Universal Critical Dynamics in High Resolution Neuronal Avalanche Data
Universal Critical Dynamics in High Resolution Neuronal Avalanche Data
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DOI：
10.1103/physrevlett.108.208102
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发表时间：
2012-05-16
影响因子：
8.6
通讯作者：
Butler, Thomas C.
中科院分区：
文献类型：
--
作者：
Friedman, Nir;Ito, Shinya;Butler, Thomas C.
神经计算的任务非常多样化。为了最佳地发挥作用，神经网络被假设在非平衡临界点附近运行。然而，临界动力学的实验证据一直是不确定的。在这里，我们表明，培养皮层网络的动力学是至关重要的。我们分析了在单个神经元水平上收集的神经网络数据，使用非平衡相变的框架。其中最引人注目的预测证实的是，广泛变化的持续时间的雪崩的平均时间分布定量描述了一个单一的通用尺度函数。我们还表明，数据有三个额外的功能预测的临界现象：近似幂律分布的雪崩的大小和持续时间，样品在亚临界和超临界阶段，和标度律之间的异常指数。
The tasks of neural computation are remarkably diverse. To function optimally, neuronal networks have been hypothesized to operate near a nonequilibrium critical point. However, experimental evidence for critical dynamics has been inconclusive. Here, we show that the dynamics of cultured cortical networks are critical. We analyze neuronal network data collected at the individual neuron level using the framework of nonequilibrium phase transitions. Among the most striking predictions confirmed is that the mean temporal profiles of avalanches of widely varying durations are quantitatively described by a single universal scaling function. We also show that the data have three additional features predicted by critical phenomena: approximate power law distributions of avalanche sizes and durations, samples in subcritical and supercritical phases, and scaling laws between anomalous exponents.