Converging integration between ascending proprioceptive inputs and the corticospinal tract motor circuit underlying skilled movement control

Future spinal reflex is embedded in primary motor cortex output

Mammals can execute intended limb movements despite the fact that spinal reflexes involuntarily modulate muscle activity. To generate appropriate muscle activity, the cortical descending motor output must coordinate with spinal reflexes, yet the underlying neural mechanism remains unclear. We simultaneously recorded activities in motor-related cortical areas, afferent neurons, and forelimb muscles of monkeys performing reaching movements. Motor-related cortical areas, predominantly primary motor cortex (M1), encode subsequent afferent activities attributed to forelimb movement. M1 also encodes a subcomponent of muscle activity evoked by these afferent activities, corresponding to spinal reflexes. Furthermore, selective disruption of the afferent pathway specifically reduced this subcomponent of muscle activity, suggesting that M1 output drives muscle activity not only through direct descending pathways but also through the "transafferent" pathway composed of descending plus subsequent spinal reflex pathways. Thus, M1 provides optimal motor output based on an internal forward model that prospectively computes future spinal reflexes.

Task-driven neural network models predict neural dynamics of proprioception

Proprioception tells the brain the state of the body based on distributed sensory neurons. Yet, the principles that govern proprioceptive processing are poorly understood. Here, we employ a task-driven modeling approach to investigate the neural code of proprioceptive neurons in cuneate nucleus (CN) and somatosensory cortex area 2 (S1). We simulated muscle spindle signals through musculoskeletal modeling and generated a large-scale movement repertoire to train neural networks based on 16 hypotheses, each representing different computational goals. We found that the emerging, task-optimized internal representations generalize from synthetic data to predict neural dynamics in CN and S1 of primates. Computational tasks that aim to predict the limb position and velocity were the best at predicting the neural activity in both areas. Since task optimization develops representations that better predict neural activity during active than passive movements, we postulate that neural activity in the CN and S1 is top-down modulated during goal-directed movements.