A more accurate synaptic modeling based on oxygen vacancy conductive mechanism is presented in this paper. Two internal state variables, that is, the length and area of conductive region, are used to describe the vertical and lateral growth/dissolution dynamics of the region, based on the physical mechanisms of ion drift and two different diffusion effects. Since the effect of length on the electric field is not negligible, it is introduced into the modeling. In addition, the Fick and Soret diffusions are considered, because they cause the model to produce a “forgetting” property and a “memory” retention. By the comparisons, this modeling captures the actual device better than others. In addition, the previous models can be derived from this one. Some rough analysis suggests that the modeling possibly captures different memristors by adjusting the parameters, and the effects of oxygen concentration and temperature on synapse can also be considered. Therefore, this modeling is more comprehensive and generalized. Several important synaptic functions are simulated, including excitatory postsynaptic current, paired‐pulse facilitation, spike‐rate‐dependent plasticity, and spike‐timing‐dependent plasticity, which may provide a theoretical basis for some potential applications such as artificial neural networks and artificial intelligence.

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