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Modeling the mammalian locomotor CPG: insights from mistakes and perturbations
Book chapter   Open access   Peer reviewed

Modeling the mammalian locomotor CPG: insights from mistakes and perturbations

David A McCrea and Ilya A Rybak
Progress in Brain Research, pp 235-253
2007
PMID: 17925250
url
https://doi.org/10.1016/S0079-6123(06)65015-2View
Published, Version of Record (VoR) Open

Abstract

rhythm generation spinal cord CPG locomotion afferent control
A computational model of the mammalian spinal cord circuitry incorporating a two-level central pattern generator (CPG) with separate half-center rhythm generator (RG) and pattern formation (PF) networks is reviewed. The model consists of interacting populations of interneurons and motoneurons described in the Hodgkin-Huxley style. Locomotor rhythm generation is based on a combination of intrinsic (persistent sodium current dependent) properties of excitatory RG neurons and reciprocal inhibition between the two half-centers comprising the RG. The two-level architecture of the CPG was suggested from an analysis of deletions (spontaneous omissions of activity) and the effects of afferent stimulation on the locomotor pattern and rhythm observed during fictive locomotion in the cat. The RG controls the activity of the PF network that in turn defines the rhythmic pattern of motoneuron activity. The model produces realistic firing patterns of two antagonist motoneuron populations and generates locomotor oscillations encompassing the range of cycle periods and phase durations observed during cat locomotion. A number of features of the real CPG operation can be reproduced with separate RG and PF networks, which would be difficult if not impossible to demonstrate with a classical single-level CPG. The two-level architecture allows the CPG to maintain the phase of locomotor oscillations and cycle timing during deletions and during sensory stimulation. The model provides a basis for functional identification of spinal interneurons involved in generation and control of the locomotor pattern.

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Mathematical & Computational Biology
Neurosciences
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