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Effect of topology on delay-induced multiple resonances in locally driven systems

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  • Yu, Dong
  • Wu, Yong
  • Yang, Lijian
  • Zhao, Yunjie
  • Jia, Ya

Abstract

Information transmission delays are inherent in neuronal systems and significantly influence their dynamic properties. Recent studies have shown that delay-induced multiple resonances occur when delays in synaptic transmission are taken into account. We reveal the influence of topology on the delay-induced multiple resonances phenomenon in locally driven systems in this paper. It is expressed as the maximum value of the Fourier coefficient, which occurs at an integer multiple of T/N, where T is the external signal period or the neuronal intrinsic oscillation period, N is determined by the topology of the network. We briefly discuss the obtained results with the underlying reasons in terms of the network’s topology. The emergence of delay-induced multiple resonances depends on the interaction of the delay enhancement effect and the delay inhibition effect. In the case of weak coupling, the signal response performance is enhanced over a wider range of delay windows, which is dominated by delay enhancement effects. These results may provide a novel perspective on weak signal response and transmission in delay-coupled neural networks.

Suggested Citation

  • Yu, Dong & Wu, Yong & Yang, Lijian & Zhao, Yunjie & Jia, Ya, 2023. "Effect of topology on delay-induced multiple resonances in locally driven systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).
    • Handle: RePEc:eee:phsmap:v:609:y:2023:i:c:s0378437122008883
    DOI: 10.1016/j.physa.2022.128330
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    1. Yu, Dong & Wang, Guowei & Ding, Qianming & Li, Tianyu & Jia, Ya, 2022. "Effects of bounded noise and time delay on signal transmission in excitable neural networks," Chaos, Solitons & Fractals, Elsevier, vol. 157(C).
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    Cited by:

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    2. Ding, Qianming & Wu, Yong & Hu, Yipeng & Liu, Chaoyue & Hu, Xueyan & Jia, Ya, 2023. "Tracing the elimination of reentry spiral waves in defibrillation: Temperature effects," Chaos, Solitons & Fractals, Elsevier, vol. 174(C).
    3. Wang, Xueqin & Yu, Dong & Li, Tianyu & Jia, Ya, 2023. "Logistic stochastic resonance in the Hodgkin–Huxley neuronal system under electromagnetic induction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 630(C).
    4. Li, Tianyu & Wu, Yong & Yang, Lijian & Fu, Ziying & Jia, Ya, 2023. "Neuronal morphology and network properties modulate signal propagation in multi-layer feedforward network," Chaos, Solitons & Fractals, Elsevier, vol. 172(C).
    5. Cambraia, E.B.S.A. & Flauzino, J.V.V. & Prado, T.L. & Lopes, S.R., 2023. "Dependence on the local dynamics of a network phase synchronization process," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 619(C).

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