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Prescription of rhythmic patterns for legged locomotion

Neural Computing and Applications volume 28, pages 3587–3601 (2017)Cite this article

Abstract

As the engine behind many life phenomena, motor information generated by the central nervous system plays a critical role in the activities of all animals. In this work, a novel, macroscopic and model-independent approach is presented for creating different patterns of coupled neural oscillations observed in biological central pattern generators (CPG) during the control of legged locomotion. Based on a simple distributed state machine, which consists of two nodes sharing pre-defined number of resources, the concept of oscillatory building blocks (OBBs) is summarised for the production of elaborated rhythmic patterns. Various types of OBBs can be designed to construct a motion joint of one degree of freedom with adjustable oscillatory frequencies and duty cycles. An OBB network can thus be potentially built to generate a full range of locomotion patterns of a legged animal with controlled transitions between different rhythmic patterns. It is shown that gait pattern transition can be achieved by simply changing a single parameter of an OBB module. Essentially, this simple mechanism allows for the consolidation of a methodology for the construction of artificial CPG architectures behaving as an asymmetric Hopfield neural network. Moreover, the proposed CPG model introduced here is amenable to analogue and/or digital circuit integration.

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Acknowledgments

This research has been supported in part by a British Biotechnology and Biological Sciences Research Council (BBSRC) Grant BBS/B/07217, an Engineering and Physical Sciences Research Council (EPSRC) Grant EP/E063322/1, and two Chinese National Science Foundation (NSFC) Grants 60673102 and 61103185. The MATLAB–Simulink program is available upon request.

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Authors and Affiliations

  1. School of Science and Technology, Middlesex University, London, NW4 4BT, UK

    Zhijun Yang & Mehmet Karamanoglu

  2. School of Software, Tongji University, Shanghai, China

    Daqiang Zhang

  3. Systems Engineering and Computer Science Program, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil

    Marlon V. Rocha & Felipe M. G. França

  4. Tércio Pacitti Institute, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil

    Priscila M. V. Lima

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  1. Zhijun Yang
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Correspondence to Zhijun Yang.

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Yang, Z., Zhang, D., Rocha, M.V. et al. Prescription of rhythmic patterns for legged locomotion. Neural Comput & Applic 28, 3587–3601 (2017). https://doi.org/10.1007/s00521-016-2237-4

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