| --- |
| language: |
| - en |
| pipeline_tag: tabular-classification |
| tags: |
| - Computational Neuroscience |
| license: mit |
| --- |
| |
| ## Model description |
| This model is part of the `UnitRefine` project and it is a direct port of [this model](https://huggingface.co/AnoushkaJain3/noise_neural_classifier). |
| The model is trained on 11 mice in V1, SC, and ALM using Neuropixels on mice. |
| Each recording was labeled by at least two people and in different combinations. |
| The agreement amongst labelers is 80%. |
|
|
| # Intended use |
| Used to identify noise clusters automatically in SpikeInterface. |
|
|
| # How to Get Started with the Model |
| This can be used to automatically identify noise in spike-sorted outputs. If you have a sorting_analyzer, it can be used as follows: |
| |
| ``` python |
| from spikeinterface.curation import auto_label_units |
| |
| labels = auto_label_units( |
| sorting_analyzer=sorting_analyzer, |
| repo_id="SpikeInterface/UnitRefine_noise_neural_classifier", |
| trust_model=True |
| ) |
| ``` |
| ## 📜 Citation |
| |
| If you find [UnitRefine](https://github.com/anoushkajain/UnitRefine) models useful in your research, please cite the following DOI: |
| **[10.6084/m9.figshare.28282841.v2](https://doi.org/10.6084/m9.figshare.28282841.v2)**. |
|
|
| We will be releasing a **preprint soon**. In the meantime, please use the above DOI for referencing. |
|
|
| ## 🔗 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. 🚀 |
|
|
| # Authors |
|
|
| Anoushka Jain and Chris Halcrow |
|
|
