title: A learning-based approach to artificial sensory feedback leads to optimal integration.
abstract: Proprioception-the sense of the body's position in space-is important to natural movement planning and execution and will likewise be necessary for successful motor prostheses and brain-machine interfaces (BMIs). Here we demonstrate that monkeys were able to learn to use an initially unfamiliar multichannel intracortical microstimulation 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.
authors.affiliation: 1] Department of Physiology, University of California, San Francisco, California, USA. [2] Center for Integrative Neuroscience, University of California, San Francisco, California, USA. [3] UC Berkeley-UCSF Center for Neural Engineering and Prosthetics, University of California, San Francisco, California, USA. [4] UC Berkeley-UCSF Graduate Program in Bioengineering, University of California, San Francisco, California, USA.
authors.forename: Maria C
authors.initials: MC
authors.lastname: Dadarlat
authors.affiliation: 1] Department of Physiology, University of California, San Francisco, California, USA. [2] Center for Integrative Neuroscience, University of California, San Francisco, California, USA. [3] UC Berkeley-UCSF Center for Neural Engineering and Prosthetics, University of California, San Francisco, California, USA.
authors.forename: Joseph E
authors.identifier: 0000000181755699
authors.initials: JE
authors.lastname: O'Doherty
authors.affiliation: 1] Department of Physiology, University of California, San Francisco, California, USA. [2] Center for Integrative Neuroscience, University of California, San Francisco, California, USA. [3] UC Berkeley-UCSF Center for Neural Engineering and Prosthetics, University of California, San Francisco, California, USA. [4] UC Berkeley-UCSF Graduate Program in Bioengineering, University of California, San Francisco, California, USA.
authors.forename: Philip N
authors.identifier: 0000000183976225
authors.initials: PN
authors.lastname: Sabes
mesh_terms: D000818:Animals; D001522:Behavior, Animal / Q000502:physiology; D062207:Brain-Computer Interfaces; D003216:Conditioning, Operant / Q000502:physiology; D030141:Feedback, Psychological / Q000502:physiology*; D007858:Learning / Q000502:physiology*; D008253:Macaca mulatta; D008297:Male; D010775:Photic Stimulation; D011597:Psychomotor Performance / Q000502:physiology; D012677:Sensation / Q000502:physiology*; D013003:Somatosensory Cortex / Q000502:physiology; D014796:Visual Perception / Q000502:physiology