Data Science in Olfaction

Vivek K Agarwal, Joshua S. Harvey, Dmitry Rinberg, Vasant Dhar
2024 · doi:10.1101/2024.10.19.619239
preprint OA: closed CC-BY-ND-4.0
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Abstract

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. Our larger objective is to create the equivalent of an “MNIST database for olfaction,” which we call ‘oMNIST,’ so that researchers are able to work from a standard dataset to further the state of the art, similar to how the availability of standard datasets catalyzed research in computer vision.
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Abstract 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. Our larger objective is to create the equivalent of an “MNIST database for olfaction,” which we call ‘oMNIST,’ so that researchers are able to work from a standard dataset to further the state of the art, similar to how the availability of standard datasets catalyzed research in computer vision. Competing Interest Statement The authors have declared no competing interest.

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License: CC-BY-ND-4.0