Journal
NEURON
Volume 93, Issue 4, Pages 955-+Publisher
CELL PRESS
DOI: 10.1016/j.neuron.2017.01.016
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Funding
- National Science Foundation Graduate Research Fellowship
- ERA-NET
- European Research Council [COG 617142]
- Howard Hughes Medical Institute [IEC 55007415]
- National Science Foundation [CBET-0954243, EFRI-M3C 1137267]
- Office of Naval Research [N00014-15-1-2312]
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During motor learning, movements and underlying neural activity initially exhibit large trial-to-trial variability that decreases over learning. However, it is unclear how task-relevant neural populations coordinate to explore and consolidate activity patterns. Exploration and consolidation could happen for each neuron independently, across the population jointly, or both. We disambiguated among these possibilities by investigating how subjects learned de novo to control a brain-machine interface using neurons from motor cortex. We decomposed population activity into the sum of private and shared signals, which produce uncorrelated and correlated neural variance, respectively, and examined how these signals' evolution causally shapes behavior. We found that initially large trial-to-trial movement and private neural variability reduce over learning. Concomitantly, task-relevant shared variance increases, consolidating a manifold containing consistent neural trajectories that generate refined control. These results suggest that motor cortex acquires skillful control by leveraging both independent and coordinated variance to explore and consolidate neural patterns.
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