In this paper, we develop a spatio-temporal memory that blends properties from long and short-term memory and is motivated by reaction diffusion mechanisms. The winning processing element of a self-organizing network creates traveling waves on the output space that gradually attenuate over time and space to diffuse temporal information and create localized spatio-temporal neighborhoods for clustering. The novelty of the model is in the creation of time varying Voronoi tessellations anticipating the learned input signal dynamics even when the cluster centers are fixed. We test the method in a robot navigation task and in vector quantization of speech. This method performs better than conventional static vector quantizers based on the same data set and similar training conditions.