Spike Timing Dependent Plasticity (STDP) is a biologically plausible learning rule routinely used for real-time learning in brain-inspired (neuromorphic) systems. In this work, we utilized an analog design of a Neural Engineering Framework (NEF)-tailored spiking neuron, termed OZ, for STDP-driven learning. We propose analog circuit designs of a STDP synapse and frequency adaptation and used them to demonstrate long-term potentiation and depression with adapted OZ neurons. Our design provides NEF-compiled energy-efficient STDP with analog circuitry.