Spike Timing Dependent Plasticity (STDP) is the physiological process thought to underlie learning in biological neural networks. It alters individual connections over time, pruning ineffective ones and strengthening effective ones. STDP has been observed in vivo during nervous system development, but requires further analysis to understand how it affects neural network behavior. This work provides initial results on the effects of STDP on a simulated network capable of complex behavior, focusing on changes to network bursting. We show that a network exhibiting bursting and traveling waves, in the presence of STDP, evolves to support more frequent bursting behavior with asymmetric wave propagation. We also show how the feedback between bursting and connection weights leads to stable alterations in network architecture.