Nonlinear dynamics of an oscilattory neural network acting as a motor central pettern generator

Abstract

In vertebrates, the central nervous system processes sensorial input from surroundings and in most of the cases determines motor responses accordingly. There exist specialized synaptic circuits or neural networks for generating and maintaining periodic motor signals necessary for the animal’s locomotion as walking, trotting, running or swimming. Recent works have addressed the study of animal locomotion by modeling the neural networks in charge of controlling the rhythmic movements of legs and motor appendices in bipeds, quadrupeds and hexapods [Kiehn et al., 2008]. These works have been extended to modeling and controlling the movements of legged robots [Teng et al., 2012; Mondal et al., 2011]. These synaptic circuits and neural networks or central pattern generators (CPG) [McCrea & Rybak, 2008] exhibit a nonlinear dynamics leading to nonlinear oscillations.