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Curr Opin Neurobiol. 2017 Oct;46:76-83. doi: 10.1016/j.conb.2017.08.002. Epub 2017 Aug 24.

Parsing learning in networks using brain-machine interfaces.

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Center for Neural Science, New York University, New York, NY 10003, USA. Electronic address:
Center for Neural Science, New York University, New York, NY 10003, USA.


Brain-machine interfaces (BMIs) define new ways to interact with our environment and hold great promise for clinical therapies. Motor BMIs, for instance, re-route neural activity to control movements of a new effector and could restore movement to people with paralysis. Increasing experience shows that interfacing with the brain inevitably changes the brain. BMIs engage and depend on a wide array of innate learning mechanisms to produce meaningful behavior. BMIs precisely define the information streams into and out of the brain, but engage wide-spread learning. We take a network perspective and review existing observations of learning in motor BMIs to show that BMIs engage multiple learning mechanisms distributed across neural networks. Recent studies demonstrate the advantages of BMI for parsing this learning and its underlying neural mechanisms. BMIs therefore provide a powerful tool for studying the neural mechanisms of learning that highlights the critical role of learning in engineered neural therapies.

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