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.

神经感知技术的进展使得我们能够以极大的细节观察嗅觉过程。本文从数据科学和人工智能的角度概念化嗅觉，将气味物质的特性与其在嗅觉系统中自鼻子到大脑的感应和分析联系起来。在与视觉感知的区别中，我们论述了嗅觉所面临的独特测量挑战，包括刺激的复杂性、感官装置的高维度特性，以及 “真实实地” 的界定。面对这些挑战，我们认为气味物质与受体间的相互作用对于嗅觉理论的发展具有核心地位。这种理论可能会在工业应用中得到广泛应用，并增加我们对嗅觉的理解，在更长远的未来，也会增进我们对其他感知和语言的理解。作为数据的初始用例，我们提出了使用基于机器学习的分类方法对记录在小鼠嗅觉球中的气味神经反应进行分类的结果。