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Biological Cybernetics

, Volume 112, Issue 4, pp 345–356 | Cite as

Modeling and analysis of a new locomotion control neural networks

Original Article

Abstract

Experimental data have shown that inherent bursting of the neuron plays an important role in the generation of rhythmic movements in spinal networks. Based on the mechanism that the spinal neurons of a lamprey generate this inherent bursting, this paper builds a simplified inherent bursting neuron model. A new locomotion control neural network is built that takes advantage of this neuron model and its performance is analyzed mathematically and by numerical simulation. From these analyses, it is found that the new control networks have no restriction on their topological structure for generating the oscillatory outputs. If a network is used to control the motion of bionic robots or build the model of the vertebrate spinal circuitry, its topological structure can be constructed using the unit burst generator model proposed by Grillner. The networks can also be easily switched between oscillatory and non-oscillatory output. Additionally, inactivity and saturation properties of the new networks can also be developed, which will be helpful to increase the motor flexibility and environmental adaptability of bionic robots.

Keywords

Locomotion control neural networks Central pattern generator (CPG) Oscillatory output Non-oscillatory output Bionic robot control 

Notes

Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant 61105110, the Natural Science Foundation of the Jiangsu Higher Education Institutions of China under Grant 14KJB510004 and the Lianyungang “521” Project and the six talent peaks project in Jiangsu Province, and the Jiangsu Overseas Research and Training Program for University Prominent Young and Middle-aged Teachers and President.

Supplementary material

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Supplementary material 1 (avi 4107 KB)
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Supplementary material 3 (avi 3657 KB)

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Electric EngineeringHuaihai Institute of TechnologyLian YungangChina

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