Abstract
To predict the odor quality of an odorant mixture, the interaction between odorants must be taken into account. Previously, an experiment in which mice discriminated between odorant mixtures identified a selective adaptation mechanism in the olfactory system. This paper proposes an olfactory model for odorant mixtures that can account for selective adaptation in terms of neural activity. The proposed model uses the spatial activity pattern of the mitral layer obtained from model simulations to predict the perceptual similarity between odors. Measured glomerular activity patterns are used as input to the model. The neural interaction between mitral cells and granular cells is then simulated, and a dissimilarity index between odors is defined using the activity patterns of the mitral layer. An odor set composed of three odorants is used to test the ability of the model. Simulations are performed based on the odor discrimination experiment on mice. As a result, we observe that part of the neural activity in the glomerular layer is enhanced in the mitral layer, whereas another part is suppressed. We find that the dissimilarity index strongly correlates with the odor discrimination rate of mice: r = 0.88 (p = 0.019). We conclude that our model has the ability to predict the perceptual similarity of odorant mixtures. In addition, the model also accounts for selective adaptation via the odor discrimination rate, and the enhancement and inhibition in the mitral layer may be related to this selective adaptation.
Highlights
Predicting the quality of an odor composed of multiple odorant components is a challenging problem
In addition to the techniques described above, this paper focuses on the brain expression of odors evoked on the olfactory bulb, including the odor maps on the glomerular layer [8,9,10,11,12,13], to predict the perceptual similarity of odorant mixtures
We investigated the changes in the activity patterns of the mitral layer and synapse connection parameters in H along with the Hebbian learning
Summary
Predicting the quality of an odor composed of multiple odorant components is a challenging problem. Even if the smell of each odorant component was known, the resultant quality of an odorant mixture may differ from the linear addition of the respective odorant qualities [1]. An interesting approach to predicting odorant quality is that proposed by Haddad et al, in which an odorant quality space was derived from about 1400 kinds of odorant descriptor using principal analysis [2]. Their group applied the developed method to predict the pleasantness of an odorant [3]. PLOS ONE | DOI:10.1371/journal.pone.0165230 December 19, 2016
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