A General Rhythmic Pattern Generation Architecture for Legged Locomotion

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Author(s): Zhijun Yang (Stirling University, UK)and Felipe M.G. França (Universidade Federal do Rio de Janeiro, Brazil)
Copyright: 2009
Pages: 29
Source title: Advancing Artificial Intelligence through Biological Process Applications
Source Author(s)/Editor(s): Ana B. Porto Pazos (Coruna University, Spain), Alejandro Pazos Sierra (Coruna University, Spain)and Washington Buño Buceta (Cajal Institute, Spanish Council for Scientific Research, Spain)
DOI: 10.4018/978-1-59904-996-0.ch012

Keywords: Bioinformatics / Biologically-Inspired Computing / Medical Information Science Reference / Medicine & Healthcare

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Abstract

As an engine of almost all life phenomena, the motor information generated by the central nervous system (CNS) plays a critical role in the activities of all animals. After a brief review of some recent research results on locomotor central pattern generators (CPG), which is a concrete branch of studies on the CNS generating rhythmic patterns, this chapter presents a novel, macroscopic and model-independent approach to the retrieval of different patterns of coupled neural oscillations observed in biological CPGs during the control of legged locomotion. Based on scheduling by multiple edge reversal (SMER), a simple and discrete distributed synchroniser, various types of oscillatory building blocks (OBB) can be reconfigured for the production of complicated rhythmic patterns and a methodology is provided for the construction of a target artificial CPG architecture behaving as a SMER-like asymmetric Hopfield neural networks.

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A General Rhythmic Pattern Generation Architecture for Legged Locomotion

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