  Thanks to novel, powerful brain activity recording techniques, we can create
data-driven models from thousands of recording channels and large portions of
the cortex, which can improve our understanding of brain-states neuromodulation
and the related richness of traveling waves dynamics.
  We investigate the inference of data-driven models and the comparison among
experiments and simulations, through the characterization of the
spatio-temporal features of cortical waves in experimental recordings and
simulations. Inference is built in two steps: the inner loop that optimizes by
likelihood maximization a mean-field model, and the outer loop that optimizes a
periodic neuro-modulation by relying on direct comparison of observables apt
for the characterization of cortical slow waves. The model is capable to
reproduce most of the features of the non-stationary and non-linear dynamics
displayed by the high-resolution recording of the in-vivo mouse brain obtained
by wide-field calcium imaging techniques. The proposed approach is of interest
for both experimental and computational neuroscientists.
