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Dynamical robustness and firing modes in multilayer memristive neural networks of nonidentical neurons

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  • Liu, Yuanyuan
  • Sun, Zhongkui
  • Yang, Xiaoli
  • Xu, Wei

Abstract

On the basis of Hindmarsh-Rose neuron model, dynamical robustness and the transition of firing modes of multilayer memristive neural network consisting of nonidentical neurons have been investigated in detail. The dynamic effects of memristive synapses coupling which are either cubic order flux or quadratic flux are detected. Our results suggest that for the cubic order flux-controlled memristors increasing electromagnetic induction parameter and parameter of memristor show the tendency to spoil the dynamical robustness of the multilayer memristive neural network. For quadratic flux-controlled memristors, weak electromagnetic induction can spoil the dynamical robustness while strong electromagnetic induction and parameter of memristor have little influence on dynamical robustness. We found that the ratio of inactive neurons switches the firing patterns of the active neuron among periodic bursting, chaotic bursting and spiking-like. In the case of memristive synapse coupling by cubic order flux-controlled memristors, chaotic bursting alternates with period bursting as the ratio of inactive neurons is increased, and the chaotic bursting occupy a large range of the ratio of inactive neurons. In the case of memristive synapse coupling by quadratic flux-controlled memristors, the main firing pattern of the active neuron is chaotic bursting. The distribution of interspike interval can be broadly classified into two groups: those that are long interspike intervals and those that are short interspike intervals. The dynamics of multilayer memristive neural network is also verified in the circuits built on Multisim. These results could give new mechanism explanation for aging transition by applying electromagnetic field to neural network.

Suggested Citation

  • Liu, Yuanyuan & Sun, Zhongkui & Yang, Xiaoli & Xu, Wei, 2021. "Dynamical robustness and firing modes in multilayer memristive neural networks of nonidentical neurons," Applied Mathematics and Computation, Elsevier, vol. 409(C).
    • Handle: RePEc:eee:apmaco:v:409:y:2021:i:c:s0096300321004732
    DOI: 10.1016/j.amc.2021.126384
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    References listed on IDEAS

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    1. Dmitri B. Strukov & Gregory S. Snider & Duncan R. Stewart & R. Stanley Williams, 2008. "The missing memristor found," Nature, Nature, vol. 453(7191), pages 80-83, May.
    2. He, Zhiwei & Liu, Shuai & Zhan, Meng, 2013. "Dynamical robustness analysis of weighted complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(18), pages 4181-4191.
    3. Takeyuki Sasai & Kai Morino & Gouhei Tanaka & Juan A Almendral & Kazuyuki Aihara, 2015. "Robustness of Oscillatory Behavior in Correlated Networks," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-21, April.
    4. Parastesh, Fatemeh & Azarnoush, Hamed & Jafari, Sajad & Hatef, Boshra & Perc, Matjaž & Repnik, Robert, 2019. "Synchronizability of two neurons with switching in the coupling," Applied Mathematics and Computation, Elsevier, vol. 350(C), pages 217-223.
    5. Ma, Jun & Mi, Lv & Zhou, Ping & Xu, Ying & Hayat, Tasawar, 2017. "Phase synchronization between two neurons induced by coupling of electromagnetic field," Applied Mathematics and Computation, Elsevier, vol. 307(C), pages 321-328.
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    1. Wang, Yuexin & Sun, Zhongkui & Zhang, Hanqi & Zhou, Yining & Liu, Shutong & Xu, Wei, 2024. "Dynamic survivability of two-layer networks: The role of interlayer coupling," Chaos, Solitons & Fractals, Elsevier, vol. 180(C).
    2. Yu, Fei & Kong, Xinxin & Yao, Wei & Zhang, Jin & Cai, Shuo & Lin, Hairong & Jin, Jie, 2024. "Dynamics analysis, synchronization and FPGA implementation of multiscroll Hopfield neural networks with non-polynomial memristor," Chaos, Solitons & Fractals, Elsevier, vol. 179(C).

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