Coherence-resonance chimeras in coupled HR neurons with alpha-stable Lévy noise

Wang, Zhanqing; Li, Yongge; Xu, Yong; Kapitaniak, Tomasz; Kurths, Jürgen

doi:10.1088/1742-5468/ac6254

Cited by 22 publications

(6 citation statements)

References 41 publications

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“…Interactions among the nodes of the SIR network model represent the movement of the infected and susceptible individual in space, and these theoretical references can provide insights for better mitigation strategies [16]. This method is implemented in other biological systems to investigate the emergence of patterns due to node interactions [18][19][20][21]. Alternative methods were proposed to indicate the generation mechanisms of Turing instability and patterns [22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal dynamics of periodic waves in SIR model with driving factors

et al. 2023

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The world is facing Covid-19 waves and the overall pattern of confirmed cases shows periodic oscillations. In this paper, we investigate the spatio-temporal spread of Covid-19 in the network-organized SIR model with an extrinsic incubation period of the driving factors. Firstly, Our analysis shows the occurrence of Hopf bifurcation and periodic outbreaks which is consistent with the actual spread of Covid-19. And we investigate periodic waves on spatial scales using Turing instability, and the spread of infected individuals increases the localized hot spots. We study the effect of the incubation period, more incubation periods generate Turing instability resulting in periodic outbreaks. There is an occurrence of bursting states at peaks of periodic waves due to small diffusion of infected and susceptible, which means stable and unstable areas try to convert each other due to high competition among nodes. Also, We note the disappearance of these bursts when infected and susceptible individuals' movements are easier, thus dominance of infected individuals prevails everywhere. Effective policy interventions and seasonality can cause periodic perturbations in the model, and therefore we study the impact of these perturbations on the spread of Covid-19. Periodic perturbations on the driving factors, infected individuals show co-existing spatial patterns. Chaotic outbreak becomes periodic outbreaks through alternating periodic or period-2 outbreaks as we regulate the amplitude and frequency of infected individuals. In short, regulations can erase period-2 and chaotic spread through policy interventions.

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Section: Introductionmentioning

confidence: 99%

Spatiotemporal dynamics of periodic waves in SIR model with driving factors

et al. 2023

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“…The Integrate-and-Fire neuron model proposed by Louis Lapicque in the early 20th century and the Hodgkin-Huxley (HH) ion channel model established by Hodgkin and Huxley in 1952 have been crucial in the study of neuron theory [18]. The HH model allows for computer simulation of biological neural network firing, and stochastic resonance has been studied in both single and coupled neurons using various theoretical models such as the FitzHugh-Nagumo (FHN) model [19][20][21][22], Integrate-and-Fire (IF) model [23][24][25][26][27], and Hindmarsh-Rose (HR) model [28][29][30][31][32][33]. Dtchetgnia et al [28].…”

Section: Introductionmentioning

confidence: 99%

Stochastic resonance in Hindmarsh-Rose neural model driven by multiplicative and additive Gaussian noise

Xu,

Zhang,

et al. 2023

Phys. Scr.

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This paper investigates the occurrence of stochastic resonance in the three-dimensional Hindmarsh-Rose (HR) neural model driven by both multiplicative and additive Gaussian noise. Firstly, the three-dimensional HR neural model is transformed into the one-dimensional Langevin equation of the HR neural model using the adiabatic elimination method, and the effects of HR neural model parameters on the potential function are analyzed. Secondly the Steady-state Probability Density (SPD), the Mean First-Passage Time (MFPT), and the Signal-to-Noise Ratio (SNR) of the HR neural model are derived, based on two-state theory. Then, the effects of different parameters (a, b, c, s), noise intensity, and the signal amplitude on these metrics are analyzed through theoretical simulations, and the behavior of particles in a potential well is used to analyze how to choose the right parameters to achieve high-performance stochastic resonance. Finally, numerical simulations conducted with the fourth-order Runge–Kutta algorithm demonstrate the superiority of the HR neural model over the classical bistable stochastic resonance (CBSR) in terms of performance. The peak SNR of the HR neural model is 0.63 dB higher than that of the CBSR system. Simulation results indicate that the occurrence of stochastic resonance occur happens in HR neural model under different values of parameters. Furthermore, under certain conditions, there is a ‘suppress’ phenomenon that can be produced by changes in noise, which provides great feasibilities and practical value for engineering application.

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“…Classical examples of systems displaying heavy-tailed fluctuations of the α-stable type includes, but are not limited to, turbulent fluid flows [31][32][33][34], magnetized plasmas [35,36], optical lattices [37], heartbeat dynamics [38], neural networks [39], search on a folding polymers [40], animal movement [41], climate dynamics [42], financial time series [43], spreading of diseases and dispersal of banknotes [44]. α-stable variables are also used in financial markets [45], portfolio optimization [46] and neuroscience [47,48].…”

Section: Introductionmentioning

confidence: 99%

Escape from the potential well: accelerating by shaping and noise tuning

Dybiec

Zawiślak²

2023

Phys. Scr.

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Noise driven escape from the potential well is the basic component of various noise induced effects. The efficiency of the escape process or time scales matching is responsible for occurrence of the stochastic resonance and (stochastic) resonant activation. Here, we are extending the discussion on how the structure of the potential can be used to optimize the mean first passage time. It is demonstrated that corrugation of the potential can be beneficial under action of the weak Gaussian white noise. Furthermore, we show that the noise tuning can be more effective than shaping the potential. Therefore, action of the tuned additive $\alpha$-stable noise can accelerate the escape kinetics more than corrugation of the potential. Finally, we demonstrate that mean first passage time from a potential well can be a non-monotonous function of the stability index $\alpha$.

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Coherence-resonance chimeras in coupled HR neurons with alpha-stable Lévy noise

Cited by 22 publications

References 41 publications

Spatiotemporal dynamics of periodic waves in SIR model with driving factors

Spatiotemporal dynamics of periodic waves in SIR model with driving factors

Stochastic resonance in Hindmarsh-Rose neural model driven by multiplicative and additive Gaussian noise

Escape from the potential well: accelerating by shaping and noise tuning

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