4.6 Article

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

期刊

BIOMIMETICS
卷 7, 期 4, 页码 -

出版社

MDPI
DOI: 10.3390/biomimetics7040226

关键词

synthetic nervous system; rat; rhythm generator; pattern formation; perturbation analysis

资金

  1. NSF as part of the NSF/CIHR/DFG/FRQ/UKRI-MRC Next Generation Networks for Neuroscience Program [DBI 2015317]
  2. NSF US-German Collaborative Grant [1608111]
  3. NSF [1943483]
  4. Direct For Computer & Info Scie & Enginr
  5. Div Of Information & Intelligent Systems [1608111] Funding Source: National Science Foundation
  6. Direct For Computer & Info Scie & Enginr
  7. Div Of Information & Intelligent Systems [1943483] Funding Source: National Science Foundation

向作者/读者索取更多资源

This work presents a numerical investigation into a hypothesized two-layer central pattern generator (CPG) that controls mammalian walking, with a focus on the role of weak cross-excitatory connectivity and synapse strength between the two layers. The study shows that the weak cross-excitatory connection makes the CPG more sensitive to perturbations, and increasing the synaptic strength results in a trade-off between forced phase locking and phase delay. The inclusion of a biomechanical model cancels out the differences in neural variables, indicating the importance of biomechanical models in understanding mammalian locomotion.
This work presents an in-depth numerical investigation into a hypothesized two-layer central pattern generator (CPG) that controls mammalian walking and how different parameter choices might affect the stepping of a simulated neuromechanical model. Particular attention is paid to the functional role of features that have not received a great deal of attention in previous work: the weak cross-excitatory connectivity within the rhythm generator and the synapse strength between the two layers. Sensitivity evaluations of deafferented CPG models and the combined neuromechanical model are performed. Locomotion frequency is increased in two different ways for both models to investigate whether the model's stability can be predicted by trends in the CPG's phase response curves (PRCs). Our results show that the weak cross-excitatory connection can make the CPG more sensitive to perturbations and that increasing the synaptic strength between the two layers results in a trade-off between forced phase locking and the amount of phase delay that can exist between the two layers. Additionally, although the models exhibit these differences in behavior when disconnected from the biomechanical model, these differences seem to disappear with the full neuromechanical model and result in similar behavior despite a variety of parameter combinations. This indicates that the neural variables do not have to be fixed precisely for stable walking; the biomechanical entrainment and sensory feedback may cancel out the strengths of excitatory connectivity in the neural circuit and play a critical role in shaping locomotor behavior. Our results support the importance of including biomechanical models in the development of computational neuroscience models that control mammalian locomotion.

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