  Criticality can be exactly demonstrated in certain models of brain activity,
yet it remains challenging to identify in empirical data. We trained a fully
connected deep neural network to learn the phases of an excitable model
unfolding on the anatomical connectome of human brain. This network was then
applied to brain-wide fMRI data acquired during the descent from wakefulness to
deep sleep. We report high correlation between the predicted proximity to the
critical point and the exponents of cluster size distributions, indicative of
subcritical dynamics. This result demonstrates that conceptual models can be
leveraged to identify the dynamical regime of real neural systems.
