A generalized locomotion CPG architecture based on oscillatory building blocks

 Neural oscillation is one of the most extensively investigated topics of artificial neural networks. Scientific approaches to the functionalities of both natural and artificial intelligences are strongly related to mechanisms underlying oscillatory activities. This paper concerns itself with the assumption of the existence of central pattern generators (CPGs), which are the plausible neural architectures with oscillatory capabilities, and presents a discrete and generalized approach to the functionality of locomotor CPGs of legged animals. Based on scheduling by multiple edge reversal (SMER), a primitive and deterministic distributed algorithm, it is shown how oscillatory building block (OBB) modules can be created and, hence, how OBB-based networks can be formulated as asymmetric Hopfield-like neural networks for the generation of complex coordinated rhythmic patterns observed among pairs of biological motor neurons working during different gait patterns. It is also shown that the resulting Hopfield-like network possesses the property of reproducing the whole spectrum of different gaits intrinsic to the target locomotor CPGs. Although the new approach is not restricted to the understanding of the neurolocomotor system of any particular animal, hexapodal and quadrupedal gait patterns are chosen as illustrations given the wide interest expressed by the ongoing research in the area. Received: 14 June 2002 / Accepted: 18 February 2003 / Published online: 20 May 2003 Correspondence to: Z. Yang (e-mail: zhijun.yang@ed.ac.uk) Acknowledgements. This work was partially supported by CNPq, the Brazilian Research Agency, under support number 143032/96-8. We are grateful for the helpful discussions with Prof. V.C. Barbosa, Dr. A.E. Xavier, Dr. M.S. Dutra, and Dr. A.F.R. Araújo. The donations of FPGA hardware and software from XILINX Incorporation under the order No. XUP2930 and XUP3576 are also highly appreciated.