论文标题
统计复杂性在皮质动力学中最大接近临界
Statistical complexity is maximized close to criticality in cortical dynamics
论文作者
论文摘要
复杂的系统通常被描述为完整的常规结构和随机系统之间的中间情况。可以将大脑信号作为此类系统的一个引人注目的例子进行研究：皮质状态可以从高度同步和有序的神经元活性（具有较高的峰值变异性）到异步和无序的状态（具有较低的峰值变异性）。最近，通过测试独立的临界性特征，表明相变发生在中间尖峰变异性的皮质状态下。在这里，我们使用一种符号信息方法来表明，尽管有序和无序的制度之间的香农熵单调增加，但我们仍可以根据Jensen不平衡度量来确定最大复杂性的中间状态。更具体地说，我们表明，对于氨基甲酸酯 - 麻醉大鼠的皮质尖峰数据，以及令人兴奋的元素的网络模型，统计复杂性被最大化接近关键，并提出了非平衡相变的关键点。
Complex systems are typically characterized as an intermediate situation between a complete regular structure and a random system. Brain signals can be studied as a striking example of such systems: cortical states can range from highly synchronous and ordered neuronal activity (with higher spiking variability) to desynchronized and disordered regimes (with lower spiking variability). It has been recently shown, by testing independent signatures of criticality, that a phase transition occurs in a cortical state of intermediate spiking variability. Here, we use a symbolic information approach to show that, despite the monotonical increase of the Shannon entropy between ordered and disordered regimes, we can determine an intermediate state of maximum complexity based on the Jensen disequilibrium measure. More specifically, we show that statistical complexity is maximized close to criticality for cortical spiking data of urethane-anesthetized rats, as well as for a network model of excitable elements that presents a critical point of a non-equilibrium phase transition.
