Rapid Integration of Artificial Sensory Feedback during Operant Conditioning of Motor Cortex Neurons

The role of forelimb motor cortex areas in goal directed action in mice

Mammalian motor cortex consists of several interconnected subregions thought to play distinct roles in voluntary movements, yet their specific role in decision making and execution is not completely elucidated. Here we used transient optogenetic inactivation of the caudal forelimb area (CFA) and rostral forelimb area (RFA) in mice as they performed a directional joystick task. Based on a vibrotactile cue applied to their forepaw, mice were trained to push or pull a joystick after a delay period. We found that choice and execution are temporally segregated processes. CFA and RFA were both essential during the stimulus delivery for correct choice and during the answer period for motor execution. Fine, distal motor deficits were restricted to CFA inactivation. Surprisingly, during the delay period neither area alone, but only combined inactivation was able to affect choice. Our findings suggest transient and partially distributed neural processing of choice and execution across different subregions of the motor cortex.

Parsing learning in networks using brain-machine interfaces

Brain-machine interfaces (BMIs) define new ways to interact with our environment and hold great promise for clinical therapies. Motor BMIs, for instance, re-route neural activity to control movements of a new effector and could restore movement to people with paralysis. Increasing experience shows that interfacing with the brain inevitably changes the brain. BMIs engage and depend on a wide array of innate learning mechanisms to produce meaningful behavior. BMIs precisely define the information streams into and out of the brain, but engage wide-spread learning. We take a network perspective and review existing observations of learning in motor BMIs to show that BMIs engage multiple learning mechanisms distributed across neural networks. Recent studies demonstrate the advantages of BMI for parsing this learning and its underlying neural mechanisms. BMIs therefore provide a powerful tool for studying the neural mechanisms of learning that highlights the critical role of learning in engineered neural therapies.