🧠 UnitRefine Mice SUA Classifier
📌 Model Summary
This model is part of the UnitRefine pipeline and is trained to classify single-unit activity (SUA) in mouse Neuropixels recordings. It uses supervised machine learning to distinguish well-isolated units from multi-unit activity (MUA) and noise based on unit-level spike metrics.
The classifier is designed for fast, automated unit curation, and generalizes across multiple recordings and brain regions, achieving high accuracy even with limited training data.
The training data includes recordings from the Allen Institute for Neural Dynamics, International Brain Laboratory, and Musall Lab.
🔍 Use Cases
- Automated post-processing of spike sorting output
- Removing low-quality or noisy units prior to analysis
- Reducing manual curation effort in large-scale neural recordings
- Benchmarking unit quality metrics against expert annotations
🧬 Metric Selection
For information on which spike metrics were used to train this classifier, please refer to the model_info.json file included in the repository.
💡 How to Use
This model can be used to automatically identify SUA units from spike-sorted data. If you are working with a SortingAnalyzer object, you can run the following:
1from spikeinterface.curation import auto_label_units
2
3labels = auto_label_units(
4 sorting_analyzer=sorting_analyzer,
5 repo_id="AnoushkaJain3/UnitRefine-mice-sua-classifier",
6 trusted=["numpy.dtype"]
7)
This returns a dictionary of predicted labels per unit (1 = SUA, 0 = MUA/Noise).
📜 Citation
If you find
UnitRefine models useful in your research, please cite:
biorxiv paper.
🔗 Resources
- GitHub Repository: UnitRefine
- 📖 SpikeInterface Tutorial – Automated Curation:
View Here
UnitRefine is fully integrated with SpikeInterface, making it easy to incorporate into existing workflows. 🚀
🙏 Acknowledgments
Special thanks to Alessio Buccino, Olivier Winter, and Alejandro Pan-Vazquez for generously providing the datasets used to train and evaluate this model.
👩🔬 Authors
Anoushka Jain
PhD Researcher, Musall Lab, Forschungszentrum Jülich
Chris Halcrow
Lead Developer, SpikeInterface
