Towards a Memristor‐Based Circuit Implementation of the Hindmarsh–Rose Model

The transition from idealized memristor models to physical implementations, such as resistive random access memory (RRAM) devices, is pivotal for advancing neuromorphic circuits in hardware. However, real‐world RRAM devices face challenges due to inherent variability and fabrication tolerances, which hinder their adoption. This study presents the variability intensity model (VIM), a statistical framework designed to assess variability in RRAM devices. The VIM is employed to evaluate the resilience of a memristor‐based Hindmarsh–Rose circuit, which models neuronal behavior in biological systems. The results indicate that the circuit exhibits heightened sensitivity to variability in high‐conductance states, while demonstrating increased tolerance in low‐conductance states. This observation is consistent with RRAM characteristics, where conductance variability decreases as conductance increases. Furthermore, robust spiking dynamics are observed, though the bursting behavior remains sensitive, leading to critical parameter constraints for RRAM devices. By employing a variability model based on real‐world measurements, this work establishes a crucial foundation for the implementation of memristor‐based Hindmarsh–Rose circuits in hardware, where variability is a paramount consideration.