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Proc Biol Sci. 2009 October 7; 276(1672): 3413–3420.
Published online 2009 July 8. doi:  10.1098/rspb.2009.0705
PMCID: PMC2817181

Odour intensity learning in fruit flies

Abstract

Animals' behaviour towards odours depends on both odour quality and odour intensity. While neuronal coding of odour quality is fairly well studied, how odour intensity is treated by olfactory systems is less clear. Here we study odour intensity processing at the behavioural level, using the fruit fly Drosophila melanogaster. We trained flies by pairing a MEDIUM intensity of an odour with electric shock, and then, at a following test phase, measured flies' conditioned avoidance of either this previously trained MEDIUM intensity or a LOWer or a HIGHer intensity. With respect to 3-octanol, n-amylacetate and 4-methylcyclohexanol, we found that conditioned avoidance is strongest when training and test intensities match, speaking for intensity-specific memories. With respect to a fourth odour, benzaldehyde, on the other hand, we found no such intensity specificity. These results form the basis for further studies of odour intensity processing at the behavioural, neuronal and molecular level.

Keywords: fruit fly, olfaction, odour intensity, associative learning, recognition, benzaldehyde

1. Introduction

Animals use odours for finding food and for detecting predators, competitors or social interaction partners. For all these functions, they potentially rely on both odour quality and odour intensity: while the quality of an odour can, for example, signal edibility, its intensity can be used to track down the food. Although neuronal coding of odour quality is fairly well studied in various species (e.g. reviewed by Buck 1996; Galizia & Menzel 2000; Laberge & Hara 2001; Laurent et al. 2001; Korsching 2002; Mainen 2006; Johnson & Leon 2007; Gerber et al. 2009), how odour intensity is treated by olfactory systems is less clear. This needs to be resolved to fully appreciate the richness of olfactory behaviour and to reach an understanding of olfaction detailed enough to permit, for instance, the implementation into a biologically inspired artificial chemosensor.

We therefore study odour intensity processing, using the fruit fly Drosophila melanogaster. Like other insects, the fruit fly is a favourable system for such research as its olfactory system shares its basic molecular and cellular architecture with vertebrates, but comprises far fewer cells (reviewed by Hildebrand & Shepherd 1997; Strausfeld & Hildebrand 1999; Davis 2004; Ache & Young 2005; Bargmann 2006). The fruit fly olfactory system is particularly well studied at the molecular and cellular level, and recently also at the physiological level (reviewed by Vosshall 2000; Hallem et al. 2006; Dahanukar et al. 2005; Benton 2006; Fiala 2007; Vosshall & Stocker 2007; Gerber et al. 2009) and is particularly accessible to transgenic intervention (Brand & Perrimon 1993; Phelps & Brand 1998; Duffy 2002). Also, fruit flies have a fairly rich repertoire of olfactory behavior: for example, having experienced an odour together with electric shock, they later strongly avoid this odour (Tully & Quinn 1985). Other, non-trained odours only partially elicit such conditioned avoidance (T. Niewalda 2009, Universität Würzburg, personal communication). Because such generalization is typically only partial, flies must to some extent recognize odour quality. That is, it matters to the flies whether the odour at test has the same quality as the trained one. Here we ask whether odour intensity is also recognized: do the flies care whether the odour at test has the same intensity as during training?

In brief, we trained flies with pairings of electric shock with an odour (figure 1) at a particular MEDIUM intensity. After training, we tested conditioned avoidance in different groups of flies that were offered either the previously trained MEDIUM or a LOWer or a HIGHer intensity. If the flies include intensity information in their memory trace (i.e. ‘a MEDIUM intensity of this odour predicts shock’), they will show strongest conditioned avoidance when the previously trained MEDIUM intensity is indeed presented at test (figure 2a). Alternatively, if the memory trace does not contain any intensity information (i.e. ‘this odour predicts shock’), the flies will show stronger conditioned avoidance when more of the previously trained odour is presented; that is, the HIGHest intensity will induce the strongest conditioned avoidance at test (figure 2b). Clearly, this type of experiment specifically probes whether the flies ‘spontaneously’ integrate intensity information into their memory trace, without explicitly being cued to do so. Using such rationale, we probed for intensity learning with respect to the four odours that have been regularly used in neurogenetic analyses of Drosophila olfactory learning.

Figure 1.

The learning paradigm. One group of flies was trained such that the odour was paired with the shock, and the solvent paraffin oil was presented alone (top); for a second group, we used a reciprocal training regimen (bottom). Each group was then tested ...

Figure 2.

Experimental strategy to probe for intensity learning. We trained flies using a designated MEDIUM intensity of an odour. In a subsequent test, different groups of flies were then offered either the trained MEDIUM intensity or a LOWer or a HIGHer intensity. ...

2. Material and methods

(a) Flies

Drosophila melanogaster of the Canton-Special wild-type strain were maintained as mass culture at 25°C and 60 to 70 per cent relative humidity, under a 14 : 10 h light : dark cycle. On the day prior to the experiments, 1- to 4-day-old flies were collected in fresh food vials and kept overnight at 18°C and 60 to 70 per cent relative humidity.

(b) Odours

As odours, we used 3-octanol (OCT), n-amylacetate (AM), 4-methylcyclohexanol (MCH) and benzaldehyde (BA; all from Fluka, Steinheim, Germany, except AM, which is from Merck, Darmstadt, Germany; CAS 589-98-0, 628-63-7, 589-91-3, 100-52-7). Odours were diluted in solvent paraffin oil (Merck; CAS 8012-95-1); the ‘relative concentration’ of an odour is operationally defined as dilution relative to pure odour throughout this paper. Differences in such relative concentration did lead to behaviourally relevant differences in intensity for all odours used (figure 3). Throughout, we applied 350 µl of either the odour solution at the specified relative concentration or the solvent paraffin oil in 1-cm-deep Teflon containers of 15 mm diameter.

Figure 3.

Dose–effect curves for olfactory learning. For the indicated odours, we characterized the dose–effect relationship of olfactory learning. With respect to each odour, we then designated relative concentrations as LOW, MEDIUM or HIGH intensity, ...

(c) Learning paradigm

Experiments were performed at approximately 25°C and 70 to 80 per cent relative humidity, under the light from a 50 W light bulb placed approximately 50 cm above the experimental setup (Schwaerzel et al. 2003; Yarali et al. 2008). Flies were trained and tested in groups of approximately 100. Training started by loading the flies into the set-up (figure 1; 0:00 min). The odour was presented from 1:00 min on for 1 min; the electric shock followed at 1:15 min as 12 pulses of 100 V; each pulse was 1.2 s long and was followed by the next pulse with an onset-to-onset interval of 5 s. The solvent was then presented from 3:00 min on, also for 1 min. In half of the cases, contrary to the above, the training started with the no-shock treatment. Once this training was completed (9:00 min), flies were transferred to a T-maze, where they could choose between the odour and the solvent arm. After an additional 2 min, the arms of the maze were closed and flies on each side were counted. A preference index (PREF) was calculated as

equation image
2.1

In this equation, # indicates the number of flies in the respective maze arm. For every group trained as above (i.e. Odour−Shock / Solvent [or Solvent / Odour−Shock]), another group was trained reciprocally (Solvent−Shock / Odour [or Odour / Solvent−Shock]; figure 1; in the figure as well as below, the subgroups that received the respective other sequence of training events are omitted for clarity). As these kinds of group received identical treatment except for the contingency between odour and shock during training, any difference between their olfactory preferences must reflect associative learning. This difference is quantified as

equation image
2.2

In this equation, the subscripts of PREF represent the respective training regimen. Learning indices thus range from −100 to 100, positive values indicating conditioned approach and negative values conditioned avoidance.

(d) Statistics

The data were analysed using non-parametric statistics throughout. For global comparisons between multiple groups, Kruskal–Wallis tests (KW test) were used. For comparing scores between pairs of groups, we used Mann–Whitney U tests (U test). When multiple pairwise comparisons were made, a Bonferroni correction kept the experiment-wide error rate at 5 per cent by dividing the critical p-value by the number of tests (e.g. p < 0.05/2 if two tests are applied). All statistics were performed on a PC with Statistica (Statsoft, Tulsa, OK, USA). We report the statistics in the figure captions.

(e) Experimental design

For each odour, we performed two experiments. The first experiment characterized the dose dependency of olfactory learning and retrieval with respect to that odour. That is, between groups, we varied the relative concentration of the odour; importantly, for each group, the relative concentration at test equalled the one used for training. Based on this experiment, we designated three relative concentrations of the respective odour as, LOW, MEDIUM or HIGH intensity. For MCH we used a fourth intensity, VERY LOW.

The second experiment then used these intensities to probe for intensity learning. We trained the flies with the designated MEDIUM intensity; then, at test, different groups of flies were offered either the same MEDIUM intensity or a LOWer one or a HIGHer one (or VERY LOW for MCH). If during training the flies learned that specifically the MEDIUM intensity of the odour predicted shock, they would show the strongest conditioned avoidance when that particular intensity was offered at test (as sketched in figure 2a). Contrarily, if the flies did not form any intensity-specific memory for the odour, they would show the strongest conditioned avoidance at test when the intensity was the HIGHest (figure 2b).

3. Results

(a) Intensity learning for 3-octanol, n-amylacetate and 4-methylcyclohexanol

Learning scores improved with increasing relative concentration of OCT, within the range we looked at, arguing that the different relative concentrations we used support different perceived intensities (figure 3a(i)). We assigned intensities as LOW, MEDIUM and HIGH according to the learning scores they supported (figure 3a(ii)).

We then used these three intensities to test whether flies learn about OCT intensity. We trained the flies with the designated MEDIUM intensity and varied the intensity between groups during the test. When the MEDIUM intensity was used for the test, we found clearly stronger conditioned avoidance compared with either LOW or HIGH intensity (figure 4a). Thus, flies showed the strongest conditioned avoidance to that odour intensity they have been trained with, suggesting that they do indeed include intensity information in their memory.

Figure 4.

Intensity learning for OCT, AM and MCH but not for BA. Statistics refer to U tests. n.s.: p > 0.05/2, *p < 0.05/2, except in (c) and (d(iii)), where n.s.: p > 0.05/3, *p < 0.05/3. Other details as in the legend of figure 3 ...

Two further odours, AM (figures 3b(i,ii) and and44b) and MCH (figures 3c(i,ii) and and44c), yielded the same pattern of results. Thus, we conclude that flies do form intensity-specific memories for AM, MCH and OCT.

(b) No intensity learning for benzaldehyde

Concerning the fourth odour, BA, we found that learning scores improve with increasing relative concentration within the covered range (figure 3d(i)); assignment of intensities as LOW, MEDIUM and HIGH followed the learning scores they support (figure 3d(ii)).

We then trained flies using the designated MEDIUM intensity of BA and, at test, offered them either this MEDIUM intensity or the LOW or the HIGH intensity. Despite having been trained with MEDIUM, flies avoided the HIGH intensity more strongly at test, whereas conditioned avoidance of LOW did not statistically differ from that of MEDIUM (figure 4d(i)).

To extend and confirm these results, we trained three further groups with the designated LOW intensity of BA and found the same pattern of results: both MEDIUM and HIGH intensity at test were avoided more strongly than the LOW intensity, which had been presented during training (figure 4d(ii)). Thus, regardless of which BA intensity was present at training, flies avoided HIGHer intensities more strongly at test, directly arguing against an intensity-specific memory trace with respect to BA.

Interestingly, despite a mismatch between training and testing intensity, when trained with the MEDIUM intensity but tested with LOW, flies had better scores than when trained and tested with LOW. This demonstrates that training with MEDIUM establishes stronger memories for BA than training with LOW does; critically, this difference in memory strength shows under conditions of equal testing intensity (figure 4d(iii)).

Thus, intensity affects both the establishment and the recall of BA memory: for a given testing intensity, scores were higher when the training intensity was higher. And, for a given training intensity, scores were higher for higher testing intensities. Most important, it was irrelevant to the flies whether the intensities at training and test matched. Thus, as far as BA is concerned, flies apparently do not include intensity information in their olfactory memory.

4. Discussion

(a) Exception and rule in Drosophila intensity learning

Do flies spontaneously integrate the information about odour intensity into their memory? Yes and no. With respect to OCT, AM and MCH, flies did show intensity learning, but for BA we found no intensity learning using the present experimental rationale (figure 4e). Using somewhat modified experimental designs, intensity learning for pentyl acetate, 6-methyl-5-hepten-2-one (see fig. 4 in DasGupta & Waddell 2008) and iso-AM (fig. 6a in Masek & Heisenberg 2008) has previously been found. Thus, it seems that BA is the ‘odd one out’ with respect to intensity learning. Indeed, BA has long been suggested to be peculiar: loss of function of the abnormal chemosensory jump 6 gene (CG9151) abolishes maxillary palp electrophysiological responses to BA (Ayer & Carlson 1992), but not to four other tested odours. Loss of function of the pentagon gene in turn disturbs the flies' jump response to BA, but not to three other tested odours (Helfand & Carlson 1989). Furthermore, after surgical removal of the antenna and the maxillary palps, flies still avoid BA, while avoidance of two other odours is lost (Keene et al. 2004). In addition, activity of the DPM neuron seems to be required for the formation of BA memory, while such activity is required with respect to two other tested odours only during the consolidation of memory (Keene et al. 2004). Interestingly, in honeybees also intensity learning has been demonstrated for some (1-hexanol and 2-octanone: Wright et al. 2005) but not for other odours (linalool: Pelz et al. 1997; geraniol: Wright et al. 2005). In both flies and bees, the neuronal determinants as well as ecological relevance of these discrepancies between odours remain clouded.

(b) Under which conditions may high–low differential training be suitable to measure intensity learning?

The present experimental design allows probing whether flies integrate information about odour intensity into their olfactory memory, without being explicitly cued to do so. Two previous studies (Xia & Tully 2007; Masek & Heisenberg 2008; see also Borst 1981 with respect to sugar reward learning) probed for intensity learning using an experimental design intended to force the flies to use intensity information: the authors trained one group of flies such that a low intensity of the odour was paired with shock, whereas a high intensity was presented alone (low–Shock / high); another group of flies was trained as high–Shock / low. Both groups were then tested for their preference between the low and the high intensity. The authors concluded that intensity learning took place based on the difference between the preferences of these two groups: namely, the flies trained with high–Shock / low avoided the high intensity more than the low–Shock / high-trained flies. Such interpretation may, but need not, be misleading. Take the case of BA: using the same testing intensity, a relatively higher intensity at training later allows stronger conditioned avoidance than a relatively lower intensity at training (figure 4d(iii): comparing between the red curve and the pink curve); in other words, the higher training intensity establishes a stronger memory trace. Regarding a differential training design, both reciprocal groups may then avoid the high intensity proportional to the strength of the aversive memory they had formed for the odour during training. Such memory would be stronger when the high intensity is paired with shock than when the low intensity is paired with shock. Thus, the group trained as high–Shock / low would avoid the high intensity more strongly than the group trained as low–Shock / high, although intensity-specific memory may not have been formed. Thus, potentially, such strategy is not actually measuring intensity learning—unless it is shown within the respective experimental series that the chosen odour intensities are learned equally well.

It is worth noting that, after differential training, intensity learning may alternatively be probed for by separately looking at the high versus low preference scores of each reciprocal group (e.g. fig. 5c in Masek & Heisenberg 2008). That is, one can test whether flies that have been trained as low–Shock / high, when given the choice between high and low intensities, avoid the low intensity more strongly in comparison with a baseline situation. But what would be the most reasonable measure of baseline? Should one use the distribution of naive, untreated flies between the high and the low intensities? We note that olfactory behaviour is altered by odour exposure per se, shock exposure per se and handling per se (e.g. Preat 1998; Acevedo et al. 2007; Stephan Knapek 2009, MPI Neurobiology, personal communication; for the situation in larval Drosophila, see Boyle & Cobb 2005; Colomb et al. 2007; Timo Saumweber and Michael Schleyer 2009, Universität Würzburg, personal communication). Thus, we believe that these effects, within each particular experimental series, need to be taken into account when determining ‘baseline’ preferences and that using the preference of naive, untreated flies may be misleading.

(c) Mechanism of intensity learning in Drosophila

Regarding a possible site of an odour intensity memory trace, local interneurons (Ng et al. 2002; Sachse & Galizia 2003; Wilson & Laurent 2005; Shang et al. 2007) or multi-glomerular projection neurons (Lai et al. 2008) appear likely candidates, as they seem to monitor the total antennal lobe activity level. As a short-term memory trace for odour quality is arguably localized to the mushroom bodies (reviewed by Gerber et al. 2004), according to this scenario memory traces for quality and intensity are stored in separate neurons. Alternatively, both kinds of memory trace may be entangled within the mushroom bodies (with the above-mentioned caveats in mind, see Xia & Tully 2007). Typically, only a small fraction of mushroom body Kenyon cells is activated by a given odour (Wang et al. 2004; Turner et al. 2008; but see Voeller 2009); different intensities of an odour might induce sufficiently non-overlapping activity patterns to support intensity-specific memory traces (fig. 3 in Wang et al. 2004; but see Voeller 2009).

It is also tempting to speculate about distinct intracellular mechanisms of quality and intensity learning. In typical differential conditioning experiments, two odours are used without adjusting their intensities for equal learnability; thus, learning scores probably reflect both quality and intensity learning. In such experiments, even null mutations of various ‘learning genes’ typically cause only partial deficits (e.g. Godenschwege et al. 2004; Michels et al. 2005 with respect to the synapsin gene), suggesting that either quality or intensity learning might be differentially impaired (with the above-mentioned caveats in mind, see Xia & Tully 2007; Masek & Heisenberg 2008). Indeed, Masek & Heisenberg (2008) found, using high-low differential training, that the rut2080 mutant is unimpaired in intensity learning while showing the previously reported defect in quality learning; however, given the recent observation of Pan et al. (2009) that in the rut2080 mutants approximately 30 per cent of rut-mRNA remains, the suggestion that intensity learning is independent of rut function is called into question.

To conclude, neither the neuronal nor the molecular mechanisms of intensity coding and intensity learning are yet understood. As an initial step towards filling this gap, we here analyse intensity learning at the behavioural level, using a simple experimental design, which as far as we see is clear of confounds. We find that flies form intensity-specific memories with respect to the odours AM, OCT and MCH; thus, it appears wise to employ these odours in further neurobiological analyses of intensity learning. A fourth odour, BA, on the other hand, seems inappropriate for intensity learning; the neurobiological and ecological reasons for this peculiarity remain to be investigated.

Acknowledgements

This study was supported by the Deutsche Forschungsgemeinschaft via the grants SFB 554/ A10 Arthropode Behaviour, SFB-TR 58/ A6 Fear, Anxiety and Anxiety Disorders and a Heisenberg Fellowship (to B.G.), the International Research Training Group GK 1156 Synaptic and Behavioral Plasticity (to J.H.), as well as by a Boehringer Ingelheim Fonds PhD fellowship (to A.Y.). The repeated discussions with M. Heisenberg, as well as the continuous support of the members of the Würzburg group, especially of K. Oechsener and H. Kaderschabek, are much appreciated. Thanks to B. Michels and T. Saumweber for comments on the manuscript.

References

  • Acevedo S. F., Froudarakis E. I., Tsiorva A. A., Skoulakis E. M. 2007. Distinct neuronal circuits mediate experience-dependent, non-associative osmotactic responses in Drosophila. Mol. Cell. Neurosci. 34, 378–389 (doi:10.1016/j.mcn.2006.11.011) [PubMed]
  • Ache B. W., Young J. M. 2005. Olfaction: diverse species, conserved principles. Neuron 48, 417–430 (doi:10.1016/j.neuron.2005.10.022) [PubMed]
  • Ayer R. K., Carlson J. 1992. Olfactory physiology in the Drosophila antenna and maxillary palp: acj6 distinguishes two classes of odorant pathways. J. Neurobiol. 23, 965–982 (doi:10.1002/neu.480230804) [PubMed]
  • Bargmann C. I. 2006. Comparative chemosensation from receptors to ecology. Nature 444, 295–301 (doi:10.1038/nature05402) [PubMed]
  • Benton R. 2006. On the ORigin of smell: odorant receptors in insects. Cell. Mol. Life Sci. 63, 1579–1585 (doi:10.1007/s00018-006-6130-7) [PubMed]
  • Borst A. 1981. Computation of olfactory signals in Drosophila melanogaster. J. Comp. Physiol. A 152, 373–383 (doi:10.1007/BF00606242)
  • Boyle J., Cobb M. 2005. Olfactory coding in Drosophila larvae investigated by cross-adaptation. J. Exp. Biol. 208, 3483–3491 (doi:10.1242/jeb.01810) [PubMed]
  • Brand A. H., Perrimon N. 1993. Targeted gene expression as a means of altering cell fates and generating dominant phenotypes. Development 118, 401–415 [PubMed]
  • Buck L. B. 1996. Information coding in the vertebrate olfactory system. Annu. Rev. Neurosci. 19, 517–544 (doi:10.1146/annurev.ne.19.030196.002505) [PubMed]
  • Colomb J., Grillenzoni N., Stocker R. F., Ramaekers A. 2007. Complex behavioural changes after odour exposure in Drosophila larvae. Anim. Behav. 73, 587–594 (doi:10.1016/j.anbehav.2006.04.016)
  • Dahanukar A., Hallem E. A., Carlson J. R. 2005. Insect chemoreception. Curr. Opin. Neurobiol. 15, 423–430 (doi:10.1016/j.conb.2005.06.001) [PubMed]
  • DasGupta S., Waddell S. 2008. Learned odor discrimination in Drosophila without combinatorial odor maps in the antennal lobe. Curr. Biol. 18, 1668–1674 (doi:10.1016/j.cub.2008.08.071) [PMC free article] [PubMed]
  • Davis R. L. 2004. Olfactory learning. Neuron 44, 31–48 (doi:10.1016/j.neuron.2004.09.008) [PubMed]
  • Duffy J. B. 2002. GAL4 system in Drosophila: a fly geneticist's Swiss army knife. Genesis 34, 1–15 (doi:10.1002/gene.10150) [PubMed]
  • Fiala A. 2007. Olfaction and olfactory learning in Drosophila: recent progress. Curr. Opin. Neurobiol. 17, 720–726 (doi:10.1016/j.conb.2007.11.009) [PubMed]
  • Galizia C. G., Menzel R. 2000. Odor perception in honeybees: coding information in glomerular patterns. Curr. Opin. Neurobiol. 10, 504–510 (doi:10.1016/S0959-4388(00)00109-4) [PubMed]
  • Gerber B., Tanimoto H., Heisenberg M. 2004. An engram found? Evaluating the evidence from fruit flies. Curr. Opin. Neurobiol. 14, 737–744 (doi:10.1016/j.conb.2004.10.014) [PubMed]
  • Gerber B., Stocker R. F., Tanimura T., Thum A. S. 2009. Smelling, tasting, learning: Drosophila as a study case. Results Probl. Cell Differ 47, 139–185 [PubMed]
  • Godenschwege T. A., et al. 2004. Flies lacking all synapsins are unexpectedly healthy but are impaired in complex behaviour. Eur. J. Neurosci. 20, 611–622 (doi:10.1111/j.1460-9568.2004.03527.x) [PubMed]
  • Hallem E. A., Dahanukar A., Carlson J. R. 2006. Insect odor and taste receptors. Annu. Rev. Entomol. 51, 113–135 (doi:10.1146/annurev.ento.51.051705.113646) [PubMed]
  • Helfand S. L., Carlson J. R. 1989. Isolation and characterization of an olfactory mutant in Drosophila with a chemically specific defect. Proc. Natl Acad. Sci. USA 86, 2908–2912 (doi:10.1073/pnas.86.8.2908) [PubMed]
  • Hildebrand J. C., Shepherd G. M. 1997. Mechanisms of olfactory discrimination: converging evidence for common principles across phyla. Annu. Rev. Neurosci. 20, 595–631 (doi:10.1146/annurev.neuro.20.1.595) [PubMed]
  • Johnson B. A., Leon M. 2007. Chemotopic odorant coding in a mammalian olfactory system. J. Comp. Neurol. 503, 1–34 (doi:10.1002/cne.21396) [PMC free article] [PubMed]
  • Keene A. C., Stratmann M., Keller A., Perrat P. N., Vosshall L. B., Waddell S. 2004. Diverse odor-conditioned memories require uniquely timed dorsal paired medial neuron output. Neuron 44, 521–533 (doi:10.1016/j.neuron.2004.10.006) [PubMed]
  • Korsching S. 2002. Olfactory maps and odor images. Curr. Opin. Neurobiol. 12, 387–392 (doi:10.1016/S0959-4388(02)00348-3) [PubMed]
  • Laberge F., Hara T. J. 2001. Neurobiology of fish olfaction: a review. Brain Res. Rev. 36, 46–59 (doi:10.1016/S0165-0173(01)00064-9) [PubMed]
  • Lai S. L., Awasaki T., Ito K., Lee T. 2008. Clonal analysis of Drosophila antennal lobe neurons: diverse neuronal architectures in the lateral neuroblast lineage. Development 135, 2883–2893 (doi:10.1242/dev.024380) [PubMed]
  • Laurent G., Stopfer M., Friedrich R. W., Rabinovich M. I., Volkovskii A., Abarbanel H. D. 2001. Odor encoding as an active, dynamical process: experiments, computation, and theory. Annu. Rev. Neurosci. 24, 263–297 (doi:10.1146/annurev.neuro.24.1.263) [PubMed]
  • Mainen Z. F. 2006. Behavioral analysis of olfactory coding and computation in rodents. Curr. Opin. Neurobiol. 16, 429–434 (doi:10.1016/j.conb.2006.06.003) [PubMed]
  • Masek P., Heisenberg M. 2008. Distinct memories of odour intensity and quality in Drosophila. Proc. Natl Acad. Sci. USA 105, 15 985–15 990 (doi:10.1073/pnas.0804086105) [PubMed]
  • Michels B., Diegelmann S., Tanimoto H., Schwenkert I., Buchner E., Gerber B. 2005. A role for Synapsin in associative learning: the Drosophila larva as a study case. Learn. Mem. 12, 224–231 (doi:10.1101/lm.92805) [PubMed]
  • Ng M., Roorda R. D., Lima S. Q., Zemelman B. V., Morcillo P., Miesenböck G. 2002. Transmission of olfactory information between three populations of neurons in the antennal lobe of the fly. Neuron 36, 463–474 (doi:10.1016/S0896-6273(02)00975-3) [PubMed]
  • Pan Y., Zhou Y., Guo C., Gong H., Gong Z., Liu L. 2009. Differential roles of the fan-shaped body and the ellipsoid body in Drosophila visual pattern memory. Learn. Mem. 16, 289–295 (doi:10.1101/lm.1331809) [PubMed]
  • Pelz C., Gerber B., Menzel R. 1997. Odorant intensity as a determinant for olfactory conditioning in honeybees: roles in discrimination, overshadowing and memory consolidation. J. Exp. Biol. 200, 837–847 [PubMed]
  • Phelps C. B., Brand A. H. 1998. Ectopic gene expression in Drosophila using GAL4 system. Methods 14, 367–379 (doi:10.1006/meth.1998.0592) [PubMed]
  • Preat T. 1998. Decreased odor avoidance after electric shock in Drosophila mutants biases learning and memory tests. J. Neurosci. 18, 8534–8538 [PubMed]
  • Sachse S., Galizia G. 2003. The coding of odour-intensity in the honeybee antennal lobe: local computation optimizes odour representation. Eur. J. Neurosci. 18, 2119–2132 (doi:10.1046/j.1460-9568.2003.02931.x) [PubMed]
  • Schwaerzel M., Monastirioti M., Scholz H., Friggi-Grelin F., Birman S., Heisenberg M. 2003. Dopamine and octopamine differentiate between aversive and appetitive olfactory memories in Drosophila. J. Neurosci. 23, 10 495–10 502 [PubMed]
  • Shang Y., Claridge-Chang A., Sjulson L., Pypaert M., Miesenböck G. 2007. Excitatory local circuits and their implications for olfactory processing in the fly antennal lobe. Cell 128, 601–612 (doi:10.1016/j.cell.2006.12.034) [PMC free article] [PubMed]
  • Strausfeld N. J., Hildebrand J. G. 1999. Olfactory systems: common design, uncommon origins? Curr. Opin. Neurobiol. 9, 634–639 (doi:10.1016/S0959-4388(99)00019-7) [PubMed]
  • Tully T., Quinn W. G. 1985. Classical conditioning and retention in normal and mutant Drosophila melanogaster. J. Comp. Physiol. A 157, 263–277 (doi:10.1007/BF01350033) [PubMed]
  • Turner G., Bazhenov M., Laurent G. 2008. Olfactory representations by mushroom body neurons. J. Neurophysiol. 99, 734–746 (doi:10.1152/jn.01283.2007) [PubMed]
  • Voeller T. 2009. Visualisierung und Manipulation neuronaler Aktivitäten im Gehirn von Drosophila melanogaster. PhD thesis, Universität Würzburg, Würzburg, Germany
  • Vosshall L. B. 2000. Olfaction in Drosophila. Curr. Opin. Neurobiol. 10, 498–503 (doi:10.1016/S0959-4388(00)00111-2) [PubMed]
  • Vosshall L. B., Stocker R. F. 2007. Molecular architecture of smell and taste in Drosophila. Annu. Rev. Neurosci. 30, 505–533 (doi:10.1146/annurev.neuro.30.051606.094306) [PubMed]
  • Wang Y., Guo H. -F., Pologruto T. A., Hannan F., Hakker I., Svoboda K., Zhong Y. 2004. Stereotyped odor-evoked activity in the mushroom body of Drosophila revealed by green fluorescence protein-based Ca2+ imaging. J. Neurosci. 24, 6507–6514 (doi:10.1523/JNEUROSCI.3727-03.2004) [PubMed]
  • Wilson R., Laurent G. 2005. Role of GABAergic inhibition in shaping odor-evoked spatiotemporal patterns in the Drosophila antennal lobe. J. Neurosci. 25, 9069–9079 (doi:10.1523/JNEUROSCI.2070-05.2005) [PubMed]
  • Wright G. A., Thomson M. G., Smith B. H. 2005. Odour concentration affects odour identity in honeybees. Proc. Biol. Sci. 272, 2417–2422 (doi:10.1098/rspb.2005.3252) [PMC free article] [PubMed]
  • Xia S., Tully T. 2007. Segregation of odor identity and intensity during odor discrimination in Drosophila mushroom body. PLOS Biol. 5, 2398–2407 (doi:10.1371/journal.pbio.0050264) [PMC free article] [PubMed]
  • Yarali A., Mayerle M., Nawroth C., Gerber B. 2008. No evidence for visual context-dependency of olfactory learning in Drosophila. Naturwissenschaften 95, 767–774 (doi:10.1007/s00114-008-0380-1) [PMC free article] [PubMed]

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