Flexible Modulation of Neural Variance Facilitates Neuroprosthetic Skill Learning

Abstract

One hallmark of natural motor control is the brains ability to adapt to perturbations ranging from temporary visual-motor rotations to paresis caused by stroke. These adaptations require modifications of learned neural patterns that can span the time-course of minutes to months. Previous work with brain-machine interfaces (BMI) has shown that over learning, neurons consolidate firing activity onto low-dimensional neural subspaces, and additional studies have shown that neurons require longer timescales to adapt to task perturbations that require neural activity outside of these subspaces. However, it is unclear how the motor cortex adapts alongside task changes that do not require modifications of the existing neural subspace over learning. To answer this question, five nonhuman primates were used in three BMI experiments, which allowed us to track how specific populations of neurons changed firing patterns as task performance improved. In each experiment, neural activity was transformed into cursor kinematics using decoding algorithms that were periodically readapted based on natural arm movements or visual feedback. We found that decoder changes caused neurons to increase exploratory-like patterns on within-day timescales without hindering previously consolidated patterns regardless of task performance. The flexible modulation of these exploratory patterns in contrast to relatively stable consolidated activity suggests a simultaneous exploration-exploitation strategy that adapts existing neural patterns during learning.