A learning–based approach to artificial sensory feedback leads to optimal integration

Abstract

Proprioception—the sense of the body’s position in space—plays an important role in natural movement planning and execution and will likewise be necessary for successful motor prostheses and Brain–Machine Interfaces (BMIs). Here, we demonstrated that monkeys could learn to use an initially unfamiliar multi–channel intracortical microstimulation (ICMS) signal, which provided continuous information about hand position relative to an unseen target, to complete accurate reaches. Furthermore, monkeys combined this artificial signal with vision to form an optimal, minimum–variance estimate of relative hand position. These results demonstrate that a learning–based approach can be used to provide a rich artificial sensory feedback signal, suggesting a new strategy for restoring proprioception to patients using BMIs as well as a powerful new tool for studying the adaptive mechanisms of sensory integration.