The effect of noise on the synchronization dynamics of the Kuramoto model on a large human connectome graph

We have extended the study of Kuramoto model with additive Gaussian noise running on KKI-18 large human connectome graph. determined dynamical behavior this by solving it numerically in an assumed homeostatic state, below synchronization crossover point we previously. The de-synchronization duration distributions exhibit power-law tails, characterized exponent range 1.1<τt<2, overlapping vivo brain activity experiments Palva et al. show that these scaling results remain valid, a transformation ultra-slow eigen-frequencies to unit variance. also compare those, obtained regular cube N=106 nodes, related embedding space, and quenched internal frequencies themselves can cause frustrated coupling space.