WhACC: Whisker Automatic Contact Classifier with Expert Human-Level Performance

Abstract

The rodent vibrissal system remains pivotal in advancing neuroscience research, particularly for studies of cortical plasticity, learning, decision-making, sensory encoding and sensorimotor integration. While this model system provides notable advantages for quantifying active tactile input, it is hindered by the labor-intensive process of curating touch events across millions of video frames. Even with the aid of automated tools like the Janelia Whisker Tracker, millisecond-accurate touch curation often requires >3 hours of manual review / million video frames. We address this limitation by introducing Whisker Automatic Contact Classifier (WhACC), a python package designed to identify touch periods from high-speed videos of head-fixed behaving rodents with human-level performance. For our model design, we train ResNet50V2 on whisker images and extract features. Next, we engineer features to improve performance with an emphasis on temporal consistency. Finally, we select only the most important features and use them to train a LightGBM classifier. Classification accuracy is assessed against three expert human curators on over one million frames. WhACC shows pairwise touch classification agreement on 99.5% of video frames, equal to between-human agreement. Additionally, comparison between an expert curator and WhACC on a holdout dataset comprising nearly four million frames and 16 single-unit electrophysiology recordings shows negligible differences in neural characterization metrics. Finally, we offer an easy way to select and curate a subset of data to adaptively retrain WhACC. Including this retraining step, we reduce human hours required to curate a 100 million frame dataset from [~]333 hours to [~]6 hours.