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.
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R.C.H. is funded by the Neuroscience Research and Education Foundation, E.N.B. is funded by US National Institutes of Health (NIH) DP1 OD003646, and Z.M.W. is funded by NIH 5R01-HD059852, a Presidential Early Career Award for Scientists and Engineers and the Whitehall Foundation.
The authors declare no competing financial interests.
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Shanechi, M., Hu, R., Powers, M. et al. Neural population partitioning and a concurrent brain-machine interface for sequential motor function. Nat Neurosci 15, 1715–1722 (2012). https://doi.org/10.1038/nn.3250
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