Optimizing Control and Learning in Neural Interfaces

Amy Orsborn

Direct interfaces with the brain provide exciting new ways to restore and repair neurological function. For instance, motor Brain-Machine Interfaces (BMIs) can bypass a paralyzed person’s injury by repurposing intact portions of their brain to control movements. Recent work shows that BMIs do not simply “decode” subjects’ intentions—they create new systems subjects learn to control. To improve BMI performance and usability, we must therefore understand how to optimize learning and control in these systems.