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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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The model is trained on 11 mice in V1, SC, and ALM using Neuropixels on mice. |
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Each recording was labeled by at least two people and in different combinations. |
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The agreement amongst labelers is 80%. |
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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 noise 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 = "AnoushkaJain3/noise_neural_classifier", |
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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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