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Inhibitory-autapse-enhanced signal transmission in neural networks

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Abstract

The multi-frequency hybrid signal is an important stimulus from the external environment on the neuronal networks for detection. The mechanism of the detection may be understood by the vibrational resonance, in which the moderate intensity of high-frequency force can amplify the response of neuronal systems to the low-frequency signal. In this paper, the effects of electrical and chemical autapses on signal transmission are investigated in scale-free and small-world neuronal networks, where an external two-frequency signal is introduced only to one neuron as a pacemaker. We observed that the inhibitory autapse can significantly enhance the signal propagation by the vibrational resonance, while the electrical and excitatory autapses typically weaken the signal transmission, indicating that the inhibitory autapse is more beneficial to transmit the rhythm of the pacemaker to the whole networks. These findings contribute to our understanding of signal detection and information processing in the autapic neuronal system.

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Acknowledgements

This work was supported partially by the National Natural Science Foundation of China under Grant Nos. 11675112, 11675134 and 11874310; and the 111 Project under Grant No. B16029.

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Authors and Affiliations

  1. Department of Mathematics, Shaoxing University, Shaoxing, China

    Chenggui Yao & Zhiwei He

  2. Graduate School of Frontier Biosciences, Osaka University, Osaka, Japan

    Tadashi Nakano

  3. Nonlinear Research Institute, Baoji University of Arts and Sciences, Baoji, 721007, China

    Yu Qian

  4. Department of Physics, Xiamen University, Xiamen, China

    Jianwei Shuai

  5. Research Institute for Biomimetics and Soft Matter, Fujian Provincial Key Laboratory for Soft Functional Materials Research, Xiamen University, Xiamen, 361005, China

    Jianwei Shuai

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  1. Chenggui Yao
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  2. Zhiwei He
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Correspondence to Jianwei Shuai.

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Yao, C., He, Z., Nakano, T. et al. Inhibitory-autapse-enhanced signal transmission in neural networks. Nonlinear Dyn 97, 1425–1437 (2019). https://doi.org/10.1007/s11071-019-05060-z

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