  Experiments suggest that cerebral cortex gains several functional advantages
by operating in a dynamical regime near the critical point of a phase
transition. However, a long-standing criticism of this hypothesis is that
critical dynamics are rather noisy, which might be detrimental to aspects of
brain function that require precision. If the cortex does operate near
criticality, how might it mitigate the noisy fluctuations? One possibility is
that other parts of the brain may act to control the fluctuations and reduce
cortical noise. To better understand this possibility, here we numerically and
analytically study a network of binary neurons. We determine how efficacy of
controlling the population firing rate depends on proximity to criticality as
well as different structural properties of the network. We found that control
is most effective - errors are minimal for the widest range of target firing
rates - near criticality. Optimal control is slightly away from criticality for
networks with heterogeneous degree distributions. Thus, while criticality is
the noisiest dynamical regime, it is also the regime that is easiest to
control, which may offer a way to mitigate the noise.
