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Proc Natl Acad Sci U S A. Mar 22, 2011; 108(12): 5075–5080.
Published online Mar 7, 2011. doi:  10.1073/pnas.1100369108
PMCID: PMC3064350
Neuroscience

Temporal coding of odor mixtures in an olfactory receptor neuron

Abstract

Most natural odors are mixtures and often elicit percepts distinct from those elicited by their constituents. This emergence of a unique odor quality has long been attributed to central processing. Here we show that sophisticated integration of olfactory information begins in olfactory receptor neurons (ORNs) in Drosophila. Odor mixtures are encoded in the temporal dynamics as well as in the magnitudes of ORN responses. ORNs can respond to an inhibitory odorant with different durations depending on the level of background excitation. ORNs respond to mixtures with distinctive temporal dynamics that reflect the physicochemical properties of the constituent odorants. The insect repellent DEET (N,N-diethyl-m-toluamide), which attenuates odor responses of multiple ORNs, differs from an ORN-specific inhibitor in its effects on temporal dynamics. Our analysis reveals a means by which integration of information from odor mixtures begins in ORNs and provides insight into the contribution of inhibitory stimuli to sensory coding.

A fascinating aspect of the sense of smell is that odor mixtures often have distinctive emergent qualities, and their individual constituents are difficult to identify (1). How the emergent quality of an odor mixture arises is unknown. It is believed to originate mainly from information processing in the central nervous system, as in the insect antennal lobe (24) and the vertebrate olfactory bulb (5, 6). However, olfactory receptor neurons (ORNs) also contribute to the integration of olfactory information. For example, some individual rat ORNs respond to a binary odor mixture with response magnitudes, i.e., firing frequencies, that cannot be predicted simply from the responses to its components (7, 8). In addition, studies in moths (914) and beetles (15) have provided evidence that information about odorants in a mixture can be integrated in the periphery. However, little is known about how information is processed in ORNs. It is unclear, for example, whether individual ORNs are capable of integrating information via means other than simple alterations of their response magnitudes.

Equally intriguing in sensory coding is the role of inhibitory stimuli. In taste, certain stimuli inhibit vertebrate sweet receptors (16, 17). In olfaction, inhibitory odorants have been identified for olfactory receptors of vertebrates (1821) and invertebrates (13, 2226). When delivered as a monomolecular odor stimulus, inhibitors reduce the spontaneous activity of ORNs. However, the low spontaneous activities of most ORNs (13, 22, 23, 27) limit the dynamic range of inhibition, and inhibitory odorants may have greater functional significance when paired with excitatory odorants. Indeed, an inhibitory odorant reduced the intensity of a concurrent excitatory odor stimulus in a human psychophysical study (28), and odorants that inhibited CO2-sensing neurons prevented CO2-mediated avoidance behaviors in Drosophila (24). However, it is not clear whether a binary mixture of an excitatory stimulus and an inhibitory stimulus can be distinguished from an excitatory stimulus alone at a lower intensity. How is a mixture encoded by the primary sensory neuron?

To analyze how ORNs integrate excitatory and inhibitory odor information, we have carried out in vivo single-unit recordings in the relatively simple and well-defined Drosophila olfactory system. Our studies reveal an unexpected capacity of individual ORNs to encode odor mixtures. The results indicate how ORNs may contribute to the generation of unique percepts for odor mixtures. More generally, our findings reveal means by which inhibitory stimuli can shape sensory responses. In addition to reducing the magnitudes of excitatory responses, inhibitors may alter their temporal dynamics. Our results identify degrees of freedom by which primary sensory neurons may encode complex sensory information.

Results and Discussion

Excitatory and Inhibitory Responses to Single Odorants.

We first compared the responses to single excitatory and inhibitory odorants in a well-characterized class of ORN, ab2A, which expresses the Or59b receptor (23, 29, 30). Excitatory responses are elicited in ab2A by several structurally similar esters and ketones (31). The magnitude of the excitatory response increased with increasing odorant concentrations (Fig. S1, Left panels). However, the width of the response, defined as the time interval between the half-maximal points, i.e., between the time points in the rising and falling phases at which the response magnitudes are half of the peak response, remained largely constant (Fig. 1A and Fig. S1). At all concentrations, the excitatory response terminated shortly after the end of the stimulus.

Fig. 1.
Comparison of excitatory and inhibitory olfactory responses to single odorants. Single-unit recordings of ab2A ORNs in response to a single excitatory odorant, ethyl acetate (A) (n = 9) or an inhibitory odorant, linalool (B) (n = 24). (C) Excitatory responses ...

Inhibitory responses are elicited in ab2A by linalool, a terpene that lowers the spontaneous activity of the ab2A ORN (22) (Fig. 1B). This inhibition depends on the receptor of the ab2A cell; it is observed following ablation of the neighboring ab2B ORN, and we verified that ectopic expression of the ab2A receptor (Or59b) in a mutant “empty neuron” (23, 32) conferred sensitivity to inhibition by linalool (Fig. S2). In measurements from the endogenous ab2A cell, we observed robust inhibition with linalool at 5 × 10−3 dilutions or at higher concentrations, although not at lower concentrations. In contrast to the ab2A excitatory responses, the width of inhibitory responses increased with higher odorant concentrations (Fig. 1B, Right). We also observed prolonged inhibition by a brief pulse of another ab2A inhibitory odorant, α-terpineol (Fig. S3A). Thus, the inhibitory responses of ab2A lasted much longer than the excitatory responses. We note that when linalool was used as an excitatory odorant, against a different ORN (ab7A), the responses did not extend beyond the length of the odor stimulus (Fig. 1C). Taken together, these results suggest a role for response duration in the coding of inhibitory stimuli in ab2A. In summary, responses of an individual ORN to individual excitatory and inhibitory odorants can differ not only in polarity but also in temporal dynamics.

Inhibition in an Excitatory Background.

Having examined the responses to excitatory and inhibitory odorants individually, we next asked how ORNs encode a pulse of inhibitory odorant in the course of a response to an excitatory background odorant. We examined the response of ab2A to a 500-ms pulse of linalool, superimposed on a constant stream of background excitatory odorant, methyl acetate. Higher concentrations of the background excitatory odorant produced higher levels of background firing rates (Fig. 2A). Interestingly, the duration of inhibition decreased with increasing background excitation. At the maximum background firing levels tested, the duration of the period of inhibition was similar to the duration of the inhibitory stimulus [Fig. 2A, 1ac (5 × 10−5), and Fig. 2B, Upper]. Similar results were observed with another background excitatory odorant, ethyl acetate, suggesting that changes in the duration of inhibition are determined primarily by the degree of background activation, rather than by the identity of the excitatory odorant. One interpretation of the decline in duration of inhibition is that higher levels of the background excitatory odorant may accelerate the recovery from inhibition.

Fig. 2.
Changes in the duration of inhibition with background excitation. (A) (Left) Raster plots and the corresponding PSTHs of ab2A spike activity to a 500-ms pulse of linalool (10−1) (gray bar), with increasing concentrations of a background excitatory ...

The concentration of linalool is encoded not only by the temporal dynamics of inhibition, but also by the magnitude of the inhibitory effect. Even at high levels of background excitation, linalool inhibited ab2A firing nearly completely (Fig. 2A). Thus, the magnitude of inhibition, defined here as the reduction in number of spikes, increased proportionally with the background firing rate (Fig. 2B, Lower). An important corollary of these results is that the response to a mixture of an inhibitory odorant and an excitatory odorant cannot be determined simply by summing the effects each has individually on the magnitude of the ORN firing rate.

We note finally that other receptor-odorant combinations, one including a mosquito olfactory receptor, AgOr8 (3335), also showed a reduction in the duration of inhibition and an increase in the magnitude of inhibition following increases in the level of background excitation (Fig. S3). These results suggest that the use of these parameters to represent odor mixtures in insects may be a common principle.

Sharpened ORN Response to an Odor Mixture.

Above we have analyzed the effects of superimposing a pulse of one odor upon a prolonged stimulus of another odor. Next we considered a pulse of a binary mixture of two odorants intended to simulate a blend of components in a food source. We compared the response to a pulse of a mixture of an excitatory odorant (methyl butyrate) and an inhibitory odorant (linalool) to the response to a pulse of the excitatory odorant and the diluent (paraffin oil). At lower concentrations, methyl butyrate elicited small excitatory responses from ab2A (peak response <50 spikes), which could be completely abolished by the inhibitory odorant, linalool (Fig. S4A). These results are consistent with a previous report that inhibition can override excitation in a binary odor mixture (36).

With higher methyl butyrate concentrations, however, linalool could only attenuate, not abolish, methyl butyrate-elicited excitatory responses (Fig. S4A). Interestingly, the attenuation was more pronounced during the falling phase of the response to the mixture, and the resulting response appeared “sharpened” (Fig. 3 A and B and Fig. S4A). In fact, linalool nearly halved the width of methyl butyrate-elicited responses to ~200 ms (Fig. 3C, Upper Left).

Fig. 3.
Sharpening of the excitatory response by an inhibitory odorant in a mixture. (A) Responses of ab2A ORNs to an odor mixture containing equal parts of an excitatory odorant, methyl butyrate (1bu), and either an inhibitory odorant, linalool (Lin, 10−1 ...

Notably, the fast-falling dynamics of the response to a 500-ms pulse of the mixture also differed from those of the response to a 200-ms pulse of methyl butyrate alone over a variety of concentrations (Fig. 3D and Fig. S4B). Thus, in the periphery, an ORN is capable of encoding a binary odor mixture with unique temporal dynamics that distinguish the mixture from the excitatory odorant alone, even when the excitatory odorant is presented at different concentrations or with different stimulus durations.

We reasoned that the unique response dynamics elicited by linalool may arise from a different effect on the rising and falling phases of the response. Indeed, the time required for the response to fall from its peak to its half-maximal magnitude (t_fall50) was much shorter in the presence of linalool (Fig. 3C, Upper Right), whereas no difference was observed in the time required for the response to rise to its half-maximal magnitude. We examined the derivative of the response to quantify the rate at which responses rise and fall (Fig. 3B, Lower Left and Right). The analysis showed that linalool did not affect the rising process, but accelerated the falling process (Fig. 3C, Lower Left and Right), as measured by the maximum rising rate and the first maximum falling rate (solid arrowheads and open arrowheads, respectively, in Fig. 3B, Lower Left and Right).

In addition, to visualize the “net effect” of the inhibitory odorant in a mixture, we subtracted the control responses (methyl butyrate mixed with paraffin oil) from the responses to the binary odor mixture containing methyl butyrate and linalool (Fig. S5). Consistent with the notion that the response to a mixture of an inhibitory odorant and an excitatory odorant cannot be determined simply by summing the effects each has individually on the magnitude of the ORN firing rate (Fig. 2), the net inhibitory effect of linalool in a binary mixture varied depending on the concentration of the concurrent excitatory odorant, methyl butyrate (Fig. S5).

DEET Attenuation Does Not Change Response Dynamics.

Having found that linalool, an ORN-specific inhibitor, affects the temporal dynamics of the response to a concurrent excitatory stimulus, we next asked whether a putative inhibitor of special interest, DEET (N,N-diethyl-m-toluamide), has similar effects on response dynamics. DEET attenuates odor responses in multiple ORN classes by blocking the common coreceptor Or83b (37) and/or by reducing the level of the concurrent excitatory odorant vapor (38), referred to as a fixative effect. We compared methyl butyrate-elicited responses with or without DEET. Indeed, DEET attenuated the responses elicited by methyl butyrate at higher concentrations (Fig. 3E). However, such attenuation resulted in little if any change in the response dynamics (Fig. 3 F and G). These results suggested that DEET differs from linalool in the mechanism by which it attenuates the activation of ab2A by methyl butyrate. Photoionization detector (PID) measurements showed that DEET reduced methyl butyrate concentration in the gaseous phase (Fig. 3H), supporting the notion that DEET has a fixative effect on other odorants (38). Our results do not exclude the possibility of additional inhibitory effects of DEET.

Odor Stimuli Differ in Their Temporal Dynamics.

Why does linalool predominantly affect the falling phase of the excitatory response? PID measurements suggested that, although linalool and methyl butyrate were premixed in solution and carried via the same puff of air, their vapors may reach the fly's antenna at different rates (Fig. 4A). At all concentrations tested, levels of methyl butyrate vapor rose faster than linalool, as reflected by the smaller rising time constants (Fig. 4B; for τon, see Materials and Methods). Consequently, methyl butyrate-elicited excitatory responses also rose faster than linalool-elicited inhibitory responses, which may explain why the falling phase of methyl butyrate-elicited excitatory response was preferentially inhibited by linalool.

Fig. 4.
Temporal dynamics of excitatory and inhibitory odor stimuli. (A and C) Photoionization detector (PID) measurements of odor stimuli, presented as 500-ms pulses (gray bars) (n = 12). Odor dilutions are indicated on the right in each panel. Information about ...

To verify this interpretation, we paired linalool with three other ab2A excitatory odorants (methyl acetate, ethyl acetate, and 2,3-butanedione), all of which demonstrate fast-rising PID profiles like that of methyl butyrate (Fig. S6). The physicochemical similarity shared by these strong ab2A excitatory odorants may contribute to the similarity of their PID profiles. As expected, we observed comparable asymmetric sharpening of the response dynamics by linalool in all three combinations (Fig. S6). Thus, in an odor mixture, a slow-arriving inhibitory odorant can sharpen the response elicited by a fast-arriving excitatory odorant. Our findings support the notions that ORN responses can reflect the relative differences in stimulus dynamics and that insect olfactory systems can respond to odor stimuli with high temporal precision (39).

Biphasic Response to an Odor Mixture.

We next considered the reciprocal situation: the pairing of a fast-arriving inhibitory odorant (6-methyl-5-hepten-2-one) with a slow-arriving excitatory odorant (4-methylphenol), which act on AgOr1 (34, 35, 40). For all concentrations tested, 6-methyl-5-hepten-2-one vapor rose faster than 4-methylphenol (Fig. 4 C and D). Interestingly, at a low concentration of the excitatory odorant 4-methylphenol, addition of the inhibitor 6-methyl-5-hepten-2-one resulted in a biphasic response (Fig. 5A). At a higher concentration of the excitatory odorant, a single, prolonged plateau was observed (Fig. 5B). At either concentration, the temporal dynamics differed from those observed for the excitatory odorant alone. We note that, in addition to rising slowly, 4-methylphenol vapor fell slowly (Fig. 4B), which may contribute to the long duration of the excitatory responses in AgOr1 (Fig. 5). Taken together, in addition to attenuating the excitatory responses, an inhibitory odorant in a mixture can generate distinctive response dynamics, depending on the properties and concentrations of the two odorants. Thus, an individual ORN may respond with distinctive dynamics that serve as a unique signature for a given blend of odorants.

Fig. 5.
Unique temporal dynamics elicited by an inhibitory odorant in a mixture of 4-methylphenol and 6-methyl-5-hepten-2-one are excitatory and inhibitory odorants for a mosquito receptor, AgOr1, respectively. Responses of AgOr1-expressing ORNs in response to ...

Finally, we note that the relative differences in the PID profiles of fast and slow odorants were observed consistently with different odor delivery methods (Fig. S7 and SI Text). The relatively slow kinetics of linalool emission was also observed using a method in which odor delivery tubes were not used (41). Most “fast” odorants in our study have higher saturated vapor pressures and lower boiling points than the “slow” odorants, which is indicative of higher volatilities (Fig. 4).

Functional Implications for Higher-Order Neurons.

As the temporal dynamics of the postsynaptic projection neurons (PNs) largely follow the temporal dynamics of the presynaptic ORNs (42), the unique temporal features of ORN responses to a given odor mixture are likely to be preserved in the PN responses. To address the effects of ORN inhibition on PN response, we used a formula that predicts the postsynaptic PN responses on the basis of the presynaptic ORN responses (43). We found a striking inhibitory effect of linalool on the PN responses (Fig. 6).

Fig. 6.
Predicted responses of the postsynaptic projection neurons (PNs). The responses of the PNs receiving direct inputs from ab2A ORNs were estimated using the formula derived by Olsen et al. (43) using parameters for PNs in the DM4 glomerulus. ORN responses ...

A particularly dramatic effect is predicted at low odorant concentrations. The greater effect at lower concentrations may be explained by the nonlinear amplification observed at the ORN–PN synapse (42, 44): because weak ORN inputs are amplified more than strong ORN inputs in the PNs, inhibition of ORNs at lower concentrations caused greater inhibition in the corresponding range in the PN dose–response curve. Consequently, the PN dose–response curve appears steeper, characteristic of a curve with a greater Hill coefficient (PN panels). Thus, our analysis supports the notion that inhibition of ORNs is translated into inhibition of PNs; in fact, at low concentrations the inhibitory effects are likely to be even more pronounced in PNs than in ORNs.

Conclusions.

We have found that ORNs are capable of sophisticated information processing. The emergent quality of an odor mixture has long been believed to arise from integration of olfactory information in the CNS. We have found that olfactory information is integrated in individual ORNs in the form of response dynamics as well as response magnitude.

We have analyzed how individual ORNs integrate olfactory information. We found that, when an inhibitory odorant is delivered in the presence of a background excitatory odorant, the duration and the magnitude of the inhibitory response reflect the concentrations of the inhibitory and excitatory odorants. ORNs also respond with distinctive temporal dynamics to premixed combinations of inhibitory and excitatory odorants. We show that the temporal dynamics of odor stimuli differ markedly, presumably due to differences in their physicochemical properties. The dynamics of the response to an odor mixture are likely to be shaped by the dynamics of its component stimuli. Taken together, our findings provide evidence that ORNs encode odor mixtures not only by altering their response magnitudes, but also by varying their response dynamics.

Do the temporal dynamics of ORNs contribute to odor identification and discrimination? A recent study showed that temporal heterogeneity in ORN firing patterns is crucial for generating spatiotemporal neural codes for odors in the locust antennal lobe (45), which represents odor identity in a concentration-independent manner (46). In the antennal lobe of moths, precise temporal activation patterns unique to certain odor blends provide a means for triggering innate behaviors (47). Moreover, mutant flies with only one functional type of Or-expressing ORN can be trained to discriminate different odorants, suggesting that odors could be discriminated by virtue of the temporal dynamics that they elicited in the ORN (48). Finally, we note that an emerging body of evidence supports a role for temporal dynamics in the coding of individual odorants in vertebrates (4951), Thus, ORN response dynamics distinctive for an odor mixture seem likely to make a major contribution to the emergent quality of the mixture, binding features of the individual constituents into a unifying odor percept.

In perfumery, odorants of high, moderate, and lower volatility are commonly blended together to create the top, middle, and base notes, respectively, of a perfume. The composition and ratio of these notes endow the perfume with a signature scent (52). Human psychophysical studies have shown that odorants in a mixture are processed and perceived in series (53). As shown by our PID measurements (Fig. 4), inhibitory and excitatory odorants may also arrive in series and may interact so as to give rise to unique, mixture-specific ORN response dynamics. In this manner, inhibitory odorants may influence not only the perception of odor intensity, but also the perception of odor identity. Inhibitory stimuli could influence the perception of stimulus identity in other sensory modalities as well.

Finally, we note that, for many years, analysis of odors that drive animal and human behavior has focused primarily on their excitatory constituents. Our results invite a greater emphasis on the identification and analysis of their inhibitory constituents.

Materials and Methods

Drosophila Stocks.

All experiments were performed on adult female flies, 5–7 d after eclosion, except for “empty neuron” recordings, in which both male and female flies were used. Flies were reared at 25 °C in an incubator with a 12-h light–dark cycle. Wild-type flies are Canton-S. “Empty neuron” recordings were from flies of genotype w; Δhalo/Δhalo; Or22a-GAL4/UAS-Or. The ab3A mutant flies and Or22a-GAL4 and UAS-Or transgenic lines were described previously (23, 32, 34, 40). Table S1 lists genotypes of the flies used in all experiments.

Electrophysiology and Data Analysis.

Extracellular single-unit recordings were performed essentially as described previously (23). All data were acquired using Axoscope 10.2 (Molecular Devices), and ORN spikes were detected using routines in Igor Pro-6.01 (Wavemetrics) (44). Peri-stimulus time histograms (PSTHs) were obtained by averaging spike activities in 50-ms bins (44) and smoothed using a binomial algorithm (Igor Pro-6.01, Wavemetrics). Response width was calculated on the basis of average PSTHs, as the time interval between half-maximal responses in the case of excitation (Max50, excitation) or half-minimal responses in the case of inhibition (Min50, inhibition). Two additional PSTH traces were generated as PSTH + SEM and PSTH − SEM, respectively. The time intervals of Max50 or Min50 for the two additional PSTH traces determined the ranges for error bars of the response widths.

Odor Stimuli.

For short odor pulses, odor stimuli (50 μL) were delivered from a Pasteur pipette via a pulse of air (200 mL/min) into the main air stream (2,000 mL/min) as described previously (23, 34), except for binary odor mixture experiments (Figs. 3 and and5)5) in which 100 μL of a premixed odor dilution was added to two filter discs, placed adjacently along the long axis of the Pasteur pipette (54). We observed no difference in the temporal dynamics of odor stimuli using one or two filter discs in our PID measurements. Background odor stimuli were delivered from a 125-mL flask containing 3 mL of odor dilutions directly downstream of the main airflow (2,000 mL/min).

PID Measurements.

A fast response mini-PID (200a, Aurora Scientific) was positioned between the head of a fly and the mouth of the odor delivery tube as described previously (55, 56). Fig. S7 and SI Text show the PID setup in relation to the odor delivery system. The rising phase of the PID response was fitted with an exponential curve y(t) = A*exp(−ton). Parameters of the fits are listed in Table S2.

Supplementary Material

Supporting Information:

Acknowledgments

We thank W. van der Goes van Naters for help with the electrophysiology setup, and V. Bhandawat and M. T. H. Do for assistance with data analyses. We thank K. Menuz, J. Reisert, C. A. Yao, L. Zwiebel, and G. M. Thomas for comments on the manuscript. We also thank B. J. Dickson for the Or85b-GAL4 transgenic line. C.M. and T.E. acknowledge support from The Raymond and Beverly Sackler Institute for Biological, Physical and Engineering Sciences and from an Alfred P. Sloan Foundation Fellowship. This work was funded by National Institutes of Health grants to J.R.C. and by a grant from the Foundation for the National Institutes of Health through the Grand Challenges in Global Health Initiative (GCGH no. 121).

Footnotes

The authors declare no conflict of interest.

This article is a PNAS Direct Submission.

This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1100369108/-/DCSupplemental.

References

1. Laing DG, Francis GW. The capacity of humans to identify odors in mixtures. Physiol Behav. 1989;46:809–814. [PubMed]
2. Deisig N, Giurfa M, Lachnit H, Sandoz JC. Neural representation of olfactory mixtures in the honeybee antennal lobe. Eur J Neurosci. 2006;24:1161–1174. [PubMed]
3. Silbering AF, Galizia CG. Processing of odor mixtures in the Drosophila antennal lobe reveals both global inhibition and glomerulus-specific interactions. J Neurosci. 2007;27:11966–11977. [PubMed]
4. Broome BM, Jayaraman V, Laurent G. Encoding and decoding of overlapping odor sequences. Neuron. 2006;51:467–482. [PubMed]
5. Tabor R, Yaksi E, Weislogel JM, Friedrich RW. Processing of odor mixtures in the zebrafish olfactory bulb. J Neurosci. 2004;24:6611–6620. [PubMed]
6. Johnson BA, Ong J, Leon M. Glomerular activity patterns evoked by natural odor objects in the rat olfactory bulb are related to patterns evoked by major odorant components. J Comp Neurol. 2010;518:1542–1555. [PMC free article] [PubMed]
7. Duchamp-Viret P, Duchamp A, Chaput MA. Single olfactory sensory neurons simultaneously integrate the components of an odour mixture. Eur J Neurosci. 2003;18:2690–2696. [PubMed]
8. Rospars JP, Lansky P, Chaput M, Duchamp-Viret P. Competitive and noncompetitive odorant interactions in the early neural coding of odorant mixtures. J Neurosci. 2008;28:2659–2666. [PubMed]
9. Den Otter CJ. Single sensillum responses in the male moth Adoxophyes orana (F.v.R.) to female sex pheromone components and their geometrical isomers. J Comp Physiol A Neuroethol Sens Neural Behav Physiol. 1977;121:205–222.
10. van der Pers JNC, Thomas G, Den Otter CJ. Interactions between plant odours and pheromone reception in small ermine moths (Lepidoptera: Yponomeutidae) Chem Senses. 1980;5:367–371.
11. Ochieng SA, Park KC, Baker TC. Host plant volatiles synergize responses of sex pheromone-specific olfactory receptor neurons in male Helicoverpa zea. J Comp Physiol A Neuroethol Sens Neural Behav Physiol. 2002;188:325–333. [PubMed]
12. Lee SG, Baker TC. Incomplete electrical isolation of sex-pheromone responsive olfactory receptor neurons from neighboring sensilla. J Insect Physiol. 2008;54:663–671. [PubMed]
13. Kaissling KE, Meng LZ, Bestmann HJ. Responses of bombykol receptor cells to (Z,E)-4,6-hexadecadiene and linalool. J Comp Physiol A Neuroethol Sens Neural Behav Physiol. 1989;165:147–154.
14. Kramer E. Attractivity of pheromone surpassed by time-patterned application of two nonpheromone compounds. J Insect Behav. 1992;5:83–97.
15. Nikonov AA, Leal WS. Peripheral coding of sex pheromone and a behavioral antagonist in the Japanese beetle, Popillia japonica. J Chem Ecol. 2002;28:1075–1089. [PubMed]
16. Xu H, et al. Different functional roles of T1R subunits in the heteromeric taste receptors. Proc Natl Acad Sci USA. 2004;101:14258–14263. [PMC free article] [PubMed]
17. Galindo-Cuspinera V, Winnig M, Bufe B, Meyerhof W, Breslin PA. A TAS1R receptor-based explanation of sweet ‘water-taste’ Nature. 2006;441:354–357. [PubMed]
18. Araneda RC, Kini AD, Firestein S. The molecular receptive range of an odorant receptor. Nat Neurosci. 2000;3:1248–1255. [PubMed]
19. Spehr M, et al. Identification of a testicular odorant receptor mediating human sperm chemotaxis. Science. 2003;299:2054–2058. [PubMed]
20. Oka Y, Omura M, Kataoka H, Touhara K. Olfactory receptor antagonism between odorants. EMBO J. 2004;23:120–126. [PMC free article] [PubMed]
21. Oka Y, Nakamura A, Watanabe H, Touhara K. An odorant derivative as an antagonist for an olfactory receptor. Chem Senses. 2004;29:815–822. [PubMed]
22. de Bruyne M, Foster K, Carlson JR. Odor coding in the Drosophila antenna. Neuron. 2001;30:537–552. [PubMed]
23. Hallem EA, Ho MG, Carlson JR. The molecular basis of odor coding in the Drosophila antenna. Cell. 2004;117:965–979. [PubMed]
24. Turner SL, Ray A. Modification of CO(2) avoidance behaviour in Drosophila by inhibitory odorants. Nature. 2009;461:477–481. [PubMed]
25. Michel WC, McClintock TS, Ache BW. Inhibition of lobster olfactory receptor cells by an odor-activated potassium conductance. J Neurophysiol. 1991;65:446–453. [PubMed]
26. Shields VD, Hildebrand JG. Responses of a population of antennal olfactory receptor cells in the female moth Manduca sexta to plant-associated volatile organic compounds. J Comp Physiol A Neuroethol Sens Neural Behav Physiol. 2000-2001;186:1135–1151. [PubMed]
27. Reisert J, Restrepo D. Molecular tuning of odorant receptors and its implication for odor signal processing. Chem Senses. 2009;34:535–545. [PMC free article] [PubMed]
28. Brodin M, Laska M, Olsson MJ. Odor interaction between Bourgeonal and its antagonist undecanal. Chem Senses. 2009;34:625–630. [PubMed]
29. Couto A, Alenius M, Dickson BJ. Molecular, anatomical, and functional organization of the Drosophila olfactory system. Curr Biol. 2005;15:1535–1547. [PubMed]
30. Fishilevich E, Vosshall LB. Genetic and functional subdivision of the Drosophila antennal lobe. Curr Biol. 2005;15:1548–1553. [PubMed]
31. Hallem EA, Carlson JR. Coding of odors by a receptor repertoire. Cell. 2006;125:143–160. [PubMed]
32. Dobritsa AA, van der Goes van Naters W, Warr CG, Steinbrecht RA, Carlson JR. Integrating the molecular and cellular basis of odor coding in the Drosophila antenna. Neuron. 2003;37:827–841. [PubMed]
33. Lu T, et al. Odor coding in the maxillary palp of the malaria vector mosquito Anopheles gambiae. Curr Biol. 2007;17:1533–1544. [PMC free article] [PubMed]
34. Carey AF, Wang G, Su CY, Zwiebel LJ, Carlson JR. Odorant reception in the malaria mosquito Anopheles gambiae. Nature. 2010;464:66–71. [PMC free article] [PubMed]
35. Wang G, Carey AF, Carlson JR, Zwiebel LJ. Molecular basis of odor coding in the malaria vector mosquito Anopheles gambiae. Proc Natl Acad Sci USA. 2010;107:4418–4423. [PMC free article] [PubMed]
36. Schuckel J, Torkkeli PH, French AS. Two interacting olfactory transduction mechanisms have linked polarities and dynamics in Drosophila melanogaster antennal basiconic sensilla neurons. J Neurophysiol. 2009;102:214–223. [PubMed]
37. Ditzen M, Pellegrino M, Vosshall LB. Insect odorant receptors are molecular targets of the insect repellent DEET. Science. 2008;319:1838–1842. [PubMed]
38. Syed Z, Leal WS. Mosquitoes smell and avoid the insect repellent DEET. Proc Natl Acad Sci USA. 2008;105:13598–13603. [PMC free article] [PubMed]
39. Vickers NJ, Christensen TA, Baker TC, Hildebrand JG. Odour-plume dynamics influence the brain's olfactory code. Nature. 2001;410:466–470. [PubMed]
40. Hallem EA, Nicole Fox A, Zwiebel LJ, Carlson JR. Olfaction: Mosquito receptor for human-sweat odorant. Nature. 2004;427:212–213. [PubMed]
41. Noe SM, Ciccioli P, Brancaleoni E, Loreto F, Niinemets U. Emissions of monoterpenes linalool and ocimene respond differently to environmental changes due to differences in physico-chemical characteristics. Atmos Environ. 2006;40:4649–4662.
42. Olsen SR, Bhandawat V, Wilson RI. Excitatory interactions between olfactory processing channels in the Drosophila antennal lobe. Neuron. 2007;54:89–103. [PMC free article] [PubMed]
43. Olsen SR, Bhandawat V, Wilson RI. Divisive normalization in olfactory population codes. Neuron. 2010;66:287–299. [PMC free article] [PubMed]
44. Bhandawat V, Olsen SR, Gouwens NW, Schlief ML, Wilson RI. Sensory processing in the Drosophila antennal lobe increases reliability and separability of ensemble odor representations. Nat Neurosci. 2007;10:1474–1482. [PMC free article] [PubMed]
45. Raman B, Joseph J, Tang J, Stopfer M. Temporally diverse firing patterns in olfactory receptor neurons underlie spatiotemporal neural codes for odors. J Neurosci. 2010;30:1994–2006. [PMC free article] [PubMed]
46. Stopfer M, Jayaraman V, Laurent G. Intensity versus identity coding in an olfactory system. Neuron. 2003;39:991–1004. [PubMed]
47. Riffell JA, Lei H, Christensen TA, Hildebrand JG. Characterization and coding of behaviorally significant odor mixtures. Curr Biol. 2009;19:335–340. [PMC free article] [PubMed]
48. DasGupta S, Waddell S. Learned odor discrimination in Drosophila without combinatorial odor maps in the antennal lobe. Curr Biol. 2008;18:1668–1674. [PMC free article] [PubMed]
49. Spors H, Wachowiak M, Cohen LB, Friedrich RW. Temporal dynamics and latency patterns of receptor neuron input to the olfactory bulb. J Neurosci. 2006;26:1247–1259. [PubMed]
50. Carey RM, Verhagen JV, Wesson DW, Pírez N, Wachowiak M. Temporal structure of receptor neuron input to the olfactory bulb imaged in behaving rats. J Neurophysiol. 2009;101:1073–1088. [PMC free article] [PubMed]
51. Junek S, Kludt E, Wolf F, Schild D. Olfactory coding with patterns of response latencies. Neuron. 2010;67:872–884. [PubMed]
52. Aftel M. Essence and Alchemy: A Natural History of Perfume. New York: North Point Press; 2001.
53. Laing DG, Eddy A, Francis GW, Stephens L. Evidence for the temporal processing of odor mixtures in humans. Brain Res. 1994;651:317–328. [PubMed]
54. de Brito Sanchez MG, Kaissling KE. Inhibitory and excitatory effects of iodobenzene on the antennal benzoic acid receptor cells of the female silk moth Bombyx mori L. Chem Senses. 2005;30:435–442. [PubMed]
55. Vetter RS, Sage AE, Justus KA, Cardé RT, Galizia CG. Temporal integrity of an airborne odor stimulus is greatly affected by physical aspects of the odor delivery system. Chem Senses. 2006;31:359–369. [PubMed]
56. Schuckel J, Meisner S, Torkkeli PH, French AS. Dynamic properties of Drosophila olfactory electroantennograms. J Comp Physiol A Neuroethol Sens Neural Behav Physiol. 2008;194:483–489. [PubMed]

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