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--- |
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tags: |
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- neuroscience, |
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- spike-sorting |
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- electrophysiology |
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- mouse |
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- neuropixels |
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- scikit-learn |
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- spikeinterface |
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--- |
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# 🧠 UnitRefine Monkey SUA Classifier |
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## 📌 Model Summary |
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This model is part of the **UnitRefine** pipeline and is trained to classify **single-unit activity (SUA)** in **monkey Utah array recordings**. It uses supervised machine learning to distinguish well-isolated units from multi-unit activity (MUA) and noise based on unit-level spike metrics. |
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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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The training data includes recordings from **Dr.Sonja Gruen's Lab**(in FZJ). |
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--- |
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## 🔍 Use Cases |
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- Automated post-processing of spike sorting output |
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- Removing low-quality or noisy units prior to analysis |
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- Reducing manual curation effort in large-scale neural recordings |
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- Benchmarking unit quality metrics against expert annotations |
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## 🧬 Metric Selection |
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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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--- |
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## 💡 How to Use |
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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: |
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```python |
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from spikeinterface.curation import auto_label_units |
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labels = auto_label_units( |
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sorting_analyzer=sorting_analyzer, |
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repo_id="AnoushkaJain3/UnitRefine-monkey-sua-classifier", |
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trusted=["numpy.dtype"] |
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) |
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``` |
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This returns a dictionary of predicted labels per unit (1 = SUA, 0 = MUA/Noise). |
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## 📜 Citation |
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If you find [UnitRefine](https://github.com/anoushkajain/UnitRefine) models useful in your research, please cite: **[biorxiv paper](https://www.biorxiv.org/content/10.1101/2025.03.30.645770v1.full.pdf)**. |
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## 🔗 Resources |
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- **GitHub Repository:** [UnitRefine](https://github.com/anoushkajain/UnitRefine) |
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- 📖 **SpikeInterface Tutorial – Automated Curation:** |
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[View Here](https://spikeinterface.readthedocs.io/en/latest/tutorials_custom_index.html#automated-curation-tutorials) |
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UnitRefine is **fully integrated with SpikeInterface**, making it easy to incorporate into existing workflows. 🚀 |
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## 🙏 Acknowledgments |
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Special thanks to **Sonja Gruen**, and **Aitor Morales-Gregorio** for generously providing the datasets used to train and evaluate this model. |
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--- |
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## 👩🔬 Authors |
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**Anoushka Jain** |
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PhD Researcher, Musall Lab, Forschungszentrum Jülich |
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**Chris Halcrow** |
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Lead Developer, SpikeInterface |
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