A Compact Behavioral Model Quantifying the Relationship between Optoelectronic Memristor Dynamics and Reservoir...

Optoelectronic memristors are promising platforms for neuromorphic and reservoir computing because of their intrinsic nonlinearity and short-term memory dynamics. However, the relationship between experimentally measurable device dynamics and computational capability remains unclear. Here, we present a compact behavioral modeling framework that establishes a direct link between experimentally measured excitatory postsynaptic current (EPSC) dynamics and reservoir computing performance. A parametric exponential model is developed to describe the temporal evolution of memristor current under single- and multipulse stimulation, enabling accurate reconstruction of history-dependent responses using a small set of physically interpretable parameters. By integrating the model into the reservoir layer, a reproducible memristor-driven reservoir computing system is constructed for diverse temporal recognition tasks. Furthermore, bit-depth scaling analysis reveals that higher-resolution dynamic state encoding improves convergence and recognition performance for complex tasks, while excessive encoding resolution yields diminishing returns. This work provides a physically interpretable framework for co-optimizing memristor dynamics, temporal encoding resolution, and reservoir-level computational capability.