Before revision: The mammalian olfactory system learns new odors rapidly, exhibits negligible interference among odor memories, and identifies known odors under challenging conditions. The mechanisms by which it does so are unknown. We here present a general theory for odor learning and identification under noise in the olfactory system, and demonstrate its efficacy using a neuromorphic model of the olfactory bulb .
After revision: We present a neural algorithm for the rapid online learning and identification under noise in the olfactory system, and demonstrate its efficacy using a neuromorphic model of the olfactory bulb .