Task-driven neural network models predict neural dynamics of proprioception

Abstract

Proprioception tells the brain the state of the body based on distributed sensors in the body. However, the principles that govern proprioceptive processing from those distributed sensors are poorly understood. Here, we employ a task-driven neural network modeling approach to investigate the neural code of proprioceptive neurons in both cuneate nucleus (CN) and somatosensory cortex area 2 (S1). We simulated muscle spindle signals through musculoskeletal modeling and generated a large-scale, naturalistic movement repertoire to train thousands of neural network models on 16 behavioral tasks, each reflecting a hypothesis about the neural computations of the ascending proprioceptive pathway. We found that the networks internal representations developed through task-optimization generalize from synthetic data to predict single-trial neural activity in CN and S1 of primates performing center-out reaching. Task-driven models outperform linear encoding models and data-driven models. Behavioral tasks, which aim to predict the limb position and velocity were the best to predict the neural activity in both areas. Architectures that are better at solving the tasks are also better at predicting the neural data. Last, since task-optimization develops representations that better predict neural activity during active but not passively generated movements, we hypothesize that neural activity in CN and S1 is top-down modulated during goal-directed movements.