| | --- |
| | tags: |
| | - neuroscience, |
| | - spike-sorting |
| | - electrophysiology |
| | - mouse |
| | - neuropixels |
| | - scikit-learn |
| | - spikeinterface |
| | --- |
| | |
| | # 🧠 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: |
| |
|
| | ```python |
| | 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](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 **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 |
