The hippocampus can exhibit different oscillatory rhythms within the sleep-wake cycle, each of them being involved in cognitive processes. For example, theta-nested gamma oscillations, consisting of the coupling of theta (4-12Hz) and gamma (40-100Hz) rhythms, are produced during wakefulness and are associated with spatial navigation and working memory tasks, whereas Sharp-Wave-Ripple (SWR) complexes, consisting of fast (140-200Hz) oscillatory events occurring during low frequency waves (<0.5Hz), are produced during slow-wave sleep and play an important role in memory consolidation. Models exist to reproduce and explain the generation of each of these rhythms individually, but to the best of our knowledge, there is at the moment no model capable of generating both rhythms and switch between them. The transitions between these rhythms suggest a change in the functional connectivity of the hippocampus. Some authors ([1], [2], see also [3]) propose that this phenomenon may be due to a neuromudulator, Acetylcholine (Ach), whose concentration is higher during wakefulness than sleep. But though we understand the influence of Ach on individual cells for different receptor types and locations ([4]), its quantitative effects on the whole hippocampal network remain unclear. In this context, we have built a computational model of the hippocampal formation that exhibits characteristic rhythms of wakefulness and slow-wave sleep, considering the varying concentration of Ach. In order to simulate the complete hippocampal formation, our model uses point neural models (single-compartment) but having realistic dynamics (conductance-based Hodgkin-Huxley neurons). Among these neurons, some have one of the membrane channel's conductance directly linked to the level of Ach (CAN, see [5]). The microscopic anatomy of the neurons was approximated by a dipole, while the macroscopic anatomy of the hippocampal structure was reproduced by positioning and connecting the neurons in an anatomically realistic manner. Based on the model proposed in [1], the network's functional connectivity was also changed between wakefulness and slow-wave sleep. Moreover, the stimulation entry of the network was derived from real sEEG measurements recorded during wake/sleep in the prefrontal cortex (projecting on the entorhinal cortex). In order to compare our results with in vivo signals from the human hippocampus, we also simulated the signals recorded by a realistic macroscopic sEEG electrode placed within the network. Our main finding is that such a model can indeed reproduce both theta-nested gamma oscillations and SWR complexes in humans by changing the level of Ach, with but little influence of the input stimulus. The network connectivity seems to determine the high frequency component of the rhythms, whereas individual neurons channel conductance seem to determine its low frequency component. 1. Hasselmo ME: ​ Neuromodulation: acetylcholine and memory consolidation.​ ​ Trends Cogn Sci ​ 1999, 3(9)​ :351-359. 2. Platt B, Riedel G: ​ The cholinergic system, EEG and sleep​ . ​ Behav Brain Res ​ 2011, ​ 221(2)​ :499-504. 3. Tiesinga PH, Fellous J-M, Jos JV, Sejnowski TJ:​ Computational model of carbachol-induced delta, theta, and gamma oscillations in the hippocampus.​ ​ Hippocampus ​ 2011, ​ 11(3​ ):251-274. 4. Drever BD, Riedel G, Platt B: ​ The cholinergic system and hippocampal plasticity.​ ​ Behav Brain Res ​ 2011, 221(2)​ :505-514. 5. Giovannini F, Knauer B, Yoshida M, Buhry L: ​ The can-in network: A biologically inspired model for self-sustained theta oscillations and memory maintenance in the hippocampus.​ ​ Hippocampus ​ 2017, 27(4)​ :450–463.