4.7 Article

Two distinct layer-specific dynamics of cortical ensembles during learning of a motor task

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NATURE NEUROSCIENCE
卷 17, 期 7, 页码 987-994

出版社

NATURE PORTFOLIO
DOI: 10.1038/nn.3739

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资金

  1. Scientific Research on Innovative Areas 'Mesoscopic Neurocircuitry' [22115005, 23115504]
  2. Neural Creativity for Communication [22120520]
  3. Challenging Exploratory Research [22650083]
  4. Scientific Research [23300148]
  5. Japan Society for the Promotion of Science Research Fellowships for Young Scientists [268449, 253960, 2213610]
  6. Research Activity Start-up [23800071]
  7. Strategic Research Program for Brain Sciences from the Ministry of Education, Culture, Sports, Science, and Technology
  8. Mitsubishi Foundation
  9. Takeda Foundation
  10. Toyoaki Foundation
  11. Uehara Memorial Foundation
  12. Brain Science Foundation
  13. [19680020]
  14. [26830020]
  15. [22680031]
  16. Grants-in-Aid for Scientific Research [22650083, 22115005, 25560432, 25113705, 23300148, 26830020, 25115705, 14J08449] Funding Source: KAKEN

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The primary motor cortex (M1) possesses two intermediate layers upstream of the motor-output layer: layer 2/3 (L2/3) and layer 5a (L5a). Although repetitive training often improves motor performance and movement coding by M1 neuronal ensembles, it is unclear how neuronal activities in L2/3 and L5a are reorganized during motor task learning. We conducted two-photon calcium imaging in mouse M1 during 14 training sessions of a self-initiated lever-pull task. In L2/3, the accuracy of neuronal ensemble prediction of lever trajectory remained unchanged globally, with a subset of individual neurons retaining high prediction accuracy throughout the training period. However, in L5a, the ensemble prediction accuracy steadily improved, and one-third of neurons, including subcortical projection neurons, evolved to contribute substantially to ensemble prediction in the late stage of learning. The L2/3 network may represent coordination of signals from other areas throughout learning, whereas L5a may participate in the evolving network representing well-learned movements.

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