Spatiotemporal patterns of spike trains convey critical information in a biological neural network. Second-order memristive devices, whose internal state variables offer short- and long-term temporal dynamics, have been employed to natively decode the temporal correlation of spiking patterns through bio-realistic implementation of synaptic learning rules. In this work, we demonstrate that a single artificial postsynaptic neuron equipped with an array of second-order memristive synapses can localize a precise spatiotemporal firing pattern, which repeats irregularly within an equally dense background of Poisson spiking events, in an unsupervised fashion.