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Sparse coding is thought to improve discrimination of sensory stimuli by reducing overlap between their representations. Two factors, however, can offset sparse coding's advantages. Similar sensory stimuli have significant overlap, and responses vary across trials. To elucidate the effect of these two factors, we analyzed odor responses in the fly and mouse olfactory regions implicated in learning and discrimination --- the Mushroom Body (MB) and the Piriform Cortex (PCx). In both species, we show that neuronal responses fall along a continuum from extremely reliable across trials to extremely variable or stochastic. Computationally, we show that the range of observed variability arises from probabilistic synapses in inhibitory feedback connections within central circuits rather than sensory noise, as is traditionally assumed. We propose this coding scheme to be advantageous for coarse- and fine-odor discrimination. More reliable cells enable quick discrimination between dissimilar odors. For similar odors, however, these cells overlap, and do not provide distinguishing information. By contrast, more unreliable cells are decorrelated for similar odors, providing distinguishing information, though this requires extended training with more trials. Overall, we have uncovered a stochastic coding scheme that is conserved in vertebrates and invertebrates, and we identify a candidate mechanism, based on variability in a winner-take-all inhibitory circuit, that improves discrimination with training.