## Model description
The model is trained on recordings from 11 mice in the V1, SC, and ALM brain regions using Neuropixels probes.
Each recording was labeled by at least two independent annotators, with different combinations of labelers, achieving an 80% agreement rate.
This model utilizes a subset of metrics that are computationally efficient while maintaining robust classification performance.
Used to identify Noise clusters automatically in SpikeInterface.
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:
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_lightweight",
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.
