  Neural networks with equal excitatory and inhibitory feedback show high
computational performance. They operate close to a critical point characterized
by the joint activation of large populations of neurons. Yet, in macaque motor
cortex we observe very different dynamics with weak fluctuations on the
population level. This suggests that motor cortex operates 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
complexities.
