  We introduce a practical and computationally not demanding technique for
inferring interactions at various microscopic levels between the units of a
network from the measurements and the processing of macroscopic signals.
Starting from a network model of Kuramoto phase oscillators which evolve
adaptively according to homophilic and homeostatic adaptive principles, we give
evidence that the increase of synchronization within groups of nodes (and the
corresponding formation of synchronous clusters) causes also the
defragmentation of the wavelet energy spectrum of the macroscopic signal. Our
methodology is then applied for getting a glance to the microscopic
interactions occurring in a neurophysiological system, namely, in the
thalamo-cortical neural network of an epileptic brain of a rat, where the group
electrical activity is registered by means of multichannel EEG. We demonstrate
that it is possible to infer the degree of interaction between the
interconnected regions of the brain during different types of brain activities,
and to estimate the regions' participation in the generation of the different
levels of consciousness.

Pattern Formation and Solitons