Analytical CPG model driven by single-limb velocity input generates accurate temporal locomotor dynamics

Department of Human Performance, School of Medicine, West Virginia University, Morgantown, WV, United States
Department of Biomedical Engineering, Benjamin M. Statler College of Engineering and Mineral Resources, West Virginia University, Morgantown, WV, United States
Rockefeller Neuroscience Institute, School of Medicine, WVU, West Virginia University, Morgantown, WV, United States
Rockefeller Neuroscience Institute, School of Medicine, West Virginia University, Morgantown, WV, United States
DOI
10.7287/peerj.preprints.26734v2
Subject Areas
Animal Behavior, Bioinformatics, Computational Biology, Neuroscience, Computational Science
Keywords
model, CPG, locomotion
Copyright
© 2018 Yakovenko et al.
Licence
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
Cite this article
Yakovenko S, Sobinov A, Gritsenko V. 2018. Analytical CPG model driven by single-limb velocity input generates accurate temporal locomotor dynamics. PeerJ Preprints 6:e26734v2

Abstract

The ability of vertebrates to generate rhythm within their spinal neural networks is essential for walking, running, and other rhythmic behaviors. The central pattern generator (CPG) network responsible for these behaviors is well-characterized with experimental and theoretical studies, and it can be formulated as a nonlinear dynamical system. The underlying mechanism responsible for locomotor behavior can be expressed as the process of leaky integration with resetting states generating appropriate phases for changing body velocity. The low-dimensional input to the CPG model generates the bilateral pattern of swing and stance modulation for each limb and is consistent with the desired limb speed as the input command. To test the minimal configuration of required parameters for this model, we reduced the system of equations representing CPG for a single limb and provided the analytical solution with two complementary methods. The analytical and empirical cycle durations were similar (R2=0.99) for the full range of walking speeds. The structure of solution is consistent with the use of limb speed as the input domain for the CPG network. Moreover, the reciprocal interaction between two leaky integration processes representing a CPG for two limbs was sufficient to capture fundamental experimental dynamics associated with the control of heading direction. This analysis provides further support for the embedded velocity or limb speed representation within spinal neural pathways involved in rhythm generation.

Author Comment

We have expanded the use of the parsimonious CPG model in the description of turning. The background literature review and the discussion sections have been expanded.

Supplemental Information

Matlab code to generate Fig.3

DOI: 10.7287/peerj.preprints.26734v2/supp-1