Biomechanical and Sensory Feedback Regularize the Behavior of Different Locomotor Central Pattern Generators

Area of Science:

• Computational Neuroscience
• Biophysics
• Robotics

Background:

• Mammalian locomotion is controlled by central pattern generators (CPGs).
• The specific roles of certain CPG parameters, like weak cross-excitatory connections and inter-layer synapse strength, remain under-explored.
• Understanding these parameters is key to developing accurate neuromechanical models.

Purpose of the Study:

• To numerically investigate a two-layer CPG model for mammalian walking.
• To analyze the impact of specific CPG parameters (cross-excitatory connectivity, inter-layer synapse strength) on locomotion.
• To determine if CPG phase response curves predict model stability.

Main Methods:

• In-depth numerical simulations of a two-layer CPG model.
• Sensitivity analysis of deafferented CPG and combined neuromechanical models.
• Systematic variation of CPG parameters and locomotion frequency.

Main Results:

• Weak cross-excitatory connections increase CPG sensitivity to perturbations.
• Increased inter-layer synapse strength creates a trade-off between phase locking and phase delay.
• Parameter-dependent differences in CPGs diminish in the full neuromechanical model.

Conclusions:

• Precise neural parameters are not essential for stable walking.
• Biomechanical entrainment and sensory feedback significantly influence locomotor behavior.
• Integrating biomechanical models is vital for computational neuroscience of locomotion.