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---
language:
- en
pipeline_tag: tabular-classification
tags:
- Computational Neuroscience
license: mit
---
## Model description
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 = "AnoushkaJain3/noise_neural_classifier",
trusted = ['numpy.dtype']
)
```
## 📜 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
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