The analysis for the transition dynamics of sleep states through the driving effects of the cholinergic inputs to hippocampal oscillations

Cholinergic inputs from the medial septum (MS) affect hippocampal memory during sleep. However, computational modeling of sleep transitions is currently lacking. Here, we use eight coupled rate equations to build a septo-hippocampal cholinergic network. The numerical results indicate that enhanced cholinergic inputs in the model can effectively suppress hippocampal ripple oscillations and shift to theta states. This exchange of dominant rhythms reflects sleep state transitions and corresponds to the results of physiological experiments. By analyzing the dynamical mechanisms underlying this transition, we found that this change originates from a bifurcation phenomenon within the hippocampal network. Additionally, we found that the adaptive gain parameter can effectively modulate the up-state oscillatory activity of the hippocampal network and exhibits greater sensitivity during rapid eye movement (REM) sleep. These results will bring possible insights into computational characterizations and transformations of different sleep states, and provide a theoretical basis for neuromodulation in memory formation.