Recordings in the locust antennal lobe (AL) reveal activity-dependent, stimulus-specific changes in projection neuron (PN) and local neuron response patterns over repeated odor trials. During the first few trials, PN response intensity decreases, while spike time precision increases, and coherent oscillations, absent at first, quickly emerge. We examined this "fast odor learning" with a realistic computational model of the AL. Activity-dependent facilitation of AL inhibitory synapses was sufficient to simulate physiological recordings of fast learning. In addition, in experiments with noisy inputs, a network including synaptic facilitation of both inhibition and excitation responded with reliable spatiotemporal patterns from trial to trial despite the noise. A network lacking fast plasticity, however, responded with patterns that varied across trials, reflecting the input variability. Thus, our study suggests that fast olfactory learning results from stimulus-specific, activity-dependent synaptic facilitation and may improve the signal-to-noise ratio for repeatedly encountered odor stimuli.