title: A comparison between the human sense of smell and neural activity in the olfactory bulb of rats.
abstract: Generally, odor qualities are evaluated via sensory tests in which predefined criteria are assessed by panelists and stochastically analyzed to reduce human inconsistencies. Because this method requires multiple, well-trained human subjects, a more convenient approach is required to enable predictions of odor qualities. In this article, we propose an approach involving linking internal states of the olfactory system with perceptual characteristics. In the study, the glomerular responses of rats were taken to represent internal olfactory system states. Similarities between the glomerular responses of rats were quantified by correlations between glomerular activity patterns, overlap rate of strongly activated part across glomerular activity patterns, and the similarity between histograms of the strength of activity. These indices were then compared with perceptual similarities measured from human subjects in sensory tests. The results of experiments involving 22 odorants showed medium strength correlations between each index and perceptual similarity. In addition, when the 3 indices were combined using their Euclidean distance, we observed middle to high correlations (r = 0.65-0.79) to human perceptual similarity. We also report the results of our use of a machine learning technique to classify the odorants into a similar and dissimilar category. Although the correct rate of classification varied from 33.3% to 92.9%, these results support the feasibility of linking the glomerular responses of rats to human perception.
authors.affiliation: Department of System Cybernetics, Institute of Engineering, Hiroshima University, 1-4-1 Kagamiyama, Higashi-Hiroshima, Hiroshima 739-8527, Japan. sozu@bsys.hiroshima-u.ac.jp.
authors.forename: Zu
authors.initials: Z
authors.lastname: Soh
authors.forename: Maki
authors.initials: M
authors.lastname: Saito
authors.forename: Yuichi
authors.initials: Y
authors.lastname: Kurita
authors.forename: Noboru
authors.initials: N
authors.lastname: Takiguchi
authors.forename: Hisao
authors.initials: H
authors.lastname: Ohtake
authors.forename: Toshio
authors.initials: T
authors.lastname: Tsuji
mesh_terms: D000818:Animals; D001185:Artificial Intelligence; D003491:Cybernetics; D003847:Deoxyglucose / Q000378:metabolism; D006801:Humans; D008959:Models, Neurological; D009812:Odorants; D009830:Olfactory Bulb / Q000502:physiology*; D055696:Olfactory Perception / Q000502:physiology*; D051381:Rats; D012903:Smell / Q000502:physiology; D013045:Species Specificity
chemical_list: D003847:Deoxyglucose
keywords: glomerular response; human odor perception; machine learning; rats; sensory test