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Stochastic Bifurcations and Noise-Induced Chaos in 3D Neuron Model

机译:三维神经元模型中的随机分岔与噪声诱发混沌

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摘要

The stochastically forced three-dimensional Hindmarsh–Rose model of neural activity is considered. We study the effect of random disturbances in parametric zones where the deterministic model exhibits mono- and bistable dynamic regimes with period-adding bifurcations of oscillatory modes. It is shown that in both cases the phenomenon of noise-induced bursting is observed. In the monostable zone, where the only attractor of the system is a stable equilibrium, this effect is connected with a stochastic generation of large-amplitude oscillations due to the high excitability of the model. In a parametric zone of coexisting stable equilibria and limit cycles, bursts appear due to noise-induced transitions between the attractors. For a quantitative analysis of the noise-induced bursting and corresponding stochastic bifurcations, an approach based on the stochastic sensitivity function (SSF) technique is applied. Our estimations of the strength of noise that generates such qualitative changes in stochastic dynamics are in a good agreement with the direct numerical simulation. A relationship of the noise-induced generation of bursts with transitions from order to chaos is discussed.
机译:考虑了神经活动的随机强迫三维Hindmarsh-Rose模型。我们研究了参数化区域中随机扰动的影响,在该区域中,确定性模型表现出单稳态和双稳态动态模式,且振荡模式的周期增加了分叉。结果表明,在两种情况下都观察到了噪声引起的爆裂现象。在单稳态区域,系统的唯一吸引子是稳定的平衡,由于模型的高激励性,这种影响与大振幅振荡的随机生成有关。在同时存在稳定平衡和极限环的参数区域中,由于噪声引起的吸引子之间的过渡而出现爆发。为了定量分析噪声引起的爆发和相应的随机分叉,应用了一种基于随机敏感性函数(SSF)技术的方法。我们对在随机动力学中产生这种质变的噪声强度的估计与直接数值模拟非常吻合。讨论了由噪声引起的突发的产生与从阶跃到混沌的关系。

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