UnitRefine-generalized-sua-classifier

🧠 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.

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🔍 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

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🧬 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.

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💡 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.

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👩‍🔬 Authors

Anoushka Jain
PhD Researcher, Musall Lab, Forschungszentrum Jülich

Chris Halcrow
Lead Developer, SpikeInterface