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Asymmetric Walkway: A Novel Behavioral Assay for Studying Asymmetric Locomotion
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An optimality principle for locomotor central pattern generators.

Hansol X Ryu1, Arthur D Kuo2,3

  • 1Biomedical Engineering Program, University of Calgary, 2500 University Drive NW, Calgary, AB, T2N 1N4, Canada. hansol.ryu@ucalgary.ca.

Scientific Reports
|June 24, 2021
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Summary
This summary is machine-generated.

Optimization under uncertainty explains how central pattern generators (CPGs) and reflex circuits combine for legged locomotion. This approach models CPGs as state estimators, improving dynamic walking efficiency and stability.

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Area of Science:

  • Neuroscience
  • Robotics
  • Biomechanics

Background:

  • Legged locomotion involves central pattern generators (CPGs) for rhythmic commands and reflex circuits for sensory feedback.
  • Balancing these circuits in control models is challenging due to infinite possible combinations.

Purpose of the Study:

  • To propose an optimization framework using uncertainty to explain the combination of CPGs and reflex circuits for locomotion.
  • To re-interpret CPGs within state estimator-based control for dynamic walking.

Main Methods:

  • Developed a control model integrating CPGs as state estimators that predict limb states.
  • Used sensory feedback to balance environmental and sensory uncertainties.
  • Applied the model to simulate dynamic bipedal walking.

Main Results:

  • The optimally predicted state minimized the energetic cost of transport and maximized stability in bipedal walking.
  • Fictive locomotion emerged as a byproduct of estimator dynamics, not an explicit rhythm.
  • Neural parameters were determined by optimal estimation principles.

Conclusions:

  • Uncertainty is crucial for shaping CPG behavior and optimizing sensory feedback use in locomotion.
  • State-estimator-based control offers a new perspective on CPG function and neural control of movement.