Osmo, Cambridge, United States.
Google Research, Brain Team, Cambridge, United States.
Elife. 2023 May 2;12:e82502. doi: 10.7554/eLife.82502.
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 , 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.
听觉和视觉感觉系统是为了适应地球上声能和电磁能的自然统计数据而调整的，并且进化得对生态相关的范围敏感。但是，嗅觉系统如何利用这些自然统计数据呢？通过对人类嗅觉感知的精确机器学习模型（Lee 等人，2022 年）进行剖析，我们发现了一种可计算的分子水平上的气味表示，它可以预测近所有在嗅觉神经科学中研究的陆地生物的气味诱发的受体、神经和行为反应。使用这个嗅觉表示（主嗅觉图 [POM]），我们发现，具有相似 POM 表示的有气味的化合物在一种物质中更有可能共同出现，并且在代谢上密切相关；代谢反应序列（Caspi 等人，2014 年）在 POM 中也遵循平滑的路径，尽管在分子结构上有很大的跳跃。就像大脑的视觉表示是围绕着光和形状的自然统计数据进化而来的一样，代谢的自然统计数据似乎也塑造了大脑对嗅觉世界的表示。
