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Joanna C Chang1, Matthew G Perich2, Lee E Miller3,4,5

  • 1Department of Bioengineering, Imperial College London, London, UK.

Biorxiv : the Preprint Server for Biology
|June 9, 2023
PubMed
Summary
This summary is machine-generated.

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Prior motor learning shapes neural activity, influencing how quickly animals adapt to new movements. A richer movement history aids adaptation, but only when task demands align with existing neural structures.

Area of Science:

  • Neuroscience
  • Motor Control
  • Computational Neuroscience

Background:

  • Animals exhibit rapid motor adaptation to external perturbations.
  • The influence of an animal's existing movement repertoire on motor adaptation is not well understood.
  • Long-term learning induces persistent neural changes that dictate potential activity patterns.

Approach:

  • Modeled motor cortical neural population dynamics using recurrent neural networks.
  • Trained networks on motor repertoires with varying numbers of movements.
  • Investigated how neural population activity repertoires affect short-term adaptation.

Key Points:

  • Networks with more movements developed more constrained and robust dynamics, forming a defined neural 'structure'.
  • This neural structure facilitated adaptation, particularly for small motor output changes.

Related Experiment Videos

  • Adaptation success depended on the congruence between network inputs, neural activity space, and perturbations.

    • Conclusions:

    • Prior experience and learning history significantly shape neural population activity geometry.
    • This neural geometry influences the efficiency and constraints of subsequent motor adaptation.
    • Findings reveal trade-offs in skill acquisition and the interplay between prior learning and new experiences.