The ultimate function of the brain is to produce behavior. How primates generate appropriate motor commands driving sophisticated skeletomuscular system in response to signals from the complex and dynamic environment is a central challenge in neuroscience. By combining behavioral, neurophysiological, and computational approaches, our lab has sought to decipher the neural codes underlying predictive sensorimotor control. Ongoing research has provided direct neurophysiological evidence in parietal cortex in support for the longstanding forward model.

  Because sensorimotor behavior arises from concerted population activity in a collection of interconnected brain areas, sensorimotor signals embodied in the parietal cortex must also rely on other cortical and subcortical regions. In future experiments, we plan not only to extend neural recording to the motor cortex and cerebellum, but also will chronically record large-scale neuronal populations in those areas via implanted micro-electrode arrays and/or two-photon optical imaging, to elucidate fundamental mechanisms underlying sensorimotor coding and learning. Moreover, we will further dissociate sensory vs motor variables by introducing virtual-reality and even examine the causal roles of each of these areas with experimental interventions, such as reversible inactivation and optogenetics.

  To elucidate the neural basis of motor control at the mechanistic level also will help neuroprosthetics and brain-inspired robotics. In future experiments, we will not only decode population neuronal activity recorded from monkeys to control robotic arms in real-time, but also will collaborate with clinicians to implement brain-machine interface in patients with brain disorders affecting motor control. In collaboration with experts in artificial intelligence and machine learning, we will also apply contemporary neural principles of sensorimotor control to the development of next generation of intelligent robots.

CUI He, Ph.D.