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) are the first layer in the sensory pathway and transmit the olfactory signal to higher regions of the brain. Yet, the response of ORNs to mixtures is strongly non-ad...

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Bibliographic Details
Published in:eLife 2018-04, Vol.7
Main Authors: Reddy, Gautam, Zak, Joseph D, Vergassola, Massimo, Murthy, Venkatesh N
Format: Article
Language:English
Subjects:
Action Potentials
Aroma compounds
Computational and Systems Biology
decoding
encoding
Humans
Ligands
mixture interaction
Models, Neurological
Neuroscience
normalization
odor perception
Odorant receptors
Odorants
Odors
Olfactory discrimination
Olfactory discrimination learning
Olfactory Perception
Olfactory receptor neurons
Olfactory Receptor Neurons - metabolism
Psychophysics
Ratios
Sensory evaluation
Signal transduction
Smell - physiology
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Summary:Natural environments feature mixtures of odorants of diverse quantities, qualities and complexities. Olfactory receptor neurons (ORNs) are the first layer in the sensory pathway and transmit the olfactory signal to higher regions of the brain. 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. We show how antagonism leads to an effective 'normalization' of the ensemble ORN response, that is, the distribution of responses of the ORN population induced by any mixture is largely independent of the number of components in the mixture. This property 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, leads to experimentally testable predictions and explains several psychophysical experiments in humans.
ISSN:2050-084X
2050-084X
DOI:10.7554/elife.34958