Modeling Sensorimotor Processing with Physics-Informed Neural Networks

Abstract

Proprioception is essential for planning and executing precise movements. Muscle spindles, the key mechanoreceptors for proprioception, are the principle sensory neurons enabling this process. Emerging evidence suggests spindles act as adaptable processors, modulated by gamma motor neurons to meet task demands. Yet, the specifics of this modulation remain unknown. Here, we present a novel, physics-informed neural network model that integrates biomechanics and neural dynamics to capture spindle function with high fidelity and efficiency, while maintaining computational tractability. Through validation across multiple experimental datasets and species, our model not only outperforms existing approaches but also reveals key drivers of variability in spindle responses, offering new insights into proprioceptive mechanisms.