Oscillations are ubiquitous in the brain and robustly correlate with distinct cognitive tasks. A specific type of oscillatory signals allows robust switching between states in networks involved in memorizing tasks. In particular, slow oscillations lead to an activation of the neuronal populations whereas oscillations in the beta range are effective in clearing the memory states. In this master thesis, previous works are revisited in order to provide a detailed analysis of the mechanisms underlying the states switching and their dependence on the network parameters. The model studied is a macroscopic description of the network recently derived due to mean-field theory advances. The role of spiking synchrony in the switching off of the active states is identified by means of bifurcation analysis and the study of the fixed points under the stroboscopic map. Finally, we propose an application of the effect of oscillations in a context of working memory
