To adjust behavior during goal-directed behaviors, the dynamic management of updating and preserving information in working memory (WM) is essential, which is probably accomplished by a WM “gating mechanism”. However, the neural implementation of this mechanism remains an outstanding subject. To capture neural network dynamics underlying mechanisms supporting a dynamic regulation of WM gate opening and closing, the current study used computational behavioral drift-diffusion modeling (DDM) in conjunction with assessments of effective connectivities (nCREANN) in theta, alpha, and beta frequency bands. We show that gate opening and closing are opposing threshold-dependent mechanisms that depend on distinct neural networks and frequency bands associated with maintenance, updating, and cognitive control. Interestingly, especially the beta frequency band was found to be of great significance for WM gate closing and maintenance. When considered collectively, the current data demonstrate that the decision criterion of whether to open or close the gate is shaped by the underlying network architecture.