Researchers have discovered that metabolic activity plays a key role in organizing olfactory representations. By using a machine learning model for human odor perception, they found a computable representation for odor at the molecular level that can predict the odor-evoked responses of nearly all terrestrial organisms studied in olfactory neuroscience. This representation, called the Principal Odor Map (POM), shows that odorous compounds with similar POM representations are more likely to co-occur within a substance and be metabolically closely related. The natural statistics of metabolism appear to shape the brain's representation of the olfactory world.
Dissecting an accurate machine learning model1 for human odor perception, we find a computable representation for odor at the molecular level that can predict the odor-evoked receptor, neural, and behavioral responses of nearly all terrestrial organisms studied in olfactory neuroscience
Using this olfactory representation (Principal Odor Map, POM), we find that odorous compounds with similar POM representations are more likely to co-occur within a substance and be metabolically closely related; metabolic reaction sequences2 also follow smooth paths in POM despite large jumps in molecular structure.
Just as the brain’s visual representations have evolved around the natural statistics of light and shapes, the natural statistics of metabolism appear to shape the brain’s representation of the olfactory world.
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