MemristiveAdamW: An Optimization Algorithm for Spiking Neural Networks Incorporating Memristive Effects

Spiking Neural Networks (SNNs), with their event-driven and energy-efficient characteristics, have shown great promise in processing data from neuromorphic sensors. However, the sparse and non-stationary nature of event-based data poses significant challenges to optimization, particularly when using conventional algorithms such as AdamW, which assume smooth gradient dynamics. To address this limitation, we propose MemristiveAdamW, a novel algorithm that integrates memristor-inspired dynamic adjustment mechanisms into the AdamW framework. This optimization algorithm introduces three biologically motivated modules: (1) a direction-aware modulation mechanism that adapts the update direction based on gradient change trends; (2) a memristive perturbation model that encodes history-sensitive adjustment inspired by the physical characteristics of memristors; and (3) a memory decay strategy that ensures stable convergence by attenuating perturbations over time. Extensive experiments are conducted on two representative event-based datasets, Prophesee NCARS and GEN1, across three SNN architectures: Spiking VGG-11, Spiking MobileNet-64, and Spiking DenseNet-121. Results demonstrate that MemristiveAdamW consistently improves convergence speed, classification accuracy, and training stability compared to standard AdamW, with the most significant gains observed in shallow or lightweight SNNs. These findings suggest that memristor-inspired optimization offers a biologically plausible and computationally effective paradigm for training SNNs on event-driven data.