Scale invariance of the dynamical rules governing neural systems

Brain research utilizes various measurement techniques which probe diverse spatial scales of neural activity. A major challenge is to reconcile the small-scale, high-resolution measurements typical in animal research with human brain imaging, which has relatively poor spatial resolution. How do the governing principles of neural network dynamics manifest at different observational length scales? One possibility is that if the system operates in a dynamical regime near a critical point, as suggested by many experiments, then the rules governing the system dynamics may be scale-invariant. Here we confirm this possibility in a computational model and demonstrate a new approach to quantify scale-invariance in experimental data.