Real-Time Selective Markerless Tracking of Forepaws of Head Fixed Mice Using Deep Neural Networks
Brandon J. Forys, Dongsheng Xiao, Pankaj Gupta, Timothy H. Murphy
Abstract
Here, we describe a system capable of tracking specific mouse paw movements at high frame rates (70.17 Hz) with a high level of accuracy (mean = 0.95, SD < 0.01). Short-latency markerless tracking of specific body parts opens up the possibility of manipulating motor feedback. We present a software and hardware scheme built on DeepLabCut—a robust movement-tracking deep neural network framework—which enables real-time estimation of paw and digit movements of mice. Using this approach, we demonstrate movement-generated feedback by triggering a USB-GPIO (general-purpose input/output)-controlled LED when the movement of one paw, but not the other, selectively exceeds a preset threshold. The mean time delay between paw movement initiation and LED flash was 44.41 ms (SD = 36.39 ms), a latency sufficient for applying behaviorally triggered feedback. We adapt DeepLabCut for real-time tracking as an open-source package we term DeepCut2RealTime. The ability of the package to rapidly assess animal behavior was demonstrated by reinforcing specific movements within water-restricted, head-fixed mice. This system could inform future work on a behaviorally triggered “closed loop” brain–machine interface that could reinforce behaviors or deliver feedback to brain regions based on prespecified body movements.