Interpreting Neural Population Activity During Feedback Motor Control

Abstract

The motor system routinely generates a multitude of fast, accurate, and elegant movements.In large part, this capacity is enabled by closed-loop feedback control systems in the brain.Brain-machine interfaces (BMIs), which translate neural activity into control signals for drivingprosthetic devices, also engage the brain’s feedback control systems and offer a promisingexperimental paradigm for studying the neural basis of feedback motor control. Here, we addressboth the engineering challenges facing current BMI systems and the basic science opportunitiesafforded by them.Previous studies have demonstrated reliable control of the direction of movement in cursorbasedBMI systems. However, control of movement speed has been notably deficient. We providean explanation for these observed difficulties based on neurophysiological studies of armreaching. These findings inspired our design of a novel BMI decoding algorithm, the speeddampeningKalman filter (SDKF) that automatically slows the cursor upon detecting changesin decoded movement direction. SDKF improved success rates by a factor of 1.7 relative to astandard Kalman filter in a closed-loop BMI task requiring stable stops at targets.Next, we transition toward leveraging the BMI paradigm for basic scientific studies of feedbackmotor control. It is widely believed that the brain employs internal models to describe ourprior beliefs about how an effector responds to motor commands. We developed a statisticalframework for extracting a subject’s internal model from neural population activity. We discoveredthat a mismatch between the actual BMI and the subjects internal model of the BMI explainsroughly 65% of movement errors. We also show that this internal model mismatch limits movementspeed dynamic range and may contribute toward the aforementioned known difficulties incontrol of BMI movement speed.