Heterogeneity and convergence of olfactory first-order neurons account for the high speed and sensitivity of second-order neurons

Autor: Alexandre Grémiaux, Philippe Lucas, Christelle Monsempes, Jean-Pierre Rospars, David Jarriault, Dominique Martinez, Nina Deisig, Sylvia Anton, Antoine Chaffiol
Přispěvatelé: Institut d'écologie et des sciences de l'environnement de Paris (iEES), Centre National de la Recherche Scientifique (CNRS)-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Recherche Agronomique (INRA), Institut de Recherche en Horticulture et Semences (IRHS), Université d'Angers (UA)-Institut National de la Recherche Agronomique (INRA)-AGROCAMPUS OUEST, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Centre des Sciences du Goût et de l'Alimentation [Dijon] (CSGA), Institut National de la Recherche Agronomique (INRA)-Université de Bourgogne (UB)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Centre National de la Recherche Scientifique (CNRS), Institut de la Vision, Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Pierre et Marie Curie - Paris 6 (UPMC), Récepteurs et Canaux Ioniques Membranaires (RCIM), Université d'Angers (UA)-Institut National de la Recherche Agronomique (INRA), ANR BBSRC SysBio 006 01, ANR-10-BINF-05, Institut National de la Recherche Agronomique (INRA)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12)-Centre National de la Recherche Scientifique (CNRS), Centre des Sciences du Goût et de l'Alimentation (CSGA), Institut National de la Recherche Agronomique (INRA)-Université de Bourgogne (UB)-Centre National de la Recherche Scientifique (CNRS), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Institut d'écologie et des sciences de l'environnement de Paris (IEES), Laboratoire Récepteurs et Canaux Ioniques Membranaires (RCIM), Institut d'écologie et des sciences de l'environnement de Paris ( IEES ), Institut National de la Recherche Agronomique ( INRA ) -Université Pierre et Marie Curie - Paris 6 ( UPMC ) -Université Paris-Est Créteil Val-de-Marne - Paris 12 ( UPEC UP12 ) -Centre National de la Recherche Scientifique ( CNRS ), Institut de Recherche en Horticulture et Semences ( IRHS ), Université d'Angers ( UA ) -Institut National de la Recherche Agronomique ( INRA ) -AGROCAMPUS OUEST, Centre des Sciences du Goût et de l'Alimentation [Dijon] ( CSGA ), Institut National de la Recherche Agronomique ( INRA ) -Université de Bourgogne ( UB ) -AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Centre National de la Recherche Scientifique ( CNRS ), Université Pierre et Marie Curie - Paris 6 ( UPMC ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ) -Centre National de la Recherche Scientifique ( CNRS ), Laboratoire Récepteurs et Canaux Ioniques Membranaires ( RCIM ), Université d'Angers ( UA ) -Institut National de la Recherche Agronomique ( INRA )
Jazyk: angličtina
Rok vydání: 2014
Předmět:
Olfactory system
Male
neurons
Moths
[ SDV.BA ] Life Sciences [q-bio]/Animal biology
Bioinformatics
curve fitting
lcsh:QH301-705.5
education.field_of_study
Ecology
[SDV.BA]Life Sciences [q-bio]/Animal biology
Olfactory Pathways
distribution curves
Sensory Systems
medicine.anatomical_structure
Computational Theory and Mathematics
Modeling and Simulation
Pheromone
[SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]
Neural coding
olfactory receptor neurons
pheromones
Research Article
moths and butterflies
Population
Models
Neurological
Sensory system
Biology
Cellular and Molecular Neuroscience
Genetics
medicine
coding mechanisms
Animals
Detection theory
Latency (engineering)
education
Molecular Biology
Ecology
Evolution
Behavior and Systematics
Computational Neuroscience
Olfactory System
Olfactory receptor
Biology and Life Sciences
Computational Biology
lcsh:Biology (General)
nervous system
[ SDV.NEU ] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]
transfer functions
sense organs
Neuroscience
Zdroj: PLoS Computational Biology
PLoS Computational Biology, Public Library of Science, 2014, 10 (12), pp.1-16. ⟨10.1371/journal.pcbi.1003975.s010⟩
PLoS Computational Biology, 2014, 10 (12), pp.1-16. ⟨10.1371/journal.pcbi.1003975.s010⟩
PLoS Computational Biology, Vol 10, Iss 12, p e1003975 (2014)
PLoS Computational Biology, Public Library of Science, 2014, 10 (12), pp.1-16. 〈10.1371/journal.pcbi.1003975.s010〉
ISSN: 1553-734X
1553-7358
Popis: In the olfactory system of male moths, a specialized subset of neurons detects and processes the main component of the sex pheromone emitted by females. It is composed of several thousand first-order olfactory receptor neurons (ORNs), all expressing the same pheromone receptor, that contact synaptically a few tens of second-order projection neurons (PNs) within a single restricted brain area. The functional simplicity of this system makes it a favorable model for studying the factors that contribute to its exquisite sensitivity and speed. Sensory information—primarily the identity and intensity of the stimulus—is encoded as the firing rate of the action potentials, and possibly as the latency of the neuron response. We found that over all their dynamic range, PNs respond with a shorter latency and a higher firing rate than most ORNs. Modelling showed that the increased sensitivity of PNs can be explained by the ORN-to-PN convergent architecture alone, whereas their faster response also requires cell-to-cell heterogeneity of the ORN population. So, far from being detrimental to signal detection, the ORN heterogeneity is exploited by PNs, and results in two different schemes of population coding based either on the response of a few extreme neurons (latency) or on the average response of many (firing rate). Moreover, ORN-to-PN transformations are linear for latency and nonlinear for firing rate, suggesting that latency could be involved in concentration-invariant coding of the pheromone blend and that sensitivity at low concentrations is achieved at the expense of precise encoding at high concentrations.
Author Summary Understanding how sensory signals are optimally encoded by nervous systems is of strong interest to neuroscientists, and also to engineers as it may lead to more efficient artificial detection systems. This is particularly relevant to olfaction, because the current electronic noses are far outperformed by their biological counterparts in terms of speed and sensitivity. We here use the moth sex pheromone processing system as a relatively simple model to understand early olfactory coding. We found that performance increases when olfactory information passes from first- to second-order neurons. Second-order neurons respond on average with shorter latency and higher sensitivity than first-order neurons. We show that two critical factors, convergent architecture and neuronal heterogeneity, are needed to account for increased performance.
Databáze: OpenAIRE
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