Advances in neural sensing technology are making it possible to observe the olfactory process in great detail. In this paper, we conceptualize smell from a Data Science and AI perspective, that relates the properties of odorants to how they are sensed and analyzed in the olfactory system from the nose to the brain. Drawing distinctions to color vision, we argue that smell presents unique measurement challenges, including the complexity of stimuli, the high dimensionality of the sensory apparatus, as well as what constitutes ground truth. In the face of these challenges, we argue for the centrality of odorant-receptor interactions in developing a theory of olfaction. Such a theory is likely to find widespread industrial applications, and enhance our understanding of smell, and in the longer-term, how it relates to other senses and language. As an initial use case of the data, we present results using machine learning-based classification of neural responses to odors as they are recorded in the mouse olfactory bulb with calcium imaging.

翻译：神经传感技术的进步使得我们在细节层面观察嗅觉过程成为可能。本文从数据科学与人工智能的视角对嗅觉进行概念化，探讨气味剂特性与其在从鼻腔到大脑的嗅觉系统中被感知和分析的方式之间的关联。通过与颜色视觉进行区分，我们论证了嗅觉面临独特的测量挑战，包括刺激的复杂性、感觉器官的高维度性以及真实参考标准的界定问题。面对这些挑战，我们强调气味剂-受体相互作用在发展嗅觉理论中的核心地位。这一理论有望在工业领域获得广泛应用，深化我们对嗅觉的理解，并在长期内阐明其与其他感官及语言的关系。作为数据应用的初始案例，我们展示了基于机器学习分类神经对气味反应的结果，这些反应通过钙成像技术在小鼠嗅球中记录获得。