This paper studies the stabilization problem of delayed memristive neural networks under eventtriggered control. A refined switching event-trigger scheme that switches between variable sampling and continuous event-trigger can be designed by introducing an exponential decay term into the threshold function. Compared with the existing mechanisms, the proposed scheme can enlarge the interval between two successively triggered events and therefore can reduce the amount of triggering times. By constructing a time-dependent and piecewise-defined Lyapunov functional, a less-conservative criterion can be derived to ensure global stability of the closed-loop system. Based on matrix decomposition, equivalent conditions in linear matrix inequalities form of the above stability criterion can be established for the co-design of both the trigger matrix and the feedback gain. A numerical example is provided to demonstrate the effectiveness of the theoretical analysis and the advantages of the refined switching event-trigger scheme. Keywords event-triggered control, delayed memristive neural networks, global stabilization, timedependent Lyapunov functional, variable sampling Citation Yan Z L, Huang X, Cao J D. Variable-sampling-period dependent global stabilization of delayed memristive neural networks based on refined switching event-triggered control.