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An olfactory cocktail party: figure-ground segregation of odorants in rodents

Nature Neuroscience volume 17, pages 1225–1232 (2014)Cite this article

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Abstract

In odorant-rich environments, animals must be able to detect specific odorants of interest against variable backgrounds. However, studies have found that both humans and rodents are poor at analyzing the components of odorant mixtures, suggesting that olfaction is a synthetic sense in which mixtures are perceived holistically. We found that mice could be easily trained to detect target odorants embedded in unpredictable and variable mixtures. To relate the behavioral performance to neural representation, we imaged the responses of olfactory bulb glomeruli to individual odors in mice expressing the Ca2+ indicator GCaMP3 in olfactory receptor neurons. The difficulty of segregating the target from the background depended strongly on the extent of overlap between the glomerular responses to target and background odors. Our study indicates that the olfactory system has powerful analytic abilities that are constrained by the limits of combinatorial neural representation of odorants at the level of the olfactory receptors.

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Figure 1: The behavioral task.
Figure 2: The odorants used in the behavioral task.
Figure 3: Decreased performance on mixtures with more odorant components is not explained by a limited sampling time.
Figure 4: Performance depends on background components that are similar to the target.
Figure 5: Tiglates evoke correlated glomerular response patterns.
Figure 6: Performance on the task depends on masking at the level of olfactory bulb inputs.

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Acknowledgements

We thank N. Uchida, R. Wilson and members of our laboratory for comments on the manuscript. Work in V.N.M.'s laboratory was supported by grants from the US National Institutes of Health (RO1DC11291). D.R. was supported by a fellowship from the Edmond and Lily Safra Center for Brain Sciences, Hebrew University.

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Authors and Affiliations

  1. Center for Brain Science, Harvard University, Cambridge, Massachusetts, USA

    Dan Rokni, Vivian Hemmelder, Vikrant Kapoor & Venkatesh N Murthy

  2. Department of Molecular & Cellular Biology, Harvard University, Cambridge, Massachusetts, USA

    Dan Rokni, Vivian Hemmelder, Vikrant Kapoor & Venkatesh N Murthy

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  1. Dan Rokni
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Contributions

D.R. and V.N.M. conceived and designed the experiments. D.R. and V.H. collected the behavioral data. V.K. collected the imaging data. D.R. analyzed the behavioral data. D.R. and V.K. analyzed the imaging data. D.R. and V.N.M. wrote the manuscript.

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Correspondence to Venkatesh N Murthy.

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Integrated supplementary information

Supplementary Figure 1 Olfactometer for behavioral experiments.

A custom built olfactometer was used to deliver mixtures of odorants to the mouse. The olfactometer was designed to allow each of the 16 odorants to be present or absent in any mixture while keeping the concentration of each odorant independent of other odorants. a. To achieve this goal, the olfactometer was built with 16 modules, each controlling one odorant and contributing a constant and equal amount to the output flow. Input flow into the modules and output flow from the modules were made using FEP-lined Tygon/PVC tubing connected in symmetric pair-wise bifurcations. Each module had a 3-way valve (Lee Company, USA) that diverted the input flow of clean air to go through either of two glass tubes, one containing the odor and solvent and one containing only the solvent. Both pathways then converged to form the module output flow. From the point where all odorants converged to the odor port, the odorous air flowed through a 4 foot long tubing of 1/16 inch diameter. This minimized the latency from valve opening to odor presentation and ensured mixing of the odorants to at least within the scale of the tubing. Odorant mixtures were generated by controlling the 16 module valves allowing 216 possible mixtures. b. Photoionization detector (miniPID, Aurora Scientific) measurements were used to analyze the output of the olfactometer. The amplitude of the PID signal in response to an odorant mixture was equal to the sum of the amplitudes of PID signals in response to the individual components, indicating that the different odorant modules are independent.

Supplementary Figure 2 Individual mouse performance: tiglate targets.

Performance of individual mice trained to detect tiglate targets. Plots show the percentage of correct trials as a function of the number of components in the mixture for all trials (black), Go trials (blue) and NoGo trials (red). Lines are linear fits to the data. Targets were Ethyl tiglate and Allyl tiglate (a and h), Benzyl tiglate and Phenylethyl tiglate (b, c, f and g), Hexyl tiglate and Methyl tiglate (d), and Isopropyl tiglate and citronellyl tiglate (e).

Supplementary Figure 3 Individual mouse performance: non-tiglate targets.

Performance of individual mice trained to detect non-tiglate targets. Plots show the percentage of correct trials as a function of the number of components in the mixture for all trials (black), Go trials (blue) and NoGo trials (red). Lines are linear fits to the data. Targets were Ethyl propionate and 2-Ethyl hexanal (a), Propyl acetate and 4-Allyl anisole (b), Isobutyl propionate and Allyl butyrate (c), and Ethyl valerate and Citronellal (d and e).

Supplementary Figure 4 Individual mouse performance: population averages.

a-c. Performance as a function of the number of components in the mixture for all mice (a, n=13), tiglate trained mice (b, n=8) and non-tiglate trained mice (c, n=5). Here data are only pooled within each mouse and then averaged across mice. Shown are mean±SE for all trials (black), Go trials (blue), and NoGo trials (red). Lines are linear fits to the data. d. The effect of tiglates and non-tiglates as background odorants on the performance of all individual mice detecting tiglates (left) and mice detecting non-tiglates (right). Group effects were calculated as the average change in % correct rejections when an odorant of the group is added to the background (see Figure 3). The lines are connecting data of individual mice. Colored dots are the mean effect of each group.

Supplementary Figure 5 Estimation of mixture responses as maximal intensity projection of individual components.

a and d. Percent of NoGo trials that were correctly rejected as a function of mixture masking (a) and target-mixture correlation (d) (top panels). Each data point represents 500 trials. Red lines are fits of sigmoidal decay to the data (see methods). Below are shown the distributions of masking and correlation values for all mixtures presented in NoGo trials. b and e. Average number of components in the mixture as a function of mixture masking (b) and target-mixture correlation (e). c and f. Percent of NoGo trials with fixed number of components in the mixture that were correctly rejected as a function of mixture masking (c) and target-mixture correlation (f). Each curve shows the data from a fixed number of components in the mixture (indicated by color). Symbols show the average percent of correct rejections.

Supplementary Figure 6 Robustness of masking analysis.

Masking was calculated as in Figure 6b, but the threshold for glomerular responses was varied from 1 to 15 standard deviations away from the baseline. Masking index was a good predictor of performance throughout this range, indicating that the results are insensitive to thresholding. Each data point represents the mean values of 500 trials. Red lines are fits of sigmoidal decay to the data (see Online Methods).

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Rokni, D., Hemmelder, V., Kapoor, V. et al. An olfactory cocktail party: figure-ground segregation of odorants in rodents. Nat Neurosci 17, 1225–1232 (2014). https://doi.org/10.1038/nn.3775

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