Integration of learned artificial sensation with vision during freely moving navigation

Abstract

Humans rely on both proprioceptive and visual feedback during reaching, integrating these two sensory streams to improve movement accuracy and precision. Patients using a brain-computer interface will similarly require artificial proprioceptive feedback in addition to vision to finely control a prosthesis. Intracortical microstimulation (ICMS) elicits sensory perceptions that could replace the lost proprioceptive signal. However, some learning may be required for encoding artificial sensation, as current technology does not give access to neurons with all of the desired encoding properties. We developed a freely moving mouse behavioral task in which to test learning and integration of artificial sensory information with natural vision. Mice implanted with a 16-channel microwire array in the primary somatosensory cortex were trained to navigate to randomly selected targets upon the floor of a custom behavioral training chamber. Target location was encoded with visual and/or patterned multichannel ICMS feedback. Mice received multimodal feedback from the beginning of training of the behavioral task, achieving 75% on multimodal trials after approximately 1,000 training trials. Mice also quickly learned to use the ICMS signal to locate invisible targets, achieving 75% proficiency on ICMS-only trials when tested. Critically, we found that performance with ICMS was as good or better than performance with natural vision, and that performance on multimodal trials significantly exceeded unimodal performance (vision or ICMS), demonstrating that animals rapidly learned to integrate natural vision with artificial sensation.