  Electroencephalography (EEG) monitors ---by either intrusive or noninvasive
electrodes--- time and frequency variations and spectral content of voltage
fluctuations or waves, known as brain rhythms, which in some way uncover
activity during both rest periods and specific events in which the subject is
under stimulus. This is a useful tool to explore brain behavior, as it
complements imaging techniques that have a poorer temporal resolution. We here
approach the understanding of EEG data from first principles by studying a
networked model of excitatory and inhibitory neurons which generates a variety
of comparable waves. In fact, we thus reproduce $\alpha$, $\beta,$ $\gamma$ and
other rhythms as observed by EEG, and identify the details of the respectively
involved complex phenomena, including a precise relationship between an input
and the collective response to it. It ensues the potentiality of our model to
better understand actual mind mechanisms and its possible disorders, and we
also describe kind of stochastic resonance phenomena which locate main
qualitative changes of mental behavior in (e.g.) humans. We also discuss the
plausible use of these findings to design deep learning algorithms to detect
the occurence of phase transitions in the brain and to analyse its
consequences.
