For the first time, researchers have demonstrated that the efficient coding principle regarding neurobiological processes applies to sense of smell. The team, comprised of researchers from the Czech Academy of Sciences and the French National Institute for Agricultural Research (INRA), displays this quantitative relationship in a study of male moths and pheromone plumes, published April 25th in the open-access journal PLoS Computational Biology.
The efficient coding principle - the adaptation of sensory neurons to the statistical characteristics of their natural stimulus - has previously been studied in visual and auditory neurobiology. In this new study, the researchers have extended this principle to sense of smell, studying how males locate their female mates via pheromone release. The team affirms that olfactory neurons in moths best process those stimuli that occur most frequently.
The authors selected the pheromone olfactory system because it is the only one in aerial animals for which quantitative properties of both the natural stimulus and the reception processes are known. These properties were used to determine the characteristics of the pheromone plume that are best detected by the male neuron reception system. The researchers then matched those characteristics with those from plume measurements in the field, providing quantitative evidence that this system obeys the efficient coding principle.
The researchers note that this study was confined to early detection events, most notably the interaction of pheromone molecules with membrane receptors. Exploring the quantitative relationship between the properties of biological sensory systems and their natural environment should lead not only to a better understanding of neural functions and evolutionary processes, but also to improvements in the design of artificial sensory systems.
PLEASE ADD THIS LINK TO THE PUBLISHED ARTICLE IN ONLINE VERSIONS OF YOUR REPORT: http://www.
CITATION: Kostal L, Lansky P, Rospars J-P (2008) Efficient Olfactory Coding in the Pheromone Receptor Neuron of a Moth. PLoS Comput Biol 4(4): e1000053. doi:10.1371/journal.pcbi.1000053
Dr. Jean-Pierre Rospars
(33) 1 30 83 33 50
PLEASE MENTION THE OPEN ACCESS JOURNAL PLoS COMPUTATIONAL BIOLOGY (www.ploscompbiol.org) AS THE SOURCE FOR THIS ARTICLE AND PROVIDE A LINK TO THE FREELY AVAILABLE TEXT. THANK YOU.
This press release refers to an upcoming article in PLoS Computational Biology. The release is provided by the article authors. Any opinions expressed in this release or article are the personal views of the journal staff and/or article contributors, and do not necessarily represent the views or policies of PLoS. PLoS expressly disclaims any and all warranties and liability in connection with the information found in the releases and articles and your use of such information.
About PLoS Computational Biology
PLoS Computational Biology (www.ploscompbiol.org) features works of exceptional significance that further our understanding of living systems at all scales through the application of computational methods. All works published in PLoS Computational Biology are open access. Everything is immediately available subject only to the condition that the original authorship and source are properly attributed. Copyright is retained by the authors. The Public Library of Science uses the Creative Commons Attribution License.
About the Public Library of Science
The Public Library of Science (PLoS) is a non-profit organization of scientists and physicians committed to making the world's scientific and medical literature a freely available public resource. For more information, visit http://www.