A learning–based approach to artificial sensory feedback leads to optimal integration
Identifieur interne : 001491 ( Main/Merge ); précédent : 001490; suivant : 001492A learning–based approach to artificial sensory feedback leads to optimal integration
Auteurs : Maria C. Dadarlat [États-Unis] ; Joseph E. O Oherty ; Philip N. Sabes [États-Unis]Source :
- Nature neuroscience [ 1097-6256 ] ; 2014.
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.
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DOI: 10.1038/nn.3883
PubMed: 25420067
PubMed Central: 4282864
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Dadarlat</name><affiliation><nlm:aff id="A1">Department of Physiology, Center for Integrative Neuroscience, and UC Berkeley–UCSF Center for Neural Engineering and Prosthetics</nlm:aff><wicri:noCountry code="subfield">and UC Berkeley–UCSF Center for Neural Engineering and Prosthetics</wicri:noCountry></affiliation><affiliation wicri:level="3"><nlm:aff id="A2">UC Berkeley–UCSF Joint Graduate Program in Bioengineering University of California, San Francisco</nlm:aff><country>États-Unis</country><placeName><settlement type="city">San Francisco</settlement><region type="state">Californie</region></placeName><wicri:orgArea>UC Berkeley–UCSF Joint Graduate Program in Bioengineering University of California</wicri:orgArea></affiliation></author><author><name sortKey="O Oherty, Joseph E" sort="O Oherty, Joseph E" uniqKey="O Oherty J" first="Joseph E." last="O Oherty">Joseph E. 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Dadarlat</name><affiliation><nlm:aff id="A1">Department of Physiology, Center for Integrative Neuroscience, and UC Berkeley–UCSF Center for Neural Engineering and Prosthetics</nlm:aff><wicri:noCountry code="subfield">and UC Berkeley–UCSF Center for Neural Engineering and Prosthetics</wicri:noCountry></affiliation><affiliation wicri:level="3"><nlm:aff id="A2">UC Berkeley–UCSF Joint Graduate Program in Bioengineering University of California, San Francisco</nlm:aff><country>États-Unis</country><placeName><settlement type="city">San Francisco</settlement><region type="state">Californie</region></placeName><wicri:orgArea>UC Berkeley–UCSF Joint Graduate Program in Bioengineering University of California</wicri:orgArea></affiliation></author><author><name sortKey="O Oherty, Joseph E" sort="O Oherty, Joseph E" uniqKey="O Oherty J" first="Joseph E." last="O Oherty">Joseph E. O Oherty</name><affiliation><nlm:aff id="A1">Department of Physiology, Center for Integrative Neuroscience, and UC Berkeley–UCSF Center for Neural Engineering and Prosthetics</nlm:aff><wicri:noCountry code="subfield">and UC Berkeley–UCSF Center for Neural Engineering and Prosthetics</wicri:noCountry></affiliation></author><author><name sortKey="Sabes, Philip N" sort="Sabes, Philip N" uniqKey="Sabes P" first="Philip N." last="Sabes">Philip N. Sabes</name><affiliation><nlm:aff id="A1">Department of Physiology, Center for Integrative Neuroscience, and UC Berkeley–UCSF Center for Neural Engineering and Prosthetics</nlm:aff><wicri:noCountry code="subfield">and UC Berkeley–UCSF Center for Neural Engineering and Prosthetics</wicri:noCountry></affiliation><affiliation wicri:level="3"><nlm:aff id="A2">UC Berkeley–UCSF Joint Graduate Program in Bioengineering University of California, San Francisco</nlm:aff><country>États-Unis</country><placeName><settlement type="city">San Francisco</settlement><region type="state">Californie</region></placeName><wicri:orgArea>UC Berkeley–UCSF Joint Graduate Program in Bioengineering University of California</wicri:orgArea></affiliation></author></analytic><series><title level="j">Nature neuroscience</title><idno type="ISSN">1097-6256</idno><idno type="eISSN">1546-1726</idno><imprint><date when="2014">2014</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p id="P1">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. 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