Learning scheme is the key to the utilization of spike-based computation and the emulation of neural/
synaptic behaviors toward realization of cognition. The biological observations reveal an integrated spike
time- and spike rate-dependent plasticity as a function of presynaptic firing frequency. However, this
integrated rate-temporal learning scheme has not been realized on any nano devices. In this paper, such
scheme is successfully demonstrated on a memristor. Great robustness against the spiking rate fluctuation is
achieved by waveform engineering with the aid of good analog properties exhibited by the iron oxide-based
memristor. The spike-time-dependence plasticity (STDP) occurs at moderate presynaptic firing frequencies
and spike-rate-dependence plasticity (SRDP) dominates other regions. This demonstration provides a novel
approach in neural coding implementation, which facilitates the development of bio-inspired computing
systems.