Bedoukian   RussellIPM   RussellIPM   Piezoelectric Micro-Sprayer


Home
Animal Taxa
Plant Taxa
Semiochemicals
Floral Compounds
Semiochemical Detail
Semiochemicals & Taxa
Synthesis
Control
Invasive spp.
References

Abstract

Guide

Alphascents
Pherobio
InsectScience
E-Econex
Counterpart-Semiochemicals
Print
Email to a Friend
Kindly Donate for The Pherobase

« Previous AbstractPsychological effects of musky compounds: comparison of androstadienone with androstenol and muscone    Next AbstractPlant volatiles guide the new pest Dichelops furcatus to feed on corn seedlings »

PLoS Comput Biol


Title:Olfactory coding in the turbulent realm
Author(s):Jacob V; Monsempes C; Rospars JP; Masson JB; Lucas P;
Address:"Institute of Ecology and Environmental Sciences, INRA, route de St Cyr, Versailles, France. Peuplements vegetaux et bioagresseurs en milieu vegetal, CIRAD, Universite de la Reunion, Saint Pierre, Ile de la Reunion, France. Decision and Bayesian Computation, Pasteur Institute, CNRS UMR 3571, 25-28 rue du Dr Roux, 75015 Paris, France. Bioinformatics and Biostatistics Hub, C3BI, Pasteur Institute, CNRS USR 3756, 25-28 rue du Dr Roux, 75015 Paris, France"
Journal Title:PLoS Comput Biol
Year:2017
Volume:20171201
Issue:12
Page Number:e1005870 -
DOI: 10.1371/journal.pcbi.1005870
ISSN/ISBN:1553-7358 (Electronic) 1553-734X (Print) 1553-734X (Linking)
Abstract:"Long-distance olfactory search behaviors depend on odor detection dynamics. Due to turbulence, olfactory signals travel as bursts of variable concentration and spacing and are characterized by long-tail distributions of odor/no-odor events, challenging the computing capacities of olfactory systems. How animals encode complex olfactory scenes to track the plume far from the source remains unclear. Here we focus on the coding of the plume temporal dynamics in moths. We compare responses of olfactory receptor neurons (ORNs) and antennal lobe projection neurons (PNs) to sequences of pheromone stimuli either with white-noise patterns or with realistic turbulent temporal structures simulating a large range of distances (8 to 64 m) from the odor source. For the first time, we analyze what information is extracted by the olfactory system at large distances from the source. Neuronal responses are analyzed using linear-nonlinear models fitted with white-noise stimuli and used for predicting responses to turbulent stimuli. We found that neuronal firing rate is less correlated with the dynamic odor time course when distance to the source increases because of improper coding during long odor and no-odor events that characterize large distances. Rapid adaptation during long puffs does not preclude however the detection of puff transitions in PNs. Individual PNs but not individual ORNs encode the onset and offset of odor puffs for any temporal structure of stimuli. A higher spontaneous firing rate coupled to an inhibition phase at the end of PN responses contributes to this coding property. This allows PNs to decode the temporal structure of the odor plume at any distance to the source, an essential piece of information moths can use in their tracking behavior"
Keywords:Animals Appetitive Behavior/*physiology Arthropod Antennae/cytology/*physiology Computational Biology/methods Male Moths/physiology Olfactory Pathways/*physiology Olfactory Receptor Neurons/metabolism/*physiology Pheromones/*metabolism;
Notes:"MedlineJacob, Vincent Monsempes, Christelle Rospars, Jean-Pierre Masson, Jean-Baptiste Lucas, Philippe eng 2017/12/02 PLoS Comput Biol. 2017 Dec 1; 13(12):e1005870. doi: 10.1371/journal.pcbi.1005870. eCollection 2017 Dec"

 
Back to top
 
Citation: El-Sayed AM 2024. The Pherobase: Database of Pheromones and Semiochemicals. <http://www.pherobase.com>.
© 2003-2024 The Pherobase - Extensive Database of Pheromones and Semiochemicals. Ashraf M. El-Sayed.
Page created on 19-12-2024