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Design and Analysis of Neuromechanic...

Deng, Kaiyu.

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Design and Analysis of Neuromechanical Models of the Rat Hindlimb with Two-Layer CPGS.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Design and Analysis of Neuromechanical Models of the Rat Hindlimb with Two-Layer CPGS./
Author:	Deng, Kaiyu.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
Description:	153 p.
Notes:	Source: Dissertations Abstracts International, Volume: 84-12, Section: B.
Contained By:	Dissertations Abstracts International84-12B.
Subject:	Neurosciences. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30557126
ISBN:	9798379605148

Design and Analysis of Neuromechanical Models of the Rat Hindlimb with Two-Layer CPGS.
Deng, Kaiyu.

Design and Analysis of Neuromechanical Models of the Rat Hindlimb with Two-Layer CPGS. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 153 p.

Source: Dissertations Abstracts International, Volume: 84-12, Section: B.

Thesis (Ph.D.)--Case Western Reserve University, 2023.

This item must not be sold to any third party vendors.

This work advances the modeling of neurologically based control of rat hindlimb walking. First, I implemented a hypothesized neural architecture -- the rhythm generator (RG) -- into a previous neural controller to form a "two-layer CPG". This model reproduces the 'non-resetting deletions' previously documented in rat locomotion experiments. I revealed that this two-layer CPG structure could enable flexibility at the individual step level while still maintaining an overall timing. I also demonstrated that the two-layer CPG exhibits greater stability than the traditional half-center oscillator. This is evidenced by the system's faster return to its unperturbed trajectory after a given perturbation. However, the neural parameters were determined by a combination of optimization and hand-tuning in the two-layer CPG model. How the intrinsic properties of the two-layer CPG cooperate with sensory feedback to produce the desired outputs remained unknown. Therefore, I next investigated how connections between the two half-centers of the CPG and descending commands influence the CPG behavior, and how the RG controls and influences the pattern formation layer under various stimuli/inputs. I demonstrated that a weak cross-excitatory connection could 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, I verified that the neural variables in the CPG do not have to be fixed precisely for stable walking; the biomechanical entrainment and sensory feedback reduce neural behavior differences due to the effect of excitatory connectivity in the neural circuit and play a critical role in shaping locomotor behaviors. Finally, I modified the musculoskeletal system of a rat hindlimb model from one pair of antagonist muscles per joint to a multi-muscle per joint configuration. In order to predict muscle stretch and the relationship between passive tension and step-phases during walking, I also formulated the inverse kinematics for the limbs and muscles. The findings presented in this work establish a guideline for future mammalian locomotor modeling.

ISBN: 9798379605148Subjects--Topical Terms:

588700
Neurosciences.
Subjects--Index Terms:

Rat

Design and Analysis of Neuromechanical Models of the Rat Hindlimb with Two-Layer CPGS.
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520 $a This work advances the modeling of neurologically based control of rat hindlimb walking. First, I implemented a hypothesized neural architecture -- the rhythm generator (RG) -- into a previous neural controller to form a "two-layer CPG". This model reproduces the 'non-resetting deletions' previously documented in rat locomotion experiments. I revealed that this two-layer CPG structure could enable flexibility at the individual step level while still maintaining an overall timing. I also demonstrated that the two-layer CPG exhibits greater stability than the traditional half-center oscillator. This is evidenced by the system's faster return to its unperturbed trajectory after a given perturbation. However, the neural parameters were determined by a combination of optimization and hand-tuning in the two-layer CPG model. How the intrinsic properties of the two-layer CPG cooperate with sensory feedback to produce the desired outputs remained unknown. Therefore, I next investigated how connections between the two half-centers of the CPG and descending commands influence the CPG behavior, and how the RG controls and influences the pattern formation layer under various stimuli/inputs. I demonstrated that a weak cross-excitatory connection could 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, I verified that the neural variables in the CPG do not have to be fixed precisely for stable walking; the biomechanical entrainment and sensory feedback reduce neural behavior differences due to the effect of excitatory connectivity in the neural circuit and play a critical role in shaping locomotor behaviors. Finally, I modified the musculoskeletal system of a rat hindlimb model from one pair of antagonist muscles per joint to a multi-muscle per joint configuration. In order to predict muscle stretch and the relationship between passive tension and step-phases during walking, I also formulated the inverse kinematics for the limbs and muscles. The findings presented in this work establish a guideline for future mammalian locomotor modeling.
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653 $a Neuromechanical model
653 $a Synthetic nervous system
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