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Spatiotemporal dynamic of a coupled neutral-type neural network with time delay and diffusion

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Abstract

In this paper, a delayed neutral-type neural network with diffusion is considered. Three spatiotemporal dynamic problems of such network, i.e., stability, Turing instability and oscillation, are addressed in detail. It is found that the diffusion may lead to Turing instability, and the time delay may result in oscillation. Then, a novel computing method is proposed to investigate the oscillation properties. Finally, numerical results not only verify the obtained results but also show the diffusion coefficients have a great effect on the appearance of pattern. There are six spatiotemporal patterns when diffusion varying.

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Acknowledgements

This work was supported in part by the National Key Research and Development Project of China under Grant 2018AAA0100101, in part by Chongqing Social Science Planning Project under Grant 2019BS053, in part by Fundamental Research Funds for the Central Universities under Grant XDJK2020B009, in part by the Chongqing Technological Innovation and Application Project under Grant cstc2018jszx-cyzdX0171, in part by Chongqing Basic and Frontier Research Project under Grant cstc2019jcyj-msxm2105, in part by the Science and Technology Research Program of Chongqing Municipal Education Commission under Grant KJQN201900816.

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Authors and Affiliations

  1. Research Center for Enterprise Management, School of Business Administration, Chongqing Technology and Business University, Chongqing, 400067, People’s Republic of China

    Wenjie Hu

  2. Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, College of Electronics and Information Engineering, Southwest University, Chongqing, 400715, People’s Republic of China

    Wenjie Hu, Xing Qiao & Tao Dong

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  1. Wenjie Hu
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  2. Xing Qiao
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  3. Tao Dong
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Correspondence to Tao Dong.

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Hu, W., Qiao, X. & Dong, T. Spatiotemporal dynamic of a coupled neutral-type neural network with time delay and diffusion. Neural Comput & Applic 33, 6415–6426 (2021). https://doi.org/10.1007/s00521-020-05404-6

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  • DOI: https://doi.org/10.1007/s00521-020-05404-6

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