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Non-Gaussian noise and autapse-induced inverse stochastic resonance in bistable Izhikevich neural system under electromagnetic induction

Author

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  • Wang, Guowei
  • Wu, Yong
  • Xiao, Fangli
  • Ye, Zhiqiu
  • Jia, Ya

Abstract

Inverse stochastic resonance refers to the phenomenon that the average firing rate of a neuron is inhibited by noise, of which mechanism is widely used in a variety of biological cells and economic phenomena. In this paper, a bistable Izhikevich neural model and triple-neuron feed-forward loop Izhikevich neural network motifs under the effects of electromagnetic induction are constructed to investigate the phenomenon of inverse stochastic resonance induced by Non-Gaussian colored noise and electrical autapse. It is found that there exists a minimum value of the average firing rate curve caused by intensity of non-Gaussian colored noise, which is the phenomenon of inverse stochastic resonance. Obtained results also show that the inverse stochastic resonance induced by electrical autapse shows a decaying oscillation process with respect to synaptic delay time, and further research indicates that average firing rate has several minimums as a function of time delay of electrical autapse, which is called multiple inverse stochastic resonance. Furthermore, the inverse stochastic resonance in triple-neuron feed-forward loop Izhikevich neural network motifs are also examined, and it is confirmed that the responses of single Izhikevich neuron and neural network motifs to different parameters show consistency under same conditions, but also show some differences. Finally, the effects of electromagnetic induction on inverse stochastic resonance are checked both in single Izhikevich neural model and feed-forward loop network motifs. Electromagnetic induction feedback gain coefficient k1 should not be too large under certain conditions, otherwise it may cause FFL network motifs to loss the function of suppressing the discharge activity. No matter how the values of electromagnetic induction parameter k2 and magnetic flux leakage coefficient k3 change, they basically cannot affect the ISR in the feed-forward loop neural network motifs The conclusions of this paper may help researchers understanding how using unique mechanism of inverse stochastic resonance to find advantages and avoid disadvantages in biomedical field and many interdisciplinary research.

Suggested Citation

  • Wang, Guowei & Wu, Yong & Xiao, Fangli & Ye, Zhiqiu & Jia, Ya, 2022. "Non-Gaussian noise and autapse-induced inverse stochastic resonance in bistable Izhikevich neural system under electromagnetic induction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 598(C).
    • Handle: RePEc:eee:phsmap:v:598:y:2022:i:c:s0378437122002369
    DOI: 10.1016/j.physa.2022.127274
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    3. Li, Tianyu & Wu, Yong & Yang, Lijian & Zhan, Xuan & Jia, Ya, 2022. "Spike-timing-dependent plasticity enhances chaotic resonance in small-world network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 606(C).
    4. Ding, Qianming & Wu, Yong & Li, Tianyu & Yu, Dong & Jia, Ya, 2023. "Metabolic energy consumption and information transmission of a two-compartment neuron model and its cortical network," Chaos, Solitons & Fractals, Elsevier, vol. 171(C).
    5. 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).
    6. 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).
    7. Baysal, Veli & Solmaz, Ramazan & Ma, Jun, 2023. "Investigation of chaotic resonance in Type-I and Type-II Morris-Lecar neurons," Applied Mathematics and Computation, Elsevier, vol. 448(C).
    8. Shadizadeh, S. Mohadeseh & Nazarimehr, Fahimeh & Jafari, Sajad & Rajagopal, Karthikeyan, 2022. "Investigating different synaptic connections of the Chay neuron model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
    9. Xiao, Fangli & Fu, Ziying & Jia, Ya & Yang, Lijian, 2023. "Resonance effects in neuronal-astrocyte model with ion channel blockage," Chaos, Solitons & Fractals, Elsevier, vol. 166(C).
    10. 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).
    11. 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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