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Neuromechanic: A computational platform for simulation and analysis of the neural control of movement
Author(s) -
Bunderson Nathan E.,
Bingham Jeffrey T.,
Hongchul Sohn M.,
Ting Lena H.,
Burkholder Thomas J.
Publication year - 2012
Publication title -
international journal for numerical methods in biomedical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.741
H-Index - 63
eISSN - 2040-7947
pISSN - 2040-7939
DOI - 10.1002/cnm.2486
Subject(s) - linearization , kinematics , stability (learning theory) , computer science , artificial neural network , computation , control theory (sociology) , control engineering , control (management) , nonlinear system , engineering , artificial intelligence , algorithm , machine learning , physics , classical mechanics , quantum mechanics
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.