Update README.md

README.md

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 - spikeinterface
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+# 🧠 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:
+
+```python
+from spikeinterface.curation import auto_label_units
+
+labels = auto_label_units(
+    sorting_analyzer=sorting_analyzer,
+    repo_id="AnoushkaJain3/UnitRefine-mice-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 **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