Stochastic resonance induced by exogenous noise in a model of a neuronal network

This paper primarily investigates the inverse stochastic resonance (ISR) of neuron network driven by Lévy noise with electrical autapse and chemical autapse, respectively. Firstly, the discharge of Hodgkin–Huxley (HH) neuron network under different noise parameters, autapse parameters and network coupling strength is shown. Then, the variation of average firing rate with Lévy noise in the case of electrical autapse and chemical autapse is presented. We find that there exists a minimum value of the average firing rate curve caused by stability index and noise intensity of Lévy noise across the whole network, which is the phenomenon of ISR. With the increase of autaptic intensity and coupling strength, the ISR inhibitory effect of neuron discharge is weakened. In addition, with the increase of coupling strength, the neuron discharge of neural network is more intense and regular. As a consequence, our work suggests that autaptic intensity and coupling efficient of neuronal network can regulate the neuronal firing activities and suppress the effect of ISR, and Lévy noise can induce ISR phenomenon in Newman–Watts neuronal network.

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Stochastic resonance in feedforward-loop neuronal network motifs in astrocyte field

Journal of Theoretical Biology ◽

10.1016/j.jtbi.2013.07.007 ◽

2013 ◽

Vol 335 ◽

pp. 265-275 ◽

Cited By ~ 9

Author(s):

Ying Liu ◽

Chunguang Li

Keyword(s):

Stochastic Resonance ◽

Neuronal Network ◽

Network Motifs ◽

Feedforward Loop

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How electromagnetic induction and coupled delay affect stochastic resonance in a modified neuronal network subject to phase noise

International Journal of Modern Physics B ◽

10.1142/s0217979219503028 ◽

2019 ◽

Vol 33 (26) ◽

pp. 1950302

Author(s):

Xiao Li Yang ◽

Xiao Qiang Liu

Keyword(s):

Time Delay ◽

Phase Noise ◽

Stochastic Resonance ◽

Neuronal Network ◽

Electromagnetic Induction ◽

Small World ◽

Feedback Gain ◽

Small World Network ◽

Induced Current ◽

Resonance Response

Through introducing the ingredients of electromagnetic induction and coupled time delay into the original Fitzhugh–Nagumo (FHN) neuronal network, the dynamics of stochastic resonance in a model of modified FHN neuronal network in the environment of phase noise is explored by numerical simulations in this study. On one hand, we demonstrate that the phenomenon of stochastic resonance can appear when the intensity of phase noise is appropriately adjusted, which is further verified to be robust to the edge-added probability of small-world network. Moreover, under the influence of electromagnetic induction, the phase noise-induced resonance response is suppressed, meanwhile, a large noise intensity is required to induce stochastic resonance as the feedback gain of induced current increases. On the other hand, when the coupled time delay is incorporated into this model, the results indicate that the properly tuned time delay can induce multiple stochastic resonances in this neuronal network. However, the phenomenon of multiple stochastic resonances is found to be restrained upon increasing feedback gain of induced current. Surprisingly, by changing the period of phase noise, multiple stochastic resonances can still emerge when the coupled time delay is appropriately tuned to be integer multiples of the period of phase noise.

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