README.md

---
tags:
- neuroscience,
- spike-sorting
- electrophysiology
- mouse
- neuropixels
- scikit-learn
- spikeinterface
---

# 🧠 UnitRefine Monkey SUA Classifier

## 📌 Model Summary

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.

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 **Dr.Sonja Gruen's Lab**(in FZJ).

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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  

---

## 🧬 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:

```python
from spikeinterface.curation import auto_label_units

labels = auto_label_units(
    sorting_analyzer=sorting_analyzer,
    repo_id="AnoushkaJain3/UnitRefine-monkey-sua-classifier",
    trusted=["numpy.dtype"]
)
```
This returns a dictionary of predicted labels per unit (1 = SUA, 0 = MUA/Noise).


## 📜 Citation  

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)**.  


## 🔗 Resources  

- **GitHub Repository:** [UnitRefine](https://github.com/anoushkajain/UnitRefine)  
- 📖 **SpikeInterface Tutorial – Automated Curation:**  
  [View Here](https://spikeinterface.readthedocs.io/en/latest/tutorials_custom_index.html#automated-curation-tutorials)  

UnitRefine is **fully integrated with SpikeInterface**, making it easy to incorporate into existing workflows. 🚀  


## 🙏 Acknowledgments

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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## 👩‍🔬 Authors

**Anoushka Jain**  
PhD Researcher, Musall Lab, Forschungszentrum Jülich  

**Chris Halcrow**  
Lead Developer, SpikeInterface