Default mode network (DMN) is a functional brain network with a unique neural activity pattern that shows high activity in resting states but low activity in task states. This unique pattern has been proved to relate with higher cognitions such as learning, memory and decision-making. But neural mechanisms of interactions between the default network and the task-related network are still poorly understood. In this paper, a theoretical model of coupling the DMN and working memory network (WMN) is proposed. The WMN and DMN both consist of excitatory and inhibitory neurons connected by AMPA, NMDA, GABA synapses, and are coupled with each other only by excitatory synapses. This model is implemented to demonstrate dynamical processes in a working memory task containing encoding, maintenance and retrieval phases. Simulated results have shown that: (1) AMPA channels could produce significant synchronous oscillations in population neurons, which is beneficial to change oscillation patterns in the WMN and DMN. (2) Different NMDA conductance between the networks could generate multiple neural activity modes in the whole network, which may be an important mechanism to switch states of the networks between three different phases of working memory. (3) The number of sequentially memorized stimuli was related to the energy consumption determined by the network's internal parameters, and the DMN contributed to a more stable working memory process. (4) Finally, this model demonstrated that, in three phases of working memory, different memory phases corresponded to different functional connections between the DMN and WMN. Coupling strengths that measured these functional connections differed in terms of phase synchronization. Phase synchronization characteristics of the contained energy were consistent with the observations of negative and positive correlations between the WMN and DMN reported in referenced fMRI experiments. The results suggested that the coupled interaction between the WMN and DMN played important roles in working memory.

默认模式网络 (DMN) 是一种功能性大脑网络，具有独特的神经活动模式，在静息状态下表现出高活动，而在任务状态下表现出低活动。这种独特的模式已被证明与更高的认知有关，如学习、记忆和决策。但是默认网络和任务相关网络之间交互的神经机制仍然知之甚少。本文提出了一种耦合DMN和工作记忆网络（WMN）的理论模型。WMN和DMN均由通过AMPA、NMDA、GABA突触连接的兴奋性和抑制性神经元组成，并且仅通过兴奋性突触相互耦合。该模型用于演示包含编码、维护和检索阶段的工作记忆任务中的动态过程。模拟结果表明：(1) AMPA 通道可以在群体神经元中产生显着的同步振荡，这有利于改变 WMN 和 DMN 中的振荡模式。(2)网络之间不同的NMDA电导可以在整个网络中产生多种神经活动模式，这可能是网络在工作记忆三个不同阶段之间切换状态的重要机制。(3)顺序记忆刺激的数量与网络内部参数决定的能量消耗有关，DMN有助于更稳定的工作记忆过程。(4) 最后，该模型证明，在工作记忆的三个阶段中，不同的记忆阶段对应于DMN和WMN之间不同的功能连接。测量这些功能连接的耦合强度在相位同步方面有所不同。所含能量的相位同步特性与参考 fMRI 实验中报告的 WMN 和 DMN 之间的负相关和正相关的观察结果一致。结果表明，WMN和DMN之间的耦合交互在工作记忆中发挥了重要作用。