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Neuromechanic: A computational platform for simulation and analysis of the neural control of movement

Authors

  • Nathan E. Bunderson,

    Corresponding author
    • School of Applied Physiology, Georgia Institute of Technology, Atlanta, GA, U.S.A.
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  • Jeffrey T. Bingham,

    1. Interdisciplinary Bioengineering Program, Georgia Institute of Technology, Atlanta, GA, U.S.A.
    2. School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, U.S.A.
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  • M. Hongchul Sohn,

    1. School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, U.S.A.
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  • Lena H. Ting,

    1. School of Applied Physiology, Georgia Institute of Technology, Atlanta, GA, U.S.A.
    2. Interdisciplinary Bioengineering Program, Georgia Institute of Technology, Atlanta, GA, U.S.A.
    3. School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, U.S.A.
    4. Department of Biomedical Engineering, Emory University and Georgia Institute of Technology Atlanta, GA, U.S.A.
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  • Thomas J. Burkholder

    1. School of Applied Physiology, Georgia Institute of Technology, Atlanta, GA, U.S.A.
    2. Interdisciplinary Bioengineering Program, Georgia Institute of Technology, Atlanta, GA, U.S.A.
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Nathan E. Bunderson, School of Applied Physiology, Georgia Institute of Technology, 55514th Street, Atlanta, GA 30332-0356 U.S.A.

E-mail: nbunderson@gatech.edu

SUMMARY

Neuromusculoskeletal models solve the basic problem of determining how the body moves under the influence of external and internal forces. Existing biomechanical modeling programs often emphasize dynamics with the goal of finding a feed-forward neural program to replicate experimental data or of estimating force contributions or individual muscles. The computation of rigid-body dynamics, muscle forces, and activation of the muscles are often performed separately. We have developed an intrinsically forward computational platform (Neuromechanic, www.neuromechanic.com) that explicitly represents the interdependencies among rigid body dynamics, frictional contact, muscle mechanics, and neural control modules. This formulation has significant advantages for optimization and forward simulation, particularly with application to neural controllers with feedback or regulatory features. Explicit inclusion of all state dependencies allows calculation of system derivatives with respect to kinematic states and muscle and neural control states, thus affording a wealth of analytical tools, including linearization, stability analyses and calculation of initial conditions for forward simulations. In this review, we describe our algorithm for generating state equations and explain how they may be used in integration, linearization, and stability analysis tools to provide structural insights into the neural control of movement. Copyright © 2012 John Wiley & Sons, Ltd.

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