Document Type
Article
Publication Date
2018
College/Unit
School of Medicine
Department/Program/Center
Physiology, Pharmacology & Neuroscience
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 dynam- ical 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.
Digital Commons Citation
Yakovenko, Sergiy; Sobinov, Anton; and Gritsenko, Valeriya, "Analytical CPG model driven by limb velocity input generates accurate temporal locomotor dynamics" (2018). Faculty & Staff Scholarship. 1350.
https://researchrepository.wvu.edu/faculty_publications/1350
Source Citation
Yakovenko, S., Sobinov, A., & Gritsenko, V. (2018). Analytical CPG model driven by limb velocity input generates accurate temporal locomotor dynamics. PeerJ, 6, e5849. https://doi.org/10.7717/peerj.5849
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Comments
Copyright
2018 Yakovenko et al.