Creating new functional circuits for action via brain-machine interfaces
Brain-machine interfaces (BMIs) are an emerging technology with great promise for developing restorative therapies for those with disabilities. BMIs also create novel, well-defined functional circuits for action that are distinct from the natural sensorimotor apparatus. Closed-loop control of BMI sy...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2013-11-01
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| Series: | Frontiers in Computational Neuroscience |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fncom.2013.00157/full |
| _version_ | 1818007735824809984 |
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| author | Amy eOrsborn Jose M Carmena Jose M Carmena Jose M Carmena |
| author_facet | Amy eOrsborn Jose M Carmena Jose M Carmena Jose M Carmena |
| author_sort | Amy eOrsborn |
| collection | DOAJ |
| description | Brain-machine interfaces (BMIs) are an emerging technology with great promise for developing restorative therapies for those with disabilities. BMIs also create novel, well-defined functional circuits for action that are distinct from the natural sensorimotor apparatus. Closed-loop control of BMI systems can also actively engage learning and adaptation. These properties make BMIs uniquely suited to study learning of motor and non-physical, abstract skills. Recent work used motor BMIs to shed light on the neural representations of skill formation and motor adaptation. Emerging work in sensory BMIs, and other novel interface systems, also highlight the promise of using BMI systems to study fundamental questions in learning and sensorimotor control. This paper outlines the interpretation of BMIs as novel closed-loop systems and the benefits of these systems for studying learning. We review BMI learning studies, their relation to motor control, and propose future directions for this nascent field. Understanding learning in BMIs may both elucidate mechanisms of natural motor and abstract skill learning, and aid in developing the next generation of neuroprostheses. |
| first_indexed | 2024-04-14T05:19:33Z |
| format | Article |
| id | doaj.art-7f6c19d05d7843fc92fa5c7f5beac2b0 |
| institution | Directory Open Access Journal |
| issn | 1662-5188 |
| language | English |
| last_indexed | 2024-04-14T05:19:33Z |
| publishDate | 2013-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Computational Neuroscience |
| spelling | doaj.art-7f6c19d05d7843fc92fa5c7f5beac2b02022-12-22T02:10:15ZengFrontiers Media S.A.Frontiers in Computational Neuroscience1662-51882013-11-01710.3389/fncom.2013.0015747556Creating new functional circuits for action via brain-machine interfacesAmy eOrsborn0Jose M Carmena1Jose M Carmena2Jose M Carmena3University of California, BerkeleyUniversity of California, BerkeleyUniversity of California, BerkeleyUniversity of California, BerkeleyBrain-machine interfaces (BMIs) are an emerging technology with great promise for developing restorative therapies for those with disabilities. BMIs also create novel, well-defined functional circuits for action that are distinct from the natural sensorimotor apparatus. Closed-loop control of BMI systems can also actively engage learning and adaptation. These properties make BMIs uniquely suited to study learning of motor and non-physical, abstract skills. Recent work used motor BMIs to shed light on the neural representations of skill formation and motor adaptation. Emerging work in sensory BMIs, and other novel interface systems, also highlight the promise of using BMI systems to study fundamental questions in learning and sensorimotor control. This paper outlines the interpretation of BMIs as novel closed-loop systems and the benefits of these systems for studying learning. We review BMI learning studies, their relation to motor control, and propose future directions for this nascent field. Understanding learning in BMIs may both elucidate mechanisms of natural motor and abstract skill learning, and aid in developing the next generation of neuroprostheses.http://journal.frontiersin.org/Journal/10.3389/fncom.2013.00157/fullmotor learningneural plasticitybrain-machine interfacesvolitional controlsensorimotor systems |
| spellingShingle | Amy eOrsborn Jose M Carmena Jose M Carmena Jose M Carmena Creating new functional circuits for action via brain-machine interfaces Frontiers in Computational Neuroscience motor learning neural plasticity brain-machine interfaces volitional control sensorimotor systems |
| title | Creating new functional circuits for action via brain-machine interfaces |
| title_full | Creating new functional circuits for action via brain-machine interfaces |
| title_fullStr | Creating new functional circuits for action via brain-machine interfaces |
| title_full_unstemmed | Creating new functional circuits for action via brain-machine interfaces |
| title_short | Creating new functional circuits for action via brain-machine interfaces |
| title_sort | creating new functional circuits for action via brain machine interfaces |
| topic | motor learning neural plasticity brain-machine interfaces volitional control sensorimotor systems |
| url | http://journal.frontiersin.org/Journal/10.3389/fncom.2013.00157/full |
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