Recent neurophysiological research has focused on the temporal relationships between neuronal firing and plasticity, and has shown the phenomenon of spike-timing-dependent plasticity (STDP). Various models were suggested to implement the STDP-like learning rule in artificial networks based on spiking neuronal representations. Here we present and analyze a simple rule that only depends on the information that is available at the synapse at the time of synaptic modification. This rule is further extended by addition of four different types of gating derived from conventionally used types of gated decay in learning rules for continuous firing rate neural networks. The results show that the advantages of using these gatings are transferred to the new rule without sacrificing its dependency on spike-timing.