Abdul Quadir, Rahul Chhimpa, Haider Hasan Jafri, Avinash Chand Yadav
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引用次数: 0
摘要
我们研究了一个神经级模型(Das 和 Levina Phys Rev X 9:021062, 2019),它表现出满足幂律分布的临界雪崩。该模型最近解释了缩放指数从3/2到5/4的变化,说明了驱动条件从无输入到中等强度的变化,以及驱动和耗散之间时间尺度分离的放宽。我们研究了中等输入条件下一类噪声刺激的影响。应用缩放方法,我们计算了与雪崩大小分布相关的缩放函数,发现了惊人的有限大小缩放。对于一类噪声驱动,我们发现缩放指数的取值可能不同于 5/4,而且与系统大小有明确的关系。
Neuronal avalanches in level model under the influence of noisy drive
We study a neural-level model (Das and Levina Phys Rev X 9:021062, 2019) that exhibits critical avalanches satisfying power-law distribution. The model recently explained a change in the scaling exponent from 3/2 to 5/4, accounting for a change in the drive condition from no input to moderate strength, along with a relaxed separation of time scale between drive and dissipation. We examine the effect of a class of noisy stimuli in the moderate input regime. Applying the scaling method, we compute scaling functions associated with the avalanche size distribution, revealing striking finite-size scaling. For a class of noisy drives, we find that the scaling exponent can take a value different from 5/4, with an explicit system size dependence.