论文标题
噪音驱动器下的关键神经元雪崩
Critical neuronal avalanches in levels model under noisy drive
论文作者
论文摘要
我们考虑了一种神经元水平模型，该模型表现出关键的雪崩，可满足幂律分布。该模型最近解释了缩放指数从3/2到5/4的变化，这是驱动条件从没有输入到中等强度的变化，以及驱动器和耗散之间的时间尺度的放松分离。为了了解缩放特征的鲁棒性，我们检查了中等输入方案中不同噪声刺激的效果。我们的分析工具是缩放方法。我们计算与雪崩尺寸分布相关的缩放缩放功能，从而揭示了惊人的有限尺寸缩放。对于一类嘈杂的驱动器，我们发现缩放指数可以采用与5/4不同的值，具有明确的分布系统大小依赖性。
We consider a neuronal levels model that exhibits critical avalanches satisfying power-law distribution. The model has 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. To understand the robustness of the scaling features, we examine the effect of different noisy stimuli in the moderate input regime. Our tool of analysis is 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 of the distribution.
