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Coherence resonance in stimulated neuronal network

Author

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  • Andreev, Andrey V.
  • Makarov, Vladimir V.
  • Runnova, Anastasija E.
  • Pisarchik, Alexander N.
  • Hramov, Alexander E.

Abstract

We consider a neuronal network model where an external stimulus excites some neurons, which in turn activate other neurons in the network via synapse. We find that the regularity in macroscopic spiking activity of the whole neuronal network maximizes at a certain level of intrinsic noise. A similar resonant behavior, referred to as coherence resonance, is also observed with respect to the stimulus strength, network size, and number of stimulated neurons. The coherence is quantitatively estimated with the signal-to-noise ratio calculated from the average power spectra of the macroscopic signal and with autocorrelation time. Overall synchronization in the neuronal network also exhibits a non-monotonic dependence on the network size.

Suggested Citation

  • Andreev, Andrey V. & Makarov, Vladimir V. & Runnova, Anastasija E. & Pisarchik, Alexander N. & Hramov, Alexander E., 2018. "Coherence resonance in stimulated neuronal network," Chaos, Solitons & Fractals, Elsevier, vol. 106(C), pages 80-85.
    • Handle: RePEc:eee:chsofr:v:106:y:2018:i:c:p:80-85
    DOI: 10.1016/j.chaos.2017.11.017
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    References listed on IDEAS

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    1. Runnova, Anastasiya E. & Hramov, Alexander E. & Grubov, Vadim V. & Koronovskii, Alexey A. & Kurovskaya, Maria K. & Pisarchik, Alexander N., 2016. "Theoretical background and experimental measurements of human brain noise intensity in perception of ambiguous images," Chaos, Solitons & Fractals, Elsevier, vol. 93(C), pages 201-206.
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    Cited by:

    1. Guo, Yitong & Xie, Ying & Ma, Jun, 2023. "Nonlinear responses in a neural network under spatial electromagnetic radiation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 626(C).
    2. Hramov, Alexander E. & Kulagin, Nikita & Andreev, Andrey V. & Pisarchik, Alexander N., 2024. "Forecasting coherence resonance in a stochastic Fitzhugh–Nagumo neuron model using reservoir computing," Chaos, Solitons & Fractals, Elsevier, vol. 178(C).
    3. Bashkirtseva, Irina A. & Ryashko, Lev B. & Pisarchik, Alexander N., 2020. "Ring of map-based neural oscillators: From order to chaos and back," Chaos, Solitons & Fractals, Elsevier, vol. 136(C).
    4. Jaimes-Reátegui, R. & García-López, J.H. & Gallegos, A. & Huerta Cuellar, G. & Chholak, P. & Pisarchik, A.N., 2021. "Deterministic coherence and anti-coherence resonances in networks of chaotic oscillators with frequency mismatch," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
    5. D’Onofrio, Giuseppe & Lansky, Petr & Tamborrino, Massimiliano, 2019. "Inhibition enhances the coherence in the Jacobi neuronal model," Chaos, Solitons & Fractals, Elsevier, vol. 128(C), pages 108-113.
    6. Bashkirtseva, Irina & Ryashko, Lev, 2022. "Stochastic generation and shifts of phantom attractors in the 2D Rulkov model," Chaos, Solitons & Fractals, Elsevier, vol. 159(C).
    7. Slepukhina, Evdokia & Bashkirtseva, Irina & Ryashko, Lev, 2020. "Stochastic spiking-bursting transitions in a neural birhythmic 3D model with the Lukyanov-Shilnikov bifurcation," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    8. Belyaev, Alexander & Bashkirtseva, Irina & Ryashko, Lev, 2021. "Stochastic variability of regular and chaotic dynamics in 2D metapopulation model," Chaos, Solitons & Fractals, Elsevier, vol. 151(C).
    9. Masoliver, Maria & Masoller, Cristina & Zakharova, Anna, 2021. "Control of coherence resonance in multiplex neural networks," Chaos, Solitons & Fractals, Elsevier, vol. 145(C).
    10. Slepukhina, Evdokiia & Bashkirtseva, Irina & Ryashko, Lev & Kügler, Philipp, 2022. "Stochastic mixed-mode oscillations in the canards region of a cardiac action potential model," Chaos, Solitons & Fractals, Elsevier, vol. 164(C).

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