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

Rospars JP, Grémiaux A, Jarriault D, Chaffiol A, Monsempes C, Deisig N, Anton S, Lucas P, Martinez D - PLoS Comput. Biol. (2014)

Bottom Line: We found that over all their dynamic range, PNs respond with a shorter latency and a higher firing rate than most ORNs.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.

View Article: PubMed Central - PubMed

Affiliation: Institut National de la Recherche Agronomique (INRA), Unité Mixte de Recherche 1392 Institut d'Ecologie et des Sciences de l'Environnement de Paris, Versailles, France.

ABSTRACT
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.

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Distributions of firing rates (top row) and latencies (bottom row) at single pheromone doses are dose-dependent.(A) Comparison in ORNs of raw firing rates Fraw (not corrected from control stimulations) for control stimulations (green) and for pheromone doses −1, 0, 1, 2, 3, 4 log ng (blue, from left to right). Fraw at C = −1 log ng not significantly different from control (Kolmogorov-Smirnov test, p = 0.43). (B) Comparison in ORNs of latencies L for same stimuli and doses (from right to left) as in (A). (C) Comparison in PNs of firing rates Fraw for control stimulations (green) and for pheromone doses −3, −2, −1, 0, 1 log ng (red), same representation as in (A). Fraw at C = −3 log ng not significantly different from control (Kolmogorov-Smirnov test, p = 0.43) but significantly different from Fraw at C = −2 (p<10−4). (D) Comparison in PNs of latencies L for same stimuli and doses as in (C). (E, G) Comparison of firing rates F (corrected from control stimulation) in ORNs (blue) and PNs (red) at the same doses −1, 0 (in E) and 1 log ng (in G). For C≤1, the mean firing firing rates of ORNs is smaller than that of PNs. (F–H) Comparison of latencies, same representation as in (E, G). At all doses, the mean firing latency of ORNs is larger than that of PNs. At C≥1, the shortest ORN latencies become almost as short as the shortest PN latencies.
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pcbi-1003975-g004: Distributions of firing rates (top row) and latencies (bottom row) at single pheromone doses are dose-dependent.(A) Comparison in ORNs of raw firing rates Fraw (not corrected from control stimulations) for control stimulations (green) and for pheromone doses −1, 0, 1, 2, 3, 4 log ng (blue, from left to right). Fraw at C = −1 log ng not significantly different from control (Kolmogorov-Smirnov test, p = 0.43). (B) Comparison in ORNs of latencies L for same stimuli and doses (from right to left) as in (A). (C) Comparison in PNs of firing rates Fraw for control stimulations (green) and for pheromone doses −3, −2, −1, 0, 1 log ng (red), same representation as in (A). Fraw at C = −3 log ng not significantly different from control (Kolmogorov-Smirnov test, p = 0.43) but significantly different from Fraw at C = −2 (p<10−4). (D) Comparison in PNs of latencies L for same stimuli and doses as in (C). (E, G) Comparison of firing rates F (corrected from control stimulation) in ORNs (blue) and PNs (red) at the same doses −1, 0 (in E) and 1 log ng (in G). For C≤1, the mean firing firing rates of ORNs is smaller than that of PNs. (F–H) Comparison of latencies, same representation as in (E, G). At all doses, the mean firing latency of ORNs is larger than that of PNs. At C≥1, the shortest ORN latencies become almost as short as the shortest PN latencies.

Mentions: In order to document this feature and to provide an overview of how the two neuron populations studied respond to a given dose of Z7-12:Ac the firing rates and latencies of all recorded neurons at each applied dose were pooled. It was found in this way that the firing rate F presents four distinct properties (Fig. 4, top row; Fig. 5, left column): (i) The firing rates across neurons stimulated at the same dose follow Gaussian distributions in ORNs (Figs. 4A, 5A) and PNs (Figs. 4C, 5C). (ii) The mean of the distributions increases with the dose (Fig. 5A, C). At the lowest dose applied (−1 log ng for ORNs, −3 for PNs) the distributions are not significantly different from the control stimulations with pure air or hexane (Fig. 4A, C). The frequencies at the two highest doses tested (3 and 4 log ng for ORNs, 0 and 1 log ng for PNs) are also not significantly different (Fig. 4A, C). Therefore, when measured at the population level, the dynamic ranges of ORNs extend from −1 to 3 log ng and for PNs from −3 to 0 log ng. (iii) For doses C≤1, PNs respond more strongly than ORNs (Fig. 3E, G). (iv) The standard deviation of the distributions increases linearly with their mean for F<∼100 AP/s, with the same slope in ORNs and PNs (i.e. same coefficient of variation CV≈0.33), notwithstanding the very different doses evoking the same variability in the two populations (Fig. 5E). Above ∼100 AP/s, before saturation of the mean ORN firing rate, the variability of ORNs and PNs becomes constant (Fig. 5E).


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

Rospars JP, Grémiaux A, Jarriault D, Chaffiol A, Monsempes C, Deisig N, Anton S, Lucas P, Martinez D - PLoS Comput. Biol. (2014)

Distributions of firing rates (top row) and latencies (bottom row) at single pheromone doses are dose-dependent.(A) Comparison in ORNs of raw firing rates Fraw (not corrected from control stimulations) for control stimulations (green) and for pheromone doses −1, 0, 1, 2, 3, 4 log ng (blue, from left to right). Fraw at C = −1 log ng not significantly different from control (Kolmogorov-Smirnov test, p = 0.43). (B) Comparison in ORNs of latencies L for same stimuli and doses (from right to left) as in (A). (C) Comparison in PNs of firing rates Fraw for control stimulations (green) and for pheromone doses −3, −2, −1, 0, 1 log ng (red), same representation as in (A). Fraw at C = −3 log ng not significantly different from control (Kolmogorov-Smirnov test, p = 0.43) but significantly different from Fraw at C = −2 (p<10−4). (D) Comparison in PNs of latencies L for same stimuli and doses as in (C). (E, G) Comparison of firing rates F (corrected from control stimulation) in ORNs (blue) and PNs (red) at the same doses −1, 0 (in E) and 1 log ng (in G). For C≤1, the mean firing firing rates of ORNs is smaller than that of PNs. (F–H) Comparison of latencies, same representation as in (E, G). At all doses, the mean firing latency of ORNs is larger than that of PNs. At C≥1, the shortest ORN latencies become almost as short as the shortest PN latencies.
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Related In: Results  -  Collection

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Show All Figures
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pcbi-1003975-g004: Distributions of firing rates (top row) and latencies (bottom row) at single pheromone doses are dose-dependent.(A) Comparison in ORNs of raw firing rates Fraw (not corrected from control stimulations) for control stimulations (green) and for pheromone doses −1, 0, 1, 2, 3, 4 log ng (blue, from left to right). Fraw at C = −1 log ng not significantly different from control (Kolmogorov-Smirnov test, p = 0.43). (B) Comparison in ORNs of latencies L for same stimuli and doses (from right to left) as in (A). (C) Comparison in PNs of firing rates Fraw for control stimulations (green) and for pheromone doses −3, −2, −1, 0, 1 log ng (red), same representation as in (A). Fraw at C = −3 log ng not significantly different from control (Kolmogorov-Smirnov test, p = 0.43) but significantly different from Fraw at C = −2 (p<10−4). (D) Comparison in PNs of latencies L for same stimuli and doses as in (C). (E, G) Comparison of firing rates F (corrected from control stimulation) in ORNs (blue) and PNs (red) at the same doses −1, 0 (in E) and 1 log ng (in G). For C≤1, the mean firing firing rates of ORNs is smaller than that of PNs. (F–H) Comparison of latencies, same representation as in (E, G). At all doses, the mean firing latency of ORNs is larger than that of PNs. At C≥1, the shortest ORN latencies become almost as short as the shortest PN latencies.
Mentions: In order to document this feature and to provide an overview of how the two neuron populations studied respond to a given dose of Z7-12:Ac the firing rates and latencies of all recorded neurons at each applied dose were pooled. It was found in this way that the firing rate F presents four distinct properties (Fig. 4, top row; Fig. 5, left column): (i) The firing rates across neurons stimulated at the same dose follow Gaussian distributions in ORNs (Figs. 4A, 5A) and PNs (Figs. 4C, 5C). (ii) The mean of the distributions increases with the dose (Fig. 5A, C). At the lowest dose applied (−1 log ng for ORNs, −3 for PNs) the distributions are not significantly different from the control stimulations with pure air or hexane (Fig. 4A, C). The frequencies at the two highest doses tested (3 and 4 log ng for ORNs, 0 and 1 log ng for PNs) are also not significantly different (Fig. 4A, C). Therefore, when measured at the population level, the dynamic ranges of ORNs extend from −1 to 3 log ng and for PNs from −3 to 0 log ng. (iii) For doses C≤1, PNs respond more strongly than ORNs (Fig. 3E, G). (iv) The standard deviation of the distributions increases linearly with their mean for F<∼100 AP/s, with the same slope in ORNs and PNs (i.e. same coefficient of variation CV≈0.33), notwithstanding the very different doses evoking the same variability in the two populations (Fig. 5E). Above ∼100 AP/s, before saturation of the mean ORN firing rate, the variability of ORNs and PNs becomes constant (Fig. 5E).

Bottom Line: We found that over all their dynamic range, PNs respond with a shorter latency and a higher firing rate than most ORNs.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.

View Article: PubMed Central - PubMed

Affiliation: Institut National de la Recherche Agronomique (INRA), Unité Mixte de Recherche 1392 Institut d'Ecologie et des Sciences de l'Environnement de Paris, Versailles, France.

ABSTRACT
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.

Show MeSH
Related in: MedlinePlus