## 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%.
Used to identify noise clusters automatically in SpikeInterface.
This can be used to automatically identify noise in spike-sorted outputs. If you have a sorting_analyzer, it can be used as follows:
1 from spikeinterface.curation import auto_label_units
2 labels = auto_label_units(
3 sorting_analyzer = sorting_analyzer,
4 repo_id = "AnoushkaJain3/noise_neural_classifier",
5 trusted = ['numpy.dtype']
6 )
If you find
UnitRefine models useful in your research, please cite the following DOI:
10.6084/m9.figshare.28282841.v2.
