Research Institute for Technical Physics and Materials Science, Centre for Energy Research of the Hungarian Academy of Sciences, P.O. Box 49, H-1525 Budapest, Hungary.
Phys Rev E. 2019 Jan;99(1-1):012113. doi: 10.1103/PhysRevE.99.012113.
I provide numerical evidence for the robustness of the Griffiths phase (GP) reported previously in dynamical threshold model simulations on a large human brain network with N=836733 connected nodes. The model, with equalized network sensitivity, is extended in two ways: introduction of refractory states or by randomized time-dependent thresholds. The nonuniversal power-law dynamics in an extended control parameter region survives these modifications for a short refractory state and weak disorder. In case of temporal disorder the GP shrinks and for stronger heterogeneity disappears, leaving behind a mean-field type of critical transition. Activity avalanche size distributions below the critical point decay faster than in the original model, but the addition of inhibitory interactions sets it back to the range of experimental values.
我提供了数值证据，证明了在一个包含 836733 个连接节点的大型人类大脑网络上的动力阈值模型模拟中，先前报道的格里菲斯相（GP）具有稳健性。该模型通过引入不应期状态或随机时变阈值在两个方面得到扩展。在较短不应期和较弱无序的情况下，扩展控制参数区域中的非普适幂律动力学仍然存在。在时间无序的情况下，GP 收缩，而在更强的异质性情况下消失，留下了一种平均场类型的临界转变。临界点以下的活动雪崩大小分布的衰减速度比原始模型更快，但添加抑制相互作用将其恢复到实验值范围内。
