Antagonism in olfactory receptor neurons and its implications for the perception of odor mixtures

Natural environments feature mixtures of odorants of diverse quantities, qualities and complexities. Olfactory receptor neurons (ORNs) expressing different olfactory receptors are the first layer in the sensory pathway and transmit the olfactory signal to higher regions of the brain. Discriminatory computations are carried out by brain regions such as the olfactory cortex, which receive global information from the ORN ensemble. Yet, the response of ORNs to mixtures is strongly non-additive, and exhibits antagonistic interactions among odorants. Here, we model the processing of mixtures by mammalian ORNs, focusing on the role of inhibitory mechanisms. Theoretically predicted response curves capture experimentally determined responses imaged by a calcium indicator expressed in ORNs of live, breathing mice. We show how antagonism leads to an effective “normalization” of the ensemble response, which arises from a novel mechanism involving the distinct statistical properties of receptor binding and activation, without any recurrent neuronal circuitry. Normalization allows our encoding model to outperform non-interacting models in odor discrimination tasks, and to explain several psychophysical experiments in humans.