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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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Firing rates are Hill functions of dose with different parameter values in each neuron.(A) Measured firing rate F (dots) of 3 ORNs fitted to Hill functions (eq. 4; solid curves) showing parameters FM and C1/2 and characteristic C0 and Cs for F0  =  5 AP/s. (B) All (N  =  38) Hill curves fitted to ORNs. (C) Hill curves of 3 PNs. (D) All (N  =  37) PN curves successfully fitted to Hill functions. (E) Distribution of maximum firing rates FM in the ORN (blue, N = 38) and PN (red, N = 37) populations. Each empirical CDF (staircase) with its fitted normal CDF (dotted curve) and corresponding PDF (dashed curve). (F) Distributions of dynamic ranges ΔC (related to n), same N and representation as in (E) except fitted distribution is lognormal for ORNs.
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pcbi-1003975-g006: Firing rates are Hill functions of dose with different parameter values in each neuron.(A) Measured firing rate F (dots) of 3 ORNs fitted to Hill functions (eq. 4; solid curves) showing parameters FM and C1/2 and characteristic C0 and Cs for F0  =  5 AP/s. (B) All (N  =  38) Hill curves fitted to ORNs. (C) Hill curves of 3 PNs. (D) All (N  =  37) PN curves successfully fitted to Hill functions. (E) Distribution of maximum firing rates FM in the ORN (blue, N = 38) and PN (red, N = 37) populations. Each empirical CDF (staircase) with its fitted normal CDF (dotted curve) and corresponding PDF (dashed curve). (F) Distributions of dynamic ranges ΔC (related to n), same N and representation as in (E) except fitted distribution is lognormal for ORNs.

Mentions: Dose-response plots were established for 38 ORNs and 47 PNs. Sigmoid Hill functions (see eqs. 3 and 4 in Methods) were fitted to the dose-firing rate C-F plots (Fig. 6). From the fitted parameters – maximum firing rate FM, efficient dose 50% (ED50) C1/2, and Hill coefficient n – we also derived three characteristics: the doses at threshold C0 and at saturation CS and their difference, the dynamic range ΔC (eqs. 5–7;). The firing rate responses to the lowest doses are not significantly different from controls (Fig. 4A, C) and those to the two highest doses are nearly equal (within 15% in 87% of ORNs and 80% of PNs, showing that the observed maximum firing rates were close to the asymptotic FM) which guarantees that the parameters were correctly estimated. Latencies were analyzed the same way. Decreasing linear functions with a lower bound were fitted to the dose-latency C-L plots (Fig. 7). Each neuron was characterized by its maximum latency LM at threshold C0, its minimum latency Lm and their difference ΔL  =  LM − Lm.


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)

Firing rates are Hill functions of dose with different parameter values in each neuron.(A) Measured firing rate F (dots) of 3 ORNs fitted to Hill functions (eq. 4; solid curves) showing parameters FM and C1/2 and characteristic C0 and Cs for F0  =  5 AP/s. (B) All (N  =  38) Hill curves fitted to ORNs. (C) Hill curves of 3 PNs. (D) All (N  =  37) PN curves successfully fitted to Hill functions. (E) Distribution of maximum firing rates FM in the ORN (blue, N = 38) and PN (red, N = 37) populations. Each empirical CDF (staircase) with its fitted normal CDF (dotted curve) and corresponding PDF (dashed curve). (F) Distributions of dynamic ranges ΔC (related to n), same N and representation as in (E) except fitted distribution is lognormal for ORNs.
© Copyright Policy
Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC4256018&req=5

pcbi-1003975-g006: Firing rates are Hill functions of dose with different parameter values in each neuron.(A) Measured firing rate F (dots) of 3 ORNs fitted to Hill functions (eq. 4; solid curves) showing parameters FM and C1/2 and characteristic C0 and Cs for F0  =  5 AP/s. (B) All (N  =  38) Hill curves fitted to ORNs. (C) Hill curves of 3 PNs. (D) All (N  =  37) PN curves successfully fitted to Hill functions. (E) Distribution of maximum firing rates FM in the ORN (blue, N = 38) and PN (red, N = 37) populations. Each empirical CDF (staircase) with its fitted normal CDF (dotted curve) and corresponding PDF (dashed curve). (F) Distributions of dynamic ranges ΔC (related to n), same N and representation as in (E) except fitted distribution is lognormal for ORNs.
Mentions: Dose-response plots were established for 38 ORNs and 47 PNs. Sigmoid Hill functions (see eqs. 3 and 4 in Methods) were fitted to the dose-firing rate C-F plots (Fig. 6). From the fitted parameters – maximum firing rate FM, efficient dose 50% (ED50) C1/2, and Hill coefficient n – we also derived three characteristics: the doses at threshold C0 and at saturation CS and their difference, the dynamic range ΔC (eqs. 5–7;). The firing rate responses to the lowest doses are not significantly different from controls (Fig. 4A, C) and those to the two highest doses are nearly equal (within 15% in 87% of ORNs and 80% of PNs, showing that the observed maximum firing rates were close to the asymptotic FM) which guarantees that the parameters were correctly estimated. Latencies were analyzed the same way. Decreasing linear functions with a lower bound were fitted to the dose-latency C-L plots (Fig. 7). Each neuron was characterized by its maximum latency LM at threshold C0, its minimum latency Lm and their difference ΔL  =  LM − Lm.

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