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---
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language:
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- en
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pipeline_tag: tabular-classification
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tags:
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- Computational Neuroscience
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license: mit
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---
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## Model description
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This model is part of the UnitRefine project and it is a direct port of [this model](https://huggingface.co/AnoushkaJain3/sua_mua_classifier_lightweight).
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The model is trained on recordings from 11 mice in the V1, SC, and ALM brain regions using Neuropixels probes.
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Each recording was labeled by at least two independent annotators, with different combinations of labelers, achieving an 80% agreement rate.
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This model utilizes a subset of metrics that are computationally efficient while maintaining robust classification performance.
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# Intended use
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Used to identify Noise clusters automatically in SpikeInterface.
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# How to Get Started with the Model
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This can be used to automatically identify SUA units in spike-sorted outputs. If you have a sorting_analyzer, it can be used as follows:
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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 = "SpikeInterface/UnitRefine_sua_mua_classifier_lightweight",
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trusted = ['numpy.dtype']
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)
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```
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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 the following DOI:
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**[10.6084/m9.figshare.28282841.v2](https://doi.org/10.6084/m9.figshare.28282841.v2)**.
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We will be releasing a **preprint soon**. In the meantime, please use the above DOI for referencing.
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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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# Authors
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Anoushka Jain and Chris Halcrow
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