Effect of time delay in FitzHugh–Nagumo neural model with correlations between multiplicative and additive noises

Zeng, Chunhua; Zeng, Chunping; Gong, Ailing; Nie, Linru

doi:10.1016/j.physa.2010.07.031

Cited by 27 publications

(9 citation statements)

References 52 publications

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“…It can be seen that the SNR also varies non-monotonically with the growth of the noise intensities, a similar effect was shown in Ref. [19]. The maximum value of SNR shifts to strong noise strength 𝐷 and 𝑃 with the increase of the system parameter 𝑎 and 𝑏.…”

supporting

confidence: 82%

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Vibrational and Stochastic Resonance in the FitzHugh—Nagumo Neural Model with Multiplicative and Additive Noise

He¹,

Zhou²

2011

Chinese Phys. Lett.

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The vibrational resonance and stochastic resonance phenomena in the FitzHugh-Nagumo (FHN) neural model, driven by a high-frequency (HF) signal and a low-frequency (LF) signal and by coupled multiplicative and additive noises, is investigated. For the case that the frequency of the HF signal is much higher than that of the LF signal, under the adiabatic approximation condition, the expression of the signal-to-noise ratio (SNR) with respect to the LF signal is obtained. It is shown that the SNR is a non-monotonous function of the amplitude and frequency of the HF signal. In addition, the SNR varies non-monotonically with the increasing intensities of the multiplicative and additive noise as well as with the increasing system parameters of the FHN model. The influence of the coupling strength between the multiplicative and additive noises on the SNR is discussed.

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supporting

confidence: 82%

“…Since HF signal does not appear in the systems of Refs. [12][13][14][15][16][17][18][19], the VR phenomena presented here are new results in the FHN model. The non-monotonous dependence of the SNR on the strengths of the multiplicative and additive noises is also investigated.…”

mentioning

confidence: 78%

Vibrational and Stochastic Resonance in the FitzHugh—Nagumo Neural Model with Multiplicative and Additive Noise

He¹,

Zhou²

2011

Chinese Phys. Lett.

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“…The two-effector model can evoke a speed-up in RT if it bifurcates to a novel stable state. Evidence for such a scenario is independently gained from various sources [37,84,85] by studying neural fields. We take two neurons 1 and 2 along with the generic two-variable reaction–diffusion model for excitable and inhibitory media—the FritzHugh-Nagumo model (1)—such that the excitory field 1 is modeled with a membrane potential x1 at time n.…”

Section: Resultsmentioning

confidence: 99%

Reaction Time Improvements by Neural Bistability

Koppelaar

Kordestani-Moghadam

Khan

et al. 2019

Behavioral Sciences

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The often reported reduction of Reaction Time (RT) by Vision Training) is successfully replicated by 81 athletes across sports. This enabled us to achieve a mean reduction of RTs for athletes eye-hand coordination of more than 10%, with high statistical significance. We explain how such an observed effect of Sensorimotor systems’ plasticity causing reduced RT can last in practice for multiple days and even weeks in subjects, via a proof of principle. Its mathematical neural model can be forced outside a previous stable (but long) RT into a state leading to reduced eye-hand coordination RT, which is, again, in a stable neural state.

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“…In fact, the Ca 2+ oscillating in astrocyte is much complex when uncertain perturbation such as noise and time delay is considered [51][52][53]. Different disturbances on excitable media are often described by applying different types of noise (additive or multiplicative) on the media and possible statistical properties are discussed [54]. Mutual coupling between astrocyte and neuron driven by autapse can trigger complex stimuli for neuron, astrocyte by setting different external forcing currents, and time delay and feedback gain in autapse; as a result, the response of electrical activities becomes more complex.…”

Section: It Is Confirmed That Oscillating Behavior Of Camentioning

confidence: 99%

Autaptic Modulation of Electrical Activity in a Network of Neuron-Coupled Astrocyte

Guo

Tang

et al. 2017

Complexity

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Autapse connection is considered on a biological neuron coupled by astrocyte, and the effect of autapse driving-induced response in electrical activities is investigated. In this paper, a simple network is developed on the Hodgkin-Huxley (HH) neuron coupled by astrocyte and the autapse effect is also considered. The modulation of autapse connected to HH neuron can change the membrane potential by applying time-delayed feedback along a close loop. It is found that the self-adaption of autapse driving can make the network of neuron-astrocyte generate different modes of electrical activities, and oscillating behavior of Ca 2+ and IP 3 setting is controlled. This new network model can give potential understanding about self-adaption of neuron to external forcing when the coupling of astrocyte and autapse is considered.

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Effect of time delay in FitzHugh–Nagumo neural model with correlations between multiplicative and additive noises

Cited by 27 publications

References 52 publications

Vibrational and Stochastic Resonance in the FitzHugh—Nagumo Neural Model with Multiplicative and Additive Noise

Vibrational and Stochastic Resonance in the FitzHugh—Nagumo Neural Model with Multiplicative and Additive Noise

Reaction Time Improvements by Neural Bistability

Autaptic Modulation of Electrical Activity in a Network of Neuron-Coupled Astrocyte

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