Spike Timing Dependant Plasticity (STDP) is a biologically-based Hebbian reinforcement learning rule for the unsupervised training of synaptic weights in spiking neural networks. We present a low complexity synthetic implementation of STDP using basic combinational digital logic gates. This approach attains comparable results to more complex implementations while utilizing only a fraction of the area. We use our STDP approach to replicate the experimental results of a balanced excitation experiment.