Literature DB >> 22030408

Modelling the signal delivered by a population of first-order neurons in a moth olfactory system.

Alexandre Grémiaux1, Thomas Nowotny, Dominique Martinez, Philippe Lucas, Jean-Pierre Rospars.   

Abstract

A statistical model of the population of first-order olfactory receptor neurons (ORNs) is proposed and analysed. It describes the relationship between stimulus intensity (odour concentration) and coding variables such as rate and latency of the population of several thousand sex-pheromone sensitive ORNs in male moths. Although these neurons likely express the same olfactory receptor, they exhibit, at any concentration, a relatively large heterogeneity of responses in both peak firing frequency and latency of the first action potential fired after stimulus onset. The stochastic model is defined by a multivariate distribution of six model parameters that describe the dependence of the peak firing rate and the latency on the stimulus dose. These six parameters and their mutual linear correlations were estimated from experiments in single ORNs and included in the multidimensional model distribution. The model is utilized to reconstruct the peak firing rate and latency of the message sent to the brain by the whole ORN population at different stimulus intensities and to establish their main qualitative and quantitative properties. Finally, these properties are shown to be in agreement with those found previously in a vertebrate ORN population. This article is part of a Special Issue entitled: Neural Coding.
Copyright © 2011 Elsevier B.V. All rights reserved.

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Year:  2011        PMID: 22030408     DOI: 10.1016/j.brainres.2011.09.035

Source DB:  PubMed          Journal:  Brain Res        ISSN: 0006-8993            Impact factor:   3.252


  5 in total

1.  Glomerular latency coding in artificial olfaction.

Authors:  Jaber Al Yamani; Farid Boussaid; Amine Bermak; Dominique Martinez
Journal:  Front Neuroeng       Date:  2012-01-03

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

Authors:  Jean-Pierre Rospars; Alexandre Grémiaux; David Jarriault; Antoine Chaffiol; Christelle Monsempes; Nina Deisig; Sylvia Anton; Philippe Lucas; Dominique Martinez
Journal:  PLoS Comput Biol       Date:  2014-12-04       Impact factor: 4.475

3.  Odorant mixtures elicit less variable and faster responses than pure odorants.

Authors:  Ho Ka Chan; Fabian Hersperger; Emiliano Marachlian; Brian H Smith; Fernando Locatelli; Paul Szyszka; Thomas Nowotny
Journal:  PLoS Comput Biol       Date:  2018-12-10       Impact factor: 4.475

4.  A spiking neural network model of self-organized pattern recognition in the early mammalian olfactory system.

Authors:  Bernhard A Kaplan; Anders Lansner
Journal:  Front Neural Circuits       Date:  2014-02-07       Impact factor: 3.492

5.  Adaptive integrate-and-fire model reproduces the dynamics of olfactory receptor neuron responses in a moth.

Authors:  Marie Levakova; Lubomir Kostal; Christelle Monsempès; Philippe Lucas; Ryota Kobayashi
Journal:  J R Soc Interface       Date:  2019-08-07       Impact factor: 4.118

  5 in total

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