Spike-timing-dependent plasticity (STDP) is emerging as a simple and biologically-plausible approach to learning, and specialized digital implementations are readily available. Memristor technology has been embraced as a much denser solution than digital static random-access memory (SRAM) implementations of STDP synapses, with plasticity capabilities built into the physics of these devices. One-selector-one-memristor (1S1R) arrays using volatile memristor devices as selectors are capable of the desired synaptic behavior using efficient spike-events, but previous literature has only explored the dynamics of single 1S1R synapses, or groups of synapses for single neurons. When placed in the context of an SNN, unintentional synapse disturbances are revealed that must be addressed. We present1 a technique for STDP-based learning, enabled for dense 1S1R technology and utilizing efficient single-spike encoding. This technique leverages the array's dynamics to produce models that are stable, resilient to noise, and power-efficient.