  The relation between EEG rhythms, brain functions, and behavioral correlates
is well-established. Some mechanisms underlying rhythm generation are
understood, enabling the replication of brain rhythms $in\; silico$. This
allows to explore relations between neural oscillations and specific neuronal
circuits, helping to decipher the functional properties of brain waves.
Integrated information Decomposition ($\Phi$-ID) framework relates dynamical
regimes with informational properties, providing deeper insights into neuronal
dynamic functions. Here, we investigate wave emergence in an
excitatory/inhibitory (E/I) balanced network of IF neurons with short-term
synaptic plasticity producing a diverse range of EEG-like rhythms, from low
$\delta$ waves to high-frequency oscillations. Through $\Phi$-ID, we analyze
the network's information dynamics elucidating the system's suitability for
robust information transfer, storage, and parallel operation. Our study
identifies also regimes that may resemble pathological states due to poor
informational properties and high randomness. We found that $in\; silico$
$\beta$ and $\delta$ waves are associated with maximum information transfer in
inhibitory and excitatory neuron populations, and the coexistence of excitatory
$\theta$, $\alpha$, and $\beta$ waves associated to information storage. Also,
high-frequency oscillations can exhibit either high or poor informational
properties, shedding light on discussions regarding physiological versus
pathological high-frequency oscillations. Our study demonstrates that dynamical
regimes with similar oscillations may exhibit different information dynamics.
Finally, our findings suggest that the use of information dynamics in both
model and experimental data analysis, could help discriminate between
oscillations associated with cognitive functions and those linked to neuronal
disorders.
