The mechanisms that underlie a wine lover's ability to identify a favorite vintage and a dog's ability to track the scent of a lost child are still deep mysteries. Our understanding of these olfactory phenomena is confounded by the difficulty encountered when attempting to identify the parameters that define odor stimuli, by the broad tuning and variability of neurons in the olfactory pathway, and by the distributed nature of olfactory encoding. These issues pertain to both biological systems and to newly developed 'artificial noses' that seek to mimic these natural processes. Information theory, which quantifies explicitly the extent to which the state of one system (for example, the universe of all odors) relates to the state of another (for example, the responses of an odor-sensing device), can serve as a basis for analysing both natural and engineered odor sensors. This analytical approach can be used to explore the problems of defining stimulus dimensions, assessing strategies of neuronal processing, and examining the properties of biological systems that emerge from interactions among their complex components. It can also serve to optimize the design of artificial olfactory devices for a variety of applications, which include process control, medical diagnostics and the detection of explosives.
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