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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Motor Control

Background:

  • Motor adaptation is crucial for animals to adjust movements in response to external perturbations.
  • Neural activity in the motor cortex changes during adaptation, but the origin of these changes (altered inputs vs. local connectivity) is debated.
  • Experimental evidence suggests preserved neural covariance points to altered inputs (Hinput) over local connectivity changes (Hlocal).

Purpose of the Study:

  • To computationally investigate whether altered inputs (Hinput) or local connectivity changes (Hlocal) better explain neural activity and covariance during motor adaptation.
  • To qualitatively test the hypothesis that preserved neural covariance during adaptation stems from altered inputs rather than local circuit modifications.

Main Methods:

  • Utilized a modular recurrent neural network model to simulate motor cortex adaptation.
  • Compared the effects of simulated changes in external inputs (Hinput) versus local synaptic connectivity (Hlocal) on neural activity and covariance.
  • Simulated tasks with varying degrees of behavioral change to differentiate the impact of Hinput and Hlocal.

Main Results:

  • Both Hinput and Hlocal simulations resulted in small neural activity changes and largely preserved neural covariance, consistent with experimental findings.
  • Contrary to expectations, Hlocal changes produced only slightly larger activity and covariance shifts than Hinput.
  • The similarity between Hinput and Hlocal effects was attributed to Hlocal requiring small, correlated connectivity changes for adaptation.
  • A divergence between Hinput and Hlocal effects emerged with increasing task demands.

Conclusions:

  • The study demonstrates that both altered inputs and local connectivity changes can lead to preserved neural covariance during motor adaptation.
  • The findings challenge the assumption that preserved covariance solely implies altered inputs.
  • The differential effects observed under increased behavioral demands offer potential avenues for future experimental validation and design.