🧠 UnitRefine Generalized SUA Classifier
📌 Model Summary
This model is part of the UnitRefine pipeline and is trained to classify single-unit activity (SUA) in across multiple species: mice, mole-rats, monkeys, and humans. It uses supervised machine learning to distinguish well-isolated units from multi-unit activity (MUA) and noise.
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.
🔍 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:
from spikeinterface.curation import auto_label_units
labels = auto_label_units(
sorting_analyzer=sorting_analyzer,
repo_id="AnoushkaJain3/UnitRefine-generalized-sua-classifier",
trusted=["numpy.dtype"]
)
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 Dr. Florian Mormann, Dr. Xiaonan Richard Sun, Yeonglong (Albert) Ay and Alana Darcher 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