Stochastic and Temporal Models of Olfactory Perception

• Main Authors: Thomas P. Hettinger, Marion E. Frank
• Format: Article
• Language: English
• Published: MDPI AG 2018-09-01
• Series: Chemosensors
• Subjects: odor-coding; mixture-suppression; selective-adaptation; mixture model; threshold; intensity; sparse coding; dynamic coding; probability functions; combinatorial-subtraction
• Online Access: http://www.mdpi.com/2227-9040/6/4/44

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.