Neural population partitioning and a concurrent brain-machine interface for sequential motor function
Open accessAbstract
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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Support vector machines with nonlinear kernels are well-suited for identifying neuronal subspaces in relation to specific task features and account for potential nonlinearities in the response properties of the neurons.
“As described previously 62–64, these approaches are well-suited for identifying neuronal ‘subspaces’ in relation to specific task features and account for potential nonlinearities in the response properties of the neurons.”
From Reduced sociability and social agency encoding in adult Shank3-mutant mice are restored through gene re-expression in real time (2021) · cites this paper as [63]
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References 54 · Cited by 96
Research works printed but not linked: 1
- [49]Brown EN Barbieri R Eden UT Frank LM Feng J Likelihood methods for neural data analysis Computational Neuroscience: A Comprehensive Approach 2003 253 286 CRC Press Boca Raton
- Authors
- Shanechi, M. M., Hu, R. C., Powers, M., Wornell, G. W., Brown, E. N., & Williams, Z. M.
- Affiliations
- Harvard University (US)
- Massachusetts General Hospital (US)
- Massachusetts Institute of Technology (US)
- Corresponding author
- Williams, Ziv M
- Venue
- Nature Neuroscience
- Published
- 2012-11-11
- Volume
- 15
- Issue
- 12
- Pages
- 1715-1722
- ISSN
- 1097-6256 (ISSN-L)
- 1546-1726
- PMC
- PMC3509235
- OpenAlex
- W2015667795
- Cited by
- 96
- References
- 54
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 7 · 2023: 10 · 2022: 8 · 2021: 8 · 2020: 5 · 2019: 7 · 2018: 6 · 2017: 6 · 2016: 5 · 2015: 8 · 2014: 12 · 2013: 10 · 2012: 1
- FWCI
- 5.07
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Neuroscience (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
- PMC
- Funders
- National Institutes of Health (DP1OD003646)
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (R01HD059852, 5R01-HD059852)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Life Sciences › Neuroscience › Cognitive Neuroscience › Motor Control and Adaptation
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neural Engineering
- Keywords
- brain-computer interface, premotor cortex, population coding, working memory, target stability, rhesus monkeys
- MeSH terms
- Action Potentials, Animals, Macaca mulatta, Male, Motor Cortex, Neurons, Photic Stimulation, Psychomotor Performance, Random Allocation, Reaction Time, Brain-Computer Interfaces
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