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« Previous AbstractCells in the vomeronasal organ express odorant receptors but project to the accessory olfactory bulb    Next AbstractAdaptive integrate-and-fire model reproduces the dynamics of olfactory receptor neuron responses in a moth »

PLoS Comput Biol


Title:Moth olfactory receptor neurons adjust their encoding efficiency to temporal statistics of pheromone fluctuations
Author(s):Levakova M; Kostal L; Monsempes C; Jacob V; Lucas P;
Address:"Institute of Physiology of the Czech Academy of Sciences, Prague, Czech Republic. Institute of Ecology and Environmental Sciences, INRA, Versailles, France. Peuplements vegetaux et bioagresseurs en milieu vegetal, CIRAD, Universite de la Reunion, Saint Pierre, Ile de la Reunion, France"
Journal Title:PLoS Comput Biol
Year:2018
Volume:20181113
Issue:11
Page Number:e1006586 -
DOI: 10.1371/journal.pcbi.1006586
ISSN/ISBN:1553-7358 (Electronic) 1553-734X (Print) 1553-734X (Linking)
Abstract:"The efficient coding hypothesis predicts that sensory neurons adjust their coding resources to optimally represent the stimulus statistics of their environment. To test this prediction in the moth olfactory system, we have developed a stimulation protocol that mimics the natural temporal structure within a turbulent pheromone plume. We report that responses of antennal olfactory receptor neurons to pheromone encounters follow the temporal fluctuations in such a way that the most frequent stimulus timescales are encoded with maximum accuracy. We also observe that the average coding precision of the neurons adjusted to the stimulus-timescale statistics at a given distance from the pheromone source is higher than if the same encoding model is applied at a shorter, non-matching, distance. Finally, the coding accuracy profile and the stimulus-timescale distribution are related in the manner predicted by the information theory for the many-to-one convergence scenario of the moth peripheral sensory system"
Keywords:"Animals Arthropod Antennae/*physiology Electrophysiological Phenomena Male Models, Statistical Moths/*physiology Neurons, Afferent/physiology Olfactory Pathways/*physiology Olfactory Receptor Neurons/*physiology Pheromones/*physiology Probability Reproduc;"
Notes:"MedlineLevakova, Marie Kostal, Lubomir Monsempes, Christelle Jacob, Vincent Lucas, Philippe eng Research Support, Non-U.S. Gov't 2018/11/14 PLoS Comput Biol. 2018 Nov 13; 14(11):e1006586. doi: 10.1371/journal.pcbi.1006586. eCollection 2018 Nov"

 
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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.
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