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Cortical population activity within a preserved neural manifold underlies multiple motor behaviors

Author

Listed:
  • Juan A. Gallego

    (Northwestern University
    Centre for Automation and Robotics CSIC-UPM)

  • Matthew G. Perich

    (Northwestern University)

  • Stephanie N. Naufel

    (Northwestern University)

  • Christian Ethier

    (CERVO Research Center)

  • Sara A. Solla

    (Northwestern University
    Northwestern University)

  • Lee E. Miller

    (Northwestern University
    Northwestern University
    Northwestern University)

Abstract

Populations of cortical neurons flexibly perform different functions; for the primary motor cortex (M1) this means a rich repertoire of motor behaviors. We investigate the flexibility of M1 movement control by analyzing neural population activity during a variety of skilled wrist and reach-to-grasp tasks. We compare across tasks the neural modes that capture dominant neural covariance patterns during each task. While each task requires different patterns of muscle and single unit activity, we find unexpected similarities at the neural population level: the structure and activity of the neural modes is largely preserved across tasks. Furthermore, we find two sets of neural modes with task-independent activity that capture, respectively, generic temporal features of the set of tasks and a task-independent mapping onto muscle activity. This system of flexibly combined, well-preserved neural modes may underlie the ability of M1 to learn and generate a wide-ranging behavioral repertoire.

Suggested Citation

  • Juan A. Gallego & Matthew G. Perich & Stephanie N. Naufel & Christian Ethier & Sara A. Solla & Lee E. Miller, 2018. "Cortical population activity within a preserved neural manifold underlies multiple motor behaviors," Nature Communications, Nature, vol. 9(1), pages 1-13, December.
    • Handle: RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-018-06560-z
    DOI: 10.1038/s41467-018-06560-z
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    Cited by:

    1. Svenja Melbaum & Eleonora Russo & David Eriksson & Artur Schneider & Daniel Durstewitz & Thomas Brox & Ilka Diester, 2022. "Conserved structures of neural activity in sensorimotor cortex of freely moving rats allow cross-subject decoding," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    2. Ege Altan & Sara A Solla & Lee E Miller & Eric J Perreault, 2021. "Estimating the dimensionality of the manifold underlying multi-electrode neural recordings," PLOS Computational Biology, Public Library of Science, vol. 17(11), pages 1-23, November.
    3. Joao Barbosa & Rémi Proville & Chris C. Rodgers & Michael R. DeWeese & Srdjan Ostojic & Yves Boubenec, 2023. "Early selection of task-relevant features through population gating," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    4. Tanner C Dixon & Christina M Merrick & Joni D Wallis & Richard B Ivry & Jose M Carmena, 2021. "Hybrid dedicated and distributed coding in PMd/M1 provides separation and interaction of bilateral arm signals," PLOS Computational Biology, Public Library of Science, vol. 17(11), pages 1-35, November.
    5. Nan-Sheng Huang & Yi-Chung Chen & Jørgen Christian Larsen & Poramate Manoonpong, 2020. "AHEAD: Automatic Holistic Energy-Aware Design Methodology for MLP Neural Network Hardware Generation in Proactive BMI Edge Devices," Energies, MDPI, vol. 13(9), pages 1-20, May.
    6. Jan Weber & Anne-Kristin Solbakk & Alejandro O. Blenkmann & Anais Llorens & Ingrid Funderud & Sabine Leske & Pål Gunnar Larsson & Jugoslav Ivanovic & Robert T. Knight & Tor Endestad & Randolph F. Helf, 2024. "Ramping dynamics and theta oscillations reflect dissociable signatures during rule-guided human behavior," Nature Communications, Nature, vol. 15(1), pages 1-16, December.

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