Reservoir computing and the liquid state machine model have received much attention in the literature in recent years. We investigate using a reservoir composed of a network of spiking neurons, with synaptic delays, whose synapses are allowed to evolve using a tri-phasic spike timing-dependent plasticity (STDP) rule. The results of using a tri-phasic STDP rule on the network properties are compared to those found using the more common exponential form of the rule. It is found that each rule causes the synaptic weights to evolve in significantly different fashions giving rise to different network dynamics.