia.
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