Two types of criticality in the brain

David Dahmen, Sonja Grün, Markus Diesmann, Moritz Helias

Published 2017-11-29Version 1

Neural networks with tightly balanced excitatory and inhibitory coupling show high computational performance. They operate close to a critical point characterized by the joint activation of large populations of neurons. Yet, macaque motor cortex shows very different dynamics with weak fluctuations on macroscopic scales. In this light motor cortex seems to operate in a sub-optimal regime. Here we show the opposite: The large dispersion of correlations across neurons is a signature of a rich dynamical repertoire, hidden from macroscopic brain signals, but essential for high performance in such concepts as reservoir computing. Our findings suggest a refinement of the view on criticality in neural systems: network topology and heterogeneity endow the brain with two complementary substrates for critical dynamics of largely different complexity.