VneuroTK#
VneuroTK is a Python toolkit for visual neuroscience. It brings neural recordings, trial and stimulus metadata, and layer-level vision-model representations into a common workflow for research analysis.
The toolkit provides path abstractions and lazy loading for heterogeneous recording sources, explicit representations for continuous, epoched, and pattern data, standalone or recording-integrated vision feature extraction, and structured HDF5 persistence with extraction provenance.
Warning
Pre-alpha software
VneuroTK is under active development. Public APIs and the HDF5 schema may change before the first stable release. Pin versions and retain source data when using it in research workflows.
Explore the documentation#
Install the core toolkit and the optional integrations needed by your workflow.
Understand the VneuroTK HDF5 layout, persistence guarantees, and compatibility policy.
Learn individual tasks through notebook-native guides for paths, neural data, recording visualization, and DNN vision extraction.
Follow complete notebooks for data, visualization, and DNN vision workflows.
Look up the documented classes, functions, and modules.
Contribute, get support, cite the toolkit, and review project policies.
Visualization and DNN vision#
Visualization uses the optional viz extra and Matplotlib to plot stimulus timing and neural recordings. DNN vision uses vneurotk.vision and vision-backend extras to extract model representations from images. These are separate features and can be installed and used independently.
See the changelog for release history.