Analysis Software

Guided Photometry Analysis in Python, a free and open-source fiber photometry data analysis tool developed in collaboration with the Lerner Lab at Northwestern University. GuPPy provides researchers with a comprehensive pipeline for processing and analyzing fiber photometry data, including artifact correction, signal normalization, peak detection, and visualization capabilities. The Lerner Lab leads the scientific development and analysis features, while our team at CatalystNeuro focuses on the software engineering foundation that makes GuPPy sustainable and accessible. We are streamlining installation and distribution, implementing automated quality assurance, improving reliability and error handling, and enabling seamless data sharing across research platforms.

Neo is a package for reading electrophysiology data in Python, writing data, and manipulating them. Neo implements a hierarchical data model well adapted to intracellular and extracellular electrophysiology. We use neo as part of our data conversion pipeline, and contribute to it, ensuring it continues to efficiently read the latest modern acquisition data formats. We also collaboratively manage a library of test electrophysiology files that was started by the neo development team and is now also used by SpikeInterface and NeuroConv.

A unified framework for spike sorting analysis and comparison. We contribute to this community-driven project that aims to standardize and simplify spike sorting workflows.

A deep learning framework for behavioral clustering and analysis. We contribute to this open-source project that enables automated analysis of animal behavior from video data.

A platform for volumetric segmentation of calcium imaging data. We contribute to this collaborative project that provides tools for analyzing and processing large-scale neural imaging datasets.