Neural population partitioning and a concurrent brain-machine interface for sequential motor function
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Brown_Neural population.pdf
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Author(s)
Hu, Rollin
Powers, Marissa
Williams, Ziv M
Modir Shanechi, Maryam
Wornell, Gregory W.
Brown, Emery N.
Date Issued
November 2012
Journal
Nature Neuroscience
Publisher
Nature Publishing Group
Citation
Shanechi, Maryam M, Rollin C Hu, Marissa Powers, Gregory W Wornell, Emery N Brown, and Ziv M Williams. “Neural Population Partitioning and a Concurrent Brain-Machine Interface for Sequential Motor Function.” Nat Neurosci 15, no. 12 (November 11, 2012): 1715–1722.
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Author's final manuscript
Abstract
Although brain-machine interfaces (BMIs) have focused largely on performing single-targeted movements, many natural tasks involve planning a complete sequence of such movements before execution. For these tasks, a BMI that can concurrently decode the full planned sequence before its execution may also consider the higher-level goal of the task to reformulate and perform it more effectively. Using population-wide modeling, we discovered two distinct subpopulations of neurons in the rhesus monkey premotor cortex that allow two planned targets of a sequential movement to be simultaneously held in working memory without degradation. Such marked stability occurred because each subpopulation encoded either only currently held or only newly added target information irrespective of the exact sequence. On the basis of these findings, we developed a BMI that concurrently decodes a full motor sequence in advance of movement and can then accurately execute it as desired.
MIT Department
Institute for Medical Engineering and Science
Harvard University--MIT Division of Health Sciences and Technology
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1038/nn.3250
