Dual cholinergic control of attractor dynamics and adaptation enabling memory state transitions

Traditional associative memory models, exemplified by the Hopfield network, have established a foundational understanding of how equilibrium states encode and store static patterns. However, the neural mechanisms governing transitions between these multiple equilibria, a hallmark of flexible cognition, remain poorly understood. Here, we address this gap by developing a biologically grounded dynamical framework that integrates cholinergic neuromodulation into an extended Hopfield network. Specifically, we model the dual regulatory effects of acetylcholine (ACh): the modulation of the local-global inhibition balance via an inhibition ratio parameter, c, and the regulation of spike frequency adaptation (SFA) through activity-dependent thresholds. Our theoretical analysis and numerical simulations reveal that SFA-driven destabilization enables controlled transitions from stable attractor states to metastable mixture states, thereby allowing the network to overcome local energy barriers. Critically, we identify an optimal synergistic control strategy that combines both ACh-mediated mechanisms, achieving an 87% success rate in facilitating attractor switching. This combined approach outperforms individual mechanisms operating in isolation (85% for SFA alone and 78% for inhibition modulation alone). Mean-field analysis demonstrates that this synergy operates by transiently destabilizing current attractors, enabling trajectories to escape shallow energy basins and converge to deeper, more energetically favorable states. Furthermore, these findings provide a computational framework grounded in energy landscape optimization that explains how neural circuits balance memory stability with cognitive flexibility, offering mechanistic insights into associative recall, free association, and state-dependent information processing.