Metabolic activity organizes olfactory representations

Hearing and vision sensory systems are tuned to the natural statistics of acoustic and electromagnetic energy on earth and are evolved to be sensitive in ethologically relevant ranges. But what are the natural statistics of odors, and how do olfactory systems exploit them? Dissecting an accurate machine learning model (Lee et al., 2022) 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 sequences (Caspi et al., 2014) 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.

Automated summary

By analyzing an accurate machine learning model for human odor perception, a computable representation for odor at the molecular level was discovered, which can predict the odor-evoked receptor, neural, and behavioral responses of almost all terrestrial organisms studied in olfactory neuroscience. This olfactory representation, known as 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. Despite large jumps in molecular structure, metabolic reaction sequences also follow smooth paths in POM. It suggests that the brain's representation of the olfactory world is shaped by the natural statistics of metabolism, similar to how the brain's visual representations have evolved around the natural statistics of light and shapes.