Refractory period in network models of excitable nodes: self-sustaining stable dynamics, extended scaling region and oscillatory behavior.
Refractory period in network models of excitable nodes: self-sustaining stable dynamics, extended scaling region and oscillatory behavior.
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DOI：
10.1038/s41598-017-07135-6
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发表时间：
2017-08-02
影响因子：
4.6
通讯作者：
Valizadeh A
中科院分区：
文献类型：
--
作者：
Moosavi SA;Montakhab A;Valizadeh A
可兴奋节点网络最近引起了广泛关注，特别是在神经元动力学方面，其中临界性被认为是一个基本属性。限制神经元兴奋性的不应性行为被认为是一种重要的动力学特性。因此，我们考虑一个简单的可兴奋节点模型，已知该模型在临界点（λ = 1）表现出向不稳定的转变，并将不应期引入其动力学。我们使用平均场分析计算以及数值模拟来计算活动相关的支化比，这对于表征关键系统的行为很有用。我们还定义雪崩并计算其大小和持续时间的概率分布。我们发现，在存在不应期的情况下，动力学稳定，同时各种参数状态变得可访问。 λ < 1.0 的亚临界状态、指数接近 λ = 1 的临界分支过程的标准临界行为、表现出有趣的缩放行为的 1 < λ < 2 的状态以及 λ > 2.0 的振荡状态。因此，我们表明，不应行为会导致广泛的缩放以及与真实神经元动力学相关的周期性行为。
Networks of excitable nodes have recently attracted much attention particularly in regards to neuronal dynamics, where criticality has been argued to be a fundamental property. Refractory behavior, which limits the excitability of neurons is thought to be an important dynamical property. We therefore consider a simple model of excitable nodes which is known to exhibit a transition to instability at a critical point (λ = 1), and introduce refractory period into its dynamics. We use mean-field analytical calculations as well as numerical simulations to calculate the activity dependent branching ratio that is useful to characterize the behavior of critical systems. We also define avalanches and calculate probability distribution of their size and duration. We find that in the presence of refractory period the dynamics stabilizes while various parameter regimes become accessible. A sub-critical regime with λ < 1.0, a standard critical behavior with exponents close to critical branching process for λ = 1, a regime with 1 < λ < 2 that exhibits an interesting scaling behavior, and an oscillating regime with λ > 2.0. We have therefore shown that refractory behavior leads to a wide range of scaling as well as periodic behavior which are relevant to real neuronal dynamics.
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影响因子：
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作者：
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通讯作者：
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