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Chemosensors 2018, 6(4), 44; https://doi.org/10.3390/chemosensors6040044

Stochastic and Temporal Models of Olfactory Perception

Dental Medicine, UConn Health, 263 Farmington Ave., Farmington, CT 06030, USA
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Received: 3 August 2018 / Revised: 5 September 2018 / Accepted: 18 September 2018 / Published: 26 September 2018
(This article belongs to the Special Issue Electronic nose’s, Machine Olfaction and Electronic Tongue’s)
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Abstract

Olfactory systems typically process signals produced by mixtures composed of very many natural odors, some that can be elicited by single compounds. The several hundred different olfactory receptors aided by several dozen different taste receptors are sufficient to define our complex chemosensory world. However, sensory processing by selective adaptation and mixture suppression leaves only a few perceptual components recognized at any time. Thresholds determined by stochastic processes are described by functions relating stimulus detection to concentration. Relative saliences of mixture components are established by relating component recognition to concentration in the presence of background components. Mathematically distinct stochastic models of perceptual component dominance in binary mixtures were developed that accommodate prediction of an appropriate range of probabilities from 0 to 1, and include errors in identifications. Prior short-term selective adaptation to some components allows temporally emergent recognition of non-adapted mixture-suppressed components. Thus, broadly tuned receptors are neutralized or suppressed by activation of other more efficacious receptors. This ‘combinatorial’ coding is more a process of subtraction than addition, with the more intense components dominating the perception. It is in this way that complex chemosensory mixtures are reduced to manageable numbers of odor notes and taste qualities. View Full-Text
Keywords: odor-coding; mixture-suppression; selective-adaptation; mixture model; threshold; intensity; sparse coding; dynamic coding; probability functions; combinatorial-subtraction odor-coding; mixture-suppression; selective-adaptation; mixture model; threshold; intensity; sparse coding; dynamic coding; probability functions; combinatorial-subtraction
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    Description: Supplementary Material contains an Excel file (Hettinger and Frank Models 2018-08-30.xlxs) that provides interactive spreadsheets and graphs to optimize fitting of mixture equations to experimental data.
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Hettinger, T.P.; Frank, M.E. Stochastic and Temporal Models of Olfactory Perception. Chemosensors 2018, 6, 44.

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