RT Journal Article SR Electronic T1 Metabolic activity organizes olfactory representations JF bioRxiv FD Cold Spring Harbor Laboratory SP 2022.07.21.500995 DO 10.1101/2022.07.21.500995 A1 Qian, Wesley W. A1 Wei, Jennifer N. A1 Sanchez-Lengeling, Benjamin A1 Lee, Brian K. A1 Luo, Yunan A1 Vlot, Marnix A1 Dechering, Koen A1 Peng, Jian A1 Gerkin, Richard C. A1 Wiltschko, Alexander B. YR 2022 UL http://biorxiv.org/content/early/2022/08/20/2022.07.21.500995.abstract AB 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 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.Competing Interest StatementThe authors have declared no competing interest.