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Model-Based Approaches to Understanding Musculoskeletal Filtering of Neural Signals

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Neuromechanical Modeling of Posture and Locomotion

Part of the book series: Springer Series in Computational Neuroscience ((NEUROSCI))

Abstract

The musculoskeletal system lies between the nervous system and its effects on the external world. Strong constraints imposed by joints, ligaments, and muscle attachments shape both the effect the nervous system can have on the external world and the proprioceptive information conveyed to the nervous system. The integrative nature of the neuro-musculo-skeletal system makes it difficult to examine the effects of this structure experimentally, and a well-developed mathematical framework allows analysis of otherwise inseparable aspects of the chain of control. Structure in the limbs seems to separate aspects of limb performance, such as weight support and propulsion, to different degrees of freedom, which may simplify neural control processes. The bi-directional nature of the musculoskeletal filter amplifies signals that the nervous system can most strongly influence and suppresses feedback of task parameters that are poorly controllable.

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Acknowledgements

This work was supported by NIH grants HD032571 and HD46922. The NIH had no role in the design, performance or interpretation of the study.

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Authors and Affiliations

  1. School of Applied Physiology and Interdisciplinary Bioengineering Program, Georgia Institute of Technology, 555 14th St NW, 30332-0356, Atlanta, GA, USA

    Thomas J. Burkholder PhD

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  1. Thomas J. Burkholder PhD
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Correspondence to Thomas J. Burkholder PhD .

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Editors and Affiliations

  1. Georgia Institute of Technology , Atlanta, Georgia, USA

    Boris I. Prilutsky

  2. Georgia State University , Atlanta, Georgia, USA

    Donald H. Edwards

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Burkholder, T. (2016). Model-Based Approaches to Understanding Musculoskeletal Filtering of Neural Signals. In: Prilutsky, B., Edwards, D. (eds) Neuromechanical Modeling of Posture and Locomotion. Springer Series in Computational Neuroscience. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-3267-2_4

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