Document Detail


Characterizing complex chemosensors: information-theoretic analysis of olfactory systems.
MedLine Citation:
PMID:  10199633     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
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.
Authors:
T K Alkasab; T C Bozza; T A Cleland; K M Dorries; T C Pearce; J White; J S Kauer
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Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't; Research Support, U.S. Gov't, Non-P.H.S.; Research Support, U.S. Gov't, P.H.S.    
Journal Detail:
Title:  Trends in neurosciences     Volume:  22     ISSN:  0166-2236     ISO Abbreviation:  Trends Neurosci.     Publication Date:  1999 Mar 
Date Detail:
Created Date:  1999-07-06     Completed Date:  1999-07-06     Revised Date:  2006-11-15    
Medline Journal Info:
Nlm Unique ID:  7808616     Medline TA:  Trends Neurosci     Country:  ENGLAND    
Other Details:
Languages:  eng     Pagination:  102-8     Citation Subset:  IM    
Affiliation:
Dept of Neuroscience, Tufts University School of Medicine, Boston, MA 02111, USA.
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MeSH Terms
Descriptor/Qualifier:
Animals
Artificial Intelligence*
Biosensing Techniques
Humans
Information Theory
Models, Neurological
Odors
Olfactory Receptor Neurons / physiology*
Receptors, Odorant / chemistry,  physiology*
Signal Transduction / physiology*
Smell / physiology*
Chemical
Reg. No./Substance:
0/Receptors, Odorant

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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