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Plos Computational Biology : Optimization of Muscle Activity for Task-level Goals Predicts Complex Changes in Limb Forces Across Biomechanical Contexts, Volume 8

By McKay, J. Lucas

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Book Id: WPLBN0003941347
Format Type: PDF eBook :
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Reproduction Date: 2015

Title: Plos Computational Biology : Optimization of Muscle Activity for Task-level Goals Predicts Complex Changes in Limb Forces Across Biomechanical Contexts, Volume 8  
Author: McKay, J. Lucas
Volume: Volume 8
Language: English
Subject: Journals, Science, Computational Biology
Collections: Periodicals: Journal and Magazine Collection (Contemporary), PLoS Computational Biology
Historic
Publication Date:
Publisher: Plos

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Mckay, J. L. (n.d.). Plos Computational Biology : Optimization of Muscle Activity for Task-level Goals Predicts Complex Changes in Limb Forces Across Biomechanical Contexts, Volume 8. Retrieved from http://www.nationalpubliclibrary.info/


Description
Description : Optimality principles have been proposed as a general framework for understanding motor control in animals and humans largely based on their ability to predict general features movement in idealized motor tasks. However, generalizing these concepts past proof-of-principle to understand the neuromechanical transformation from task-level control to detailed execution-level muscle activity and forces during behaviorally-relevant motor tasks has proved difficult. In an unrestrained balance task in cats, we demonstrate that achieving task-level constraints center of mass forces and moments while minimizing control effort predicts detailed patterns of muscle activity and ground reaction forces in an anatomically-realistic musculoskeletal model. Whereas optimization is typically used to resolve redundancy at a single level of the motor hierarchy, we simultaneously resolved redundancy across both muscles and limbs and directly compared predictions to experimental measures across multiple perturbation directions that elicit different intra- and interlimb coordination patterns. Further, although some candidate task-level variables and cost functions generated indistinguishable predictions in a single biomechanical context, we identified a common optimization framework that could predict up to 48 experimental conditions per animal (n = 3) across both perturbation directions and different biomechanical contexts created by altering animals’ postural configuration. Predictions were further improved by imposing experimentally-derived muscle synergy constraints, suggesting additional task variables or costs that may be relevant to the neural control of balance. These results suggested that reduced-dimension neural control mechanisms such as muscle synergies can achieve similar kinetics to the optimal solution, but with increased control effort (<26) compared to individual muscle control. Our results are consistent with the idea that hierarchical, task-level neural control mechanisms previously associated with voluntary tasks may also be used in automatic brainstem-mediated pathways for balance.

 

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