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Inhibition enhances the coherence in the Jacobi neuronal model

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  • D’Onofrio, Giuseppe
  • Lansky, Petr
  • Tamborrino, Massimiliano

Abstract

The output signal is examined for the Jacobi neuronal model which is characterized by input-dependent multiplicative noise. The dependence of the noise on the rate of inhibition turns out to be of primary importance to observe maxima both in the output firing rate and in the diffusion coefficient of the spike count and, simultaneously, a minimum in the coefficient of variation (Fano factor). Moreover, we observe that an increment of the rate of inhibition can increase the degree of coherence computed from the power spectrum. This means that inhibition can enhance the coherence and thus the information transmission between the input and the output in this neuronal model. Finally, we stress that the firing rate, the coefficient of variation and the diffusion coefficient of the spike count cannot be used as the only indicator of coherence resonance without considering the power spectrum.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:chsofr:v:128:y:2019:i:c:p:108-113
    DOI: 10.1016/j.chaos.2019.07.040
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    References listed on IDEAS

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    1. Cofré, Rodrigo & Cessac, Bruno, 2013. "Dynamics and spike trains statistics in conductance-based integrate-and-fire neural networks with chemical and electric synapses," Chaos, Solitons & Fractals, Elsevier, vol. 50(C), pages 13-31.
    2. 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.
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    4. Xie, Huijuan & Gong, Yubing & Wang, Baoying, 2018. "Spike-timing-dependent plasticity optimized coherence resonance and synchronization transitions by autaptic delay in adaptive scale-free neuronal networks," Chaos, Solitons & Fractals, Elsevier, vol. 108(C), pages 1-7.
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    Cited by:

    1. Rajdl, Kamil & Kostal, Lubomir, 2023. "Estimation of the instantaneous spike train variability," Chaos, Solitons & Fractals, Elsevier, vol. 177(C).

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