This letter presents a novel dynamic Neuro-space mapping (Neuro-SM) technique for nonlinear device modeling. This is an advance over the existing static Neuro-SM which aims to map a given approximate device model towards an accurate model. The proposed technique retains the ability of static Neuro-SM in modifying the effects of nonlinear resistors and current sources. The proposed technique can also make up for any capacitive effects and non-quasi-static effects that maybe missing in the given model, which is not achievable by the existing static Neuro-SM. In this way, the dynamic Neuro-SM model can exceed the accuracy limit of the static Neuro-SM. The validity and efficiency of the proposed approach are verified through two high-electron mobility transistor (HEMT) modeling examples.