The transition from idealized descriptions of memristors to physical implementations, such as resistive random access memory (RRAM) devices, is crucial for realizing many neuromorphic computing concepts in hardware. However, the intrinsic variability and fabrication tolerances of real-world RRAM devices present significant challenges for designing reliable circuits. This study focuses on addressing these challenges in the context of a memristive Hindmarsh-Rose circuit, a model for simulating the spiking and bursting behavior of biological neurons. To evaluate the robustness of the circuit in maintaining oscillatory behavior the Variability Intensity Model (VIM) is introduced, which enables a two-stage analysis with respect to the absolute tolerances and the dynamic variabilities. The results show that high conductance states are the most sensitive parameters, while low conductance states are more robust, optimally matching the behavior of real RRAM devices where relative variability decreases with increasing conductance. In addition, the tolerance analysis shows that spiking dynamics are generally robust to parameter variation, while bursting behavior is more sensitive. These results establish parameter constraints for actual RRAM devices as a basis for designing robust RRAMbased implementations of the Hindmarsh-Rose circuit.