Data Science In Olfaction
Vivek Agarwal Joshua Harvey Dmitry Rinberg Vasant Dhar
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.