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Resonant activation in a stochastic Hodgkin-Huxley model: Interplay between noise and suprathreshold driving effects

Author

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  • E. V. Pankratova
  • A. V. Polovinkin
  • E. Mosekilde

Abstract

The paper considers an excitable Hodgkin-Huxley system subjected to a strong periodic forcing in the presence of random noise. The influence of the forcing frequency on the response of the system is examined in the realm of suprathreshold amplitudes. Our results confirm that the presence of noise has a detrimental effect on the neuronal response. Fluctuations can induce significant delays in the detection of an external signal. We demonstrate, however, that this negative influence may be minimized by a resonant activation effect: Both the mean escape time and its standard deviation exhibit a minimum as functions of the forcing frequency. The destructive influence of noise on the interspike interval can also be reduced. With driving signals in a certain frequency range, the system can show stable periodic spiking even for relatively large noise intensities. Outside this frequency range, noise of similar intensity destroys the regularity of the spike trains by suppressing the generation of some of the spikes. Copyright EDP Sciences/Società Italiana di Fisica/Springer-Verlag 2005

Suggested Citation

  • E. V. Pankratova & A. V. Polovinkin & E. Mosekilde, 2005. "Resonant activation in a stochastic Hodgkin-Huxley model: Interplay between noise and suprathreshold driving effects," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 45(3), pages 391-397, June.
    • Handle: RePEc:spr:eurphb:v:45:y:2005:i:3:p:391-397
    DOI: 10.1140/epjb/e2005-00187-2
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    Cited by:

    1. Baysal, Veli & Yılmaz, Ergin, 2021. "Chaotic Signal Induced Delay Decay in Hodgkin-Huxley Neuron," Applied Mathematics and Computation, Elsevier, vol. 411(C).
    2. Yilmaz, Ergin, 2014. "Impacts of hybrid synapses on the noise-delayed decay in scale-free neural networks," Chaos, Solitons & Fractals, Elsevier, vol. 66(C), pages 1-8.
    3. Andreev, Andrey V. & Ivanchenko, Mikhail V. & Pisarchik, Alexander N. & Hramov, Alexander E., 2020. "Stimulus classification using chimera-like states in a spiking neural network," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    4. Uzuntarla, Muhammet & Uzun, Rukiye & Yilmaz, Ergin & Ozer, Mahmut & Perc, Matjaž, 2013. "Noise-delayed decay in the response of a scale-free neuronal network," Chaos, Solitons & Fractals, Elsevier, vol. 56(C), pages 202-208.
    5. Wang, Min & Fang, Yuwen & Luo, Yuhui & Yang, Fengzao & Zeng, Chunhua & Duan, Wei-Long, 2019. "Influence of non-Gaussian noise on the coherent feed-forward loop with time delay," Chaos, Solitons & Fractals, Elsevier, vol. 129(C), pages 46-55.
    6. 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).
    7. Yao, Chenggui & Sun, JianQiang & Jin, Jun & Shuai, Jianwei & Li, Xiang & Yao, Yuangen & Xu, Xufan, 2023. "The power law statistics of the spiking timing in a neuronal network," Chaos, Solitons & Fractals, Elsevier, vol. 172(C).

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