  Recent enhancements in neuroscience, like the development of new and powerful
recording techniques of the brain activity combined with the increasing
anatomical knowledge provided by atlases and the growing understanding of
neuromodulation principles, allow studying the brain at a whole new level,
paving the way to the creation of extremely detailed effective network models
directly from observed data. Leveraging the advantages of this integrated
approach, we propose a method to infer models capable of reproducing the
complex spatio-temporal dynamics of the slow waves observed in the experimental
recordings of the cortical hemisphere of a mouse under anesthesia. To reliably
claim the good match between data and simulations, we implemented a versatile
ensemble of analysis tools, applicable to both experimental and simulated data
and capable to identify and quantify the spatio-temporal propagation of waves
across the cortex. In order to reproduce the observed slow wave dynamics, we
introduced an inference procedure composed of two steps: the inner and the
outer loop. In the inner loop, the parameters of a mean-field model are
optimized by likelihood maximization, exploiting the anatomical knowledge to
define connectivity priors. The outer loop explores "external" parameters,
seeking for an optimal match between the simulation outcome and the data,
relying on observables (speed, directions, and frequency of the waves) apt for
the characterization of cortical slow waves; the outer loop includes a periodic
neuro-modulation for better reproduction of the experimental recordings. We
show that our model is capable to reproduce most of the features of the
non-stationary and non-linear dynamics displayed by the biological network.
Also, the proposed method allows to infer which are the relevant modifications
of parameters when the brain state changes, e.g. according to anesthesia
levels.
