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• W2431792021 abstract "•Olfactory behavior is predicted by superposition of normalized glomerular activity•Behavioral/neuronal responses to binary mixtures scale linearly with mixing ratio•Manipulation of glomerular activity biases behavior according to the decoding model•Relative valence of odors changes and even switches depending on the context Odor information is encoded in the activity of a population of glomeruli in the primary olfactory center. However, how this information is decoded in the brain remains elusive. Here, we address this question in Drosophila by combining neuronal imaging and tracking of innate behavioral responses. We find that the behavior is accurately predicted by a model summing normalized glomerular responses, in which each glomerulus contributes a specific, small amount to odor preference. This model is further supported by targeted manipulations of glomerular input, which biased the behavior. Additionally, we observe that relative odor preference changes and can even switch depending on the context, an effect correctly predicted by our normalization model. Our results indicate that olfactory information is decoded from the pooled activity of a glomerular repertoire and demonstrate the ability of the olfactory system to adapt to the statistics of its environment. Odor information is encoded in the activity of a population of glomeruli in the primary olfactory center. However, how this information is decoded in the brain remains elusive. Here, we address this question in Drosophila by combining neuronal imaging and tracking of innate behavioral responses. We find that the behavior is accurately predicted by a model summing normalized glomerular responses, in which each glomerulus contributes a specific, small amount to odor preference. This model is further supported by targeted manipulations of glomerular input, which biased the behavior. Additionally, we observe that relative odor preference changes and can even switch depending on the context, an effect correctly predicted by our normalization model. Our results indicate that olfactory information is decoded from the pooled activity of a glomerular repertoire and demonstrate the ability of the olfactory system to adapt to the statistics of its environment. A prominent feature of the olfactory system is its widely distributed code. Even monomolecular odorants typically bind to multiple types of olfactory receptors (Malnic et al., 1999Malnic B. Hirono J. Sato T. Buck L.B. Combinatorial receptor codes for odors.Cell. 1999; 96: 713-723Abstract Full Text Full Text PDF PubMed Scopus (1696) Google Scholar), recruiting multiple postsynaptic glomeruli without apparent spatial or chemotopic organization. Different odorants recruit different sets of glomeruli (Friedrich and Korsching, 1997Friedrich R.W. Korsching S.I. Combinatorial and chemotopic odorant coding in the zebrafish olfactory bulb visualized by optical imaging.Neuron. 1997; 18: 737-752Abstract Full Text Full Text PDF PubMed Scopus (476) Google Scholar, Hallem and Carlson, 2006Hallem E.A. Carlson J.R. Coding of odors by a receptor repertoire.Cell. 2006; 125: 143-160Abstract Full Text Full Text PDF PubMed Scopus (835) Google Scholar, Joerges et al., 1997Joerges J. Kuttner A. Galizia C.G. Menzel R. Representations of odours and odour mixtures visualized in the honeybee brain.Nature. 1997; 387: 285-288Crossref Scopus (368) Google Scholar, Ng et al., 2002Ng M. Roorda R.D. Lima S.Q. Zemelman B.V. Morcillo P. Miesenböck G. Transmission of olfactory information between three populations of neurons in the antennal lobe of the fly.Neuron. 2002; 36: 463-474Abstract Full Text Full Text PDF PubMed Scopus (375) Google Scholar, Rubin and Katz, 1999Rubin B.D. Katz L.C. Optical imaging of odorant representations in the mammalian olfactory bulb.Neuron. 1999; 23: 499-511Abstract Full Text Full Text PDF PubMed Scopus (547) Google Scholar, Uchida et al., 2000Uchida N. Takahashi Y.K. Tanifuji M. Mori K. Odor maps in the mammalian olfactory bulb: domain organization and odorant structural features.Nat. Neurosci. 2000; 3: 1035-1043Crossref PubMed Scopus (413) Google Scholar, Wang et al., 2003Wang J.W. Wong A.M. Flores J. Vosshall L.B. Axel R. Two-photon calcium imaging reveals an odor-evoked map of activity in the fly brain.Cell. 2003; 112: 271-282Abstract Full Text Full Text PDF PubMed Scopus (638) Google Scholar) with distinct temporal dynamics (Cury and Uchida, 2010Cury K.M. Uchida N. Robust odor coding via inhalation-coupled transient activity in the mammalian olfactory bulb.Neuron. 2010; 68: 570-585Abstract Full Text Full Text PDF PubMed Scopus (209) Google Scholar, Friedrich and Laurent, 2001Friedrich R.W. Laurent G. Dynamic optimization of odor representations by slow temporal patterning of mitral cell activity.Science. 2001; 291: 889-894Crossref PubMed Scopus (362) Google Scholar, Spors and Grinvald, 2002Spors H. Grinvald A. Spatio-temporal dynamics of odor representations in the mammalian olfactory bulb.Neuron. 2002; 34: 301-315Abstract Full Text Full Text PDF PubMed Scopus (271) Google Scholar, Wehr and Laurent, 1996Wehr M. Laurent G. Odour encoding by temporal sequences of firing in oscillating neural assemblies.Nature. 1996; 384: 162-166Crossref PubMed Scopus (412) Google Scholar), and most glomeruli respond to a wide array of odors. These observations demonstrate that in a range of organisms, including insects, fish, and mammals, early olfactory information is represented as spatio-temporal patterns of glomerular activity. How these activity patterns are decoded by the brain to guide odor-evoked behavior, however, remains largely unknown. Olfactory research in Drosophila melanogaster has provided elements of answer to this question. Using behavioral genetics, attraction, and aversion to specific odors have been linked to the activation of one or a few glomeruli in the antennal lobe (AL) (Ai et al., 2010Ai M. Min S. Grosjean Y. Leblanc C. Bell R. Benton R. Suh G.S.B. Acid sensing by the Drosophila olfactory system.Nature. 2010; 468: 691-695Crossref PubMed Scopus (229) Google Scholar, Dweck et al., 2013Dweck H.K. Ebrahim S.A. Kromann S. Bown D. Hillbur Y. Sachse S. Hansson B.S. Stensmyr M.C. Olfactory preference for egg laying on citrus substrates in Drosophila.Curr. Biol. 2013; 23: 2472-2480Abstract Full Text Full Text PDF PubMed Scopus (159) Google Scholar, Min et al., 2013Min S. Ai M. Shin S.A. Suh G.S.B. Dedicated olfactory neurons mediating attraction behavior to ammonia and amines in Drosophila.Proc. Natl. Acad. Sci. USA. 2013; 110: E1321-E1329Crossref PubMed Scopus (112) Google Scholar, Ronderos et al., 2014Ronderos D.S. Lin C.C. Potter C.J. Smith D.P. Farnesol-detecting olfactory neurons in Drosophila.J. Neurosci. 2014; 34: 3959-3968Crossref PubMed Scopus (74) Google Scholar, Schlief and Wilson, 2007Schlief M.L. Wilson R.I. Olfactory processing and behavior downstream from highly selective receptor neurons.Nat. Neurosci. 2007; 10: 623-630Crossref PubMed Scopus (114) Google Scholar, Semmelhack and Wang, 2009Semmelhack J.L. Wang J.W. Select Drosophila glomeruli mediate innate olfactory attraction and aversion.Nature. 2009; 459: 218-223Crossref PubMed Scopus (236) Google Scholar, Stensmyr et al., 2012Stensmyr M.C. Dweck H.K.M. Farhan A. Ibba I. Strutz A. Mukunda L. Linz J. Grabe V. Steck K. Lavista-Llanos S. et al.A conserved dedicated olfactory circuit for detecting harmful microbes in Drosophila.Cell. 2012; 151: 1345-1357Abstract Full Text Full Text PDF PubMed Scopus (381) Google Scholar, Suh et al., 2004Suh G.S. Wong A.M. Hergarden A.C. Wang J.W. Simon A.F. Benzer S. Axel R. Anderson D.J. A single population of olfactory sensory neurons mediates an innate avoidance behaviour in Drosophila.Nature. 2004; 431: 854-859Crossref PubMed Scopus (412) Google Scholar). These observations suggest a labeled-line coding strategy, in which individual glomeruli convey signals of specific ethological relevance for the animal and their activation triggers the execution of hard-wired behavioral programs. However, it remains uncertain whether this is a property of a specialized subset of glomeruli or constitutes a general coding principle in the AL. More importantly, how compound signals from multiple glomeruli are integrated to determine the valence of odors, including mixtures of odors commonly found in natural environments, is poorly understood (Kreher et al., 2008Kreher S.A. Mathew D. Kim J. Carlson J.R. Translation of sensory input into behavioral output via an olfactory system.Neuron. 2008; 59: 110-124Abstract Full Text Full Text PDF PubMed Scopus (182) Google Scholar). Another open question is the coding beyond absolute odor valence. Flies, for example, choose the less aversive odor when forced to make a choice between two inherently aversive stimuli (Tully and Quinn, 1985Tully T. Quinn W.G. Classical conditioning and retention in normal and mutant Drosophila melanogaster.J. Comp. Physiol. A Neuroethol. Sens. Neural Behav. Physiol. 1985; 157: 263-277Crossref PubMed Scopus (898) Google Scholar). Moreover, studies in humans have shown that the evaluation of relative odor valence changes depending on the context (Clepce et al., 2014Clepce M. Neumann K. Martus P. Nitsch M. Wielopolski J. Koch A. Kornhuber J. Reich K. Thuerauf N. The psychophysical assessment of odor valence: does an anchor stimulus influence the hedonic evaluation of odors?.Chem. Senses. 2014; 39: 17-25Crossref PubMed Scopus (3) Google Scholar). How relative valence is computed in the brain and how it is modulated according to the context remains elusive, although a recent study showed that divisive normalization can explain context-dependent choice behavior in primates (Louie et al., 2013Louie K. Khaw M.W. Glimcher P.W. Normalization is a general neural mechanism for context-dependent decision making.Proc. Natl. Acad. Sci. USA. 2013; 110: 6139-6144Crossref PubMed Scopus (182) Google Scholar). Here, we used a combination of optical imaging and behavioral techniques to address these questions systematically. We used two-photon imaging to monitor Ca2+ signals in the whole AL in response to a diverse set of odors. Comparing these data with odor-evoked behavioral responses allowed us to formulate a decoding model describing how innate olfactory behavior is dictated by the population of projection neurons (PNs), the AL output neurons, in a quantitative manner. We found that a weighted sum of normalized PN responses can recapitulate observed behavior and predict responses to novel odors, including mixtures and odors at different concentrations. The valence assigned to individual glomeruli in our analysis is consistent with previous findings; however, the contribution of individual glomeruli to the behavioral output is small, indicating that odor valence is not dominated by a few privileged glomeruli but depends on pooling small contributions over a large number of glomeruli. This conclusion is further supported by genetic silencing and optogenetic activation of individual olfactory receptor neuron (ORN) types, which evoked modest biases in behavior in accordance with the model predictions. Strikingly, behavioral responses were altered when the same odors were tested in different olfactory contexts. Our decoding model captured this adaptive behavior and correctly predicted that the relative preference of pairs of odors could switch depending on the olfactory context. These results highlight the ability of the olfactory system to adapt to the statistics of its olfactory environment, similarly to the visual and auditory systems (Kohn, 2007Kohn A. Visual adaptation: physiology, mechanisms, and functional benefits.J. Neurophysiol. 2007; 97: 3155-3164Crossref PubMed Scopus (536) Google Scholar, Wark et al., 2007Wark B. Lundstrom B.N. Fairhall A. Sensory adaptation.Curr. Opin. Neurobiol. 2007; 17: 423-429Crossref PubMed Scopus (352) Google Scholar). We first sought to characterize odor-evoked behavioral responses by monitoring the behavior of tethered flies in a flight-simulator arena. The arena was equipped with a closed-loop feedback system that allowed animals to virtually adjust their heading direction by modulating the amplitudes of their left and right wing beats (Figures 1A and S1). A change in heading direction was accompanied by a corresponding rotation of the visual and olfactory landscapes, defining a virtual-reality-like environment for the animal to navigate. Olfactory stimuli were delivered in the form of a plume, spanning 45° in azimuth, which flies were free to exit or re-enter by adjusting their heading direction (Figure 1B). The valence of olfactory stimuli in this setup could thus be assessed by monitoring the tendency to navigate in or out of the odor plume. We initially characterized odor-evoked behavior in this assay using a set of six odorants, which we expected on the basis of prior reports (Knaden et al., 2012Knaden M. Strutz A. Ahsan J. Sachse S. Hansson B.S. Spatial representation of odorant valence in an insect brain.Cell Rep. 2012; 1: 392-399Abstract Full Text Full Text PDF PubMed Scopus (132) Google Scholar, Semmelhack and Wang, 2009Semmelhack J.L. Wang J.W. Select Drosophila glomeruli mediate innate olfactory attraction and aversion.Nature. 2009; 459: 218-223Crossref PubMed Scopus (236) Google Scholar) and preliminary experiments to be either strongly attractive or aversive, and three control stimuli (mineral oil, water, and air). Flies often exited aversive odors by producing a fast saccadic turn (Figure 1C). Similar avoidance behavior was not observed in response to air, for which trajectories only showed occasional low-amplitude saccades. Attractive odors, on the other hand, evoked a reduction in turning rate and a tendency to navigate in the odor for a longer time. To quantify these responses, we defined a measure of olfactory preference termed valence index (VI) as the proportion of time spent outside the odor plume. When computed relative to the time of odor application, instantaneous VIs increased monotonically and showed consistent trends extending beyond the period of odor application (Figure 1D). Given that attractive and aversive VIs were best separated near the end of the odor application period, we scored each odor using the mean VI value in the last 1 s of odor application (Figure 1E; our results are largely independent of the choice of the scoring period, see Figure S1). Of the six tested odors, two attractive and three aversive odors evoked significant responses compared with air (Figure 1E). Solvents also evoked mildly attractive responses. Attraction to humidified air has been documented and is strongly reduced by the mutation of Orco (Lin et al., 2015Lin C.C. Prokop-Prigge K.A. Preti G. Potter C.J. Food odors trigger Drosophila males to deposit a pheromone that guides aggregation and female oviposition decisions.eLife. 2015; 4: e08688Google Scholar). Attraction to mineral oil also likely depends on the olfactory system, as mineral oil evoked sparse but significant glomerular activity in our measurements (see Figure 2D), and blocking synaptic transmission in Orco-dependent ORNs evoked a small increase in VI (Figure S2). To further examine responses to diverse odors, we screened odor-evoked responses to a panel of 84 odors, consisting of 36 pure odorants, 4 of which were sampled at 4 different concentrations, and 36 binary mixtures of attractive and aversive odors at various ratios (Table S1). Odors were sampled in sets of 6 and delivered sequentially in each experiment. To verify that the baseline behavior was consistent across different odor sets, we included mineral oil in every experiment. VI values for the 36 pure odorants showed a nearly continuous array of responses (Figure 1F). After correcting for multiple comparisons, 3 odorants were significantly more attractive, and 13 were more aversive, than mineral oil. When compared to air, odors were almost symmetrically split into two sides (Figure 1F). These results demonstrate that flies exhibit both attraction and aversion in response to a variety of odors in our assay. Inspection of individual flight trajectories (Figure 1C) revealed that escape responses to aversive stimuli can be remarkably fast. Because flight orientation is controlled by the relative amplitude of wing beats, we computed the absolute difference between the left and the right wing-beat amplitudes (ΔWBA). As expected, air did not evoke noticeable changes in ΔWBA (Figure 1G). In contrast, aversive odors evoked a sharp, transient increase in ΔWBA, whereas attractive odors evoked a slower, sustained decrease. VI values were tightly correlated with the cumulative ΔWBA (Figure 1G, inset), confirming that our behavioral measure reflects an active, stimulus-evoked modulation of the turning rate. To estimate the response latency, we fitted ΔWBA for an aversive odor with a sum of exponentials (Figure 1G). The fitted curve deflected (10% rise time) 200 ms after the first contact with the odor, indicating that the time required to perceive, process, and respond to an aversive stimulus is shorter than 200 ms. We next examined PN activity in response to the same panel of odors under identical odor delivery conditions. In the fly AL, inputs from ORNs are segregated into ∼50 glomeruli, all of which are identifiable across individuals (Couto et al., 2005Couto A. Alenius M. Dickson B.J. Molecular, anatomical, and functional organization of the Drosophila olfactory system.Curr. Biol. 2005; 15: 1535-1547Abstract Full Text Full Text PDF PubMed Scopus (671) Google Scholar, Fishilevich and Vosshall, 2005Fishilevich E. Vosshall L.B. Genetic and functional subdivision of the Drosophila antennal lobe.Curr. Biol. 2005; 15: 1548-1553Abstract Full Text Full Text PDF PubMed Scopus (436) Google Scholar, Vosshall et al., 2000Vosshall L.B. Wong A.M. Axel R. An olfactory sensory map in the fly brain.Cell. 2000; 102: 147-159Abstract Full Text Full Text PDF PubMed Scopus (813) Google Scholar). Because PNs in the same glomerulus transmit highly correlated information (Kazama and Wilson, 2009Kazama H. Wilson R.I. Origins of correlated activity in an olfactory circuit.Nat. Neurosci. 2009; 12: 1136-1144Crossref PubMed Scopus (100) Google Scholar), each glomerulus can be considered as a unit of output. Therefore, the entire olfactory information transmitted from the AL can be obtained by monitoring PN activity in a mere ∼50 dimensional space. To achieve this, we expressed the calcium sensor GCaMP6f (Chen et al., 2013Chen T.-W. Wardill T.J. Sun Y. Pulver S.R. Renninger S.L. Baohan A. Schreiter E.R. Kerr R.A. Orger M.B. Jayaraman V. et al.Ultrasensitive fluorescent proteins for imaging neuronal activity.Nature. 2013; 499: 295-300Crossref PubMed Scopus (3618) Google Scholar) under the control of NP225-Gal4 (Tanaka et al., 2012Tanaka N.K. Endo K. Ito K. Organization of antennal lobe-associated neurons in adult Drosophila melanogaster brain.J. Comp. Neurol. 2012; 520: 4067-4130Crossref PubMed Scopus (103) Google Scholar, Thum et al., 2007Thum A.S. Jenett A. Ito K. Heisenberg M. Tanimoto H. Multiple memory traces for olfactory reward learning in Drosophila.J. Neurosci. 2007; 27: 11132-11138Crossref PubMed Scopus (96) Google Scholar), which labels 37 glomeruli with high specificity (Figure 2A). GCaMP fluorescence in the entire AL was imaged at ∼1.7 Hz using two-photon microscopy in response to the 84 odors in our dataset. To account for brain movement during recordings and variability in AL morphology across animals, we registered all images to a template AL (Figure 2B), and the fluorescence in each glomerulus was extracted by averaging over the spatial extent of the corresponding glomerulus in the template (Figure S3). Consistent with previous reports, glomeruli exhibited various tuning breadths: a glomerulus responded on average to 22% ± 16% (mean ± SD) of all tested odors. Similarly, odors recruited on average 8 ± 5 of the 37 glomeruli (Figures 2C and 2D). The rapidity of aversive responses suggests that animals rely on the instantaneous, spatial dimension of PN responses to make perceptual decisions. This requires that a single “snapshot” of the PN response contain sufficient information to categorize odorants. To verify this, we trained a linear classifier to predict odor identity based on PN responses in a single imaging frame. Because each odor was presented four times in each animal, we used data from three trials to train the classifier and the remaining trial to test classification performance. As expected, classification accuracy was close to chance level before odor application, and increased shortly after odor onset (∼150 ms after odor contact; Figure 2E), peaking near 65% over time (∼1.3–3.7 s after odor contact). It is worth noting that the onset of this increase in classification accuracy matches well with the onset of the aversive response. Performance decayed after odor offset; however, it remained well above chance level throughout the recording period (13 s beyond odor offset), suggesting that residual PN responses carry a significant amount of information about past stimuli. Comparing behavioral and imaging data showed that PN responses in a number of glomeruli exhibited significant linear correlations with VI (Figure S4), suggesting some degree of linearity in the transformation from PN responses to behavior. The total AL activity, however, only correlated weakly with behavior (Figure S4), indicating that the intensity of AL activation is not the sole determinant of odor valence. To examine the relationship in more detail, we focused on mixture data and asked whether behavioral and physiological responses vary in proportion to the mixing ratio of the components. VI scaled linearly with the mixing ratio (Figure 3A). This trend was paralleled in PN activity: linearly regressing the vectors of PN responses on the mixing ratio lead to a very accurate description of PN data (R2 of fit 0.6–0.97; Figure 3B). These results suggest a linear relationship between PN responses and behavior. Linear models are widely used to examine the relationships between neuronal activity and behavior. In larval Drosophila, a linear model based on the activity of ORNs was successful in predicting behavioral responses to a panel of 26 odorants (Kreher et al., 2008Kreher S.A. Mathew D. Kim J. Carlson J.R. Translation of sensory input into behavioral output via an olfactory system.Neuron. 2008; 59: 110-124Abstract Full Text Full Text PDF PubMed Scopus (182) Google Scholar). To test whether this framework could explain our data quantitatively, we constructed a decoding model (referred to as linear model) in which the VI is calculated as a weighted sum of glomerular activity (Figure 4A). We fit the model parameters using regularized linear regression (see Experimental Procedures) with training data, and examined its predictive power with test data not used in the fitting procedure. Prediction performance was evaluated using the R2 coefficient (see Experimental Procedures). Unexpectedly, we found that while the model could accurately describe the training data, it performed poorly at predicting the test data (Figure 4B). We reasoned that a possible cause for the failure of the linear model is that odorants were delivered in sets of six in our experiments: the response to one of the odors in the set may be affected by the experience of the other five odors, for example, through sensory adaptation. To test this, we constructed a second decoding model (referred to as normalization model) in which glomerular outputs were passed through a nonlinear normalization step before the summation (Figure 4A). Normalization is meant to represent adaptation to the statistics of the olfactory environment and was minimally modeled using a mean subtraction followed by a rescaling by the SD of each glomerulus’ response to the odor set, both of which commonly describe neuronal adaptation (Baccus and Meister, 2002Baccus S.A. Meister M. Fast and slow contrast adaptation in retinal circuitry.Neuron. 2002; 36: 909-919Abstract Full Text Full Text PDF PubMed Scopus (383) Google Scholar, Chander and Chichilnisky, 2001Chander D. Chichilnisky E.J. Adaptation to temporal contrast in primate and salamander retina.J. Neurosci. 2001; 21: 9904-9916PubMed Google Scholar, Nagel and Doupe, 2006Nagel K.I. Doupe A.J. Temporal processing and adaptation in the songbird auditory forebrain.Neuron. 2006; 51: 845-859Abstract Full Text Full Text PDF PubMed Scopus (131) Google Scholar, Ohzawa et al., 1985Ohzawa I. Sclar G. Freeman R.D. Contrast gain control in the cat’s visual system.J. Neurophysiol. 1985; 54: 651-667PubMed Google Scholar). Importantly, this form of normalization is compatible with the linearity observed in response to mixtures. We fit the model parameters using the same procedure as for the linear model. Notably, we found that the normalization model not only accurately described the training data, but also yielded accurate predictions of the test data (Figure 4B). This prediction performance was highly significant (permutation test, p < 0.002), suggesting that the normalization operation renders the relationship between the predictors (glomerular activation) and the dependent variable (VI) more linear. We next tested how the performance varied with the number of glomeruli included in the list of predictors. Because sampling all possible subsets of 37 glomeruli would be computationally prohibitive, we tested instead a multitude (742,700) of randomly chosen subsets. The average prediction performance increased monotonically with the number of glomeruli (Figure 4C, top graph). Importantly, even when restricted to only the best subset of each size (Figure 4C, bottom graph; see Supplemental Experimental Procedures for definition of best subsets), the performance continuously increased up to intermediate sizes (∼20 glomeruli) before slight overfitting occurred. These results suggest that the majority of glomeruli carry information about the dependent variable. A final model was constructed by averaging the sets of glomerulus weights obtained in the cross-validation procedure. This resulted in a balanced number of 18 attractive and 19 aversive glomeruli (Figure 4D). We confirmed that the model accurately explained the responses to pure odors and mixtures (Figure 4E), and further challenged the model with an additional set of test data consisting of four odors at three different concentrations. Again, while the linear model failed to predict these data quantitatively, the normalization model gave accurate predictions (Figure 4E). These results demonstrate strong correlation, but not causality, between glomerular response and behavioral output. To test the causal relationship, it is necessary to perturb glomerular activity and assess the impact of the manipulation on the behavior. To achieve this in a highly glomerulus-specific manner, we genetically silenced synaptic input to glomeruli by expressing tetanus toxin (TNT), a potent blocker of vesicular release (Sweeney et al., 1995Sweeney S.T. Broadie K. Keane J. Niemann H. O’Kane C.J. Targeted expression of tetanus toxin light chain in Drosophila specifically eliminates synaptic transmission and causes behavioral defects.Neuron. 1995; 14: 341-351Abstract Full Text PDF PubMed Scopus (667) Google Scholar), in ORNs using Or-Gal4 drivers, which drive virtually no expression outside the target ORNs (Couto et al., 2005Couto A. Alenius M. Dickson B.J. Molecular, anatomical, and functional organization of the Drosophila olfactory system.Curr. Biol. 2005; 15: 1535-1547Abstract Full Text Full Text PDF PubMed Scopus (671) Google Scholar, Fishilevich and Vosshall, 2005Fishilevich E. Vosshall L.B. Genetic and functional subdivision of the Drosophila antennal lobe.Curr. Biol. 2005; 15: 1548-1553Abstract Full Text Full Text PDF PubMed Scopus (436) Google Scholar). We targeted five different glomeruli: DM1, DM4, DM5, DC3, and VC1. These particular glomeruli were chosen in part on the basis of their established attractive or aversive role, and partly based on the predictions of our model. In an attempt to evoke larger effects, we tested two lines in which TNT was co-expressed in three different ORN types. A first set (glomerulus DM5, DC3, and VC1) was chosen to evoke changes toward more attractive VI values and a second set (glomerulus DM4, VA2, and VA4) was selected to induce more aversive responses. A number of results supported the conclusion that behavior is determined by the summed activity of many glomeruli. First, in accordance with the prediction that blocking input to a small number of glomeruli should have limited behavioral effect, VIs in flies expressing TNT in one or three glomeruli were not substantially different from those in the control flies (Figure 5A). Notably, blockade of synaptic transmission in DM1 or DM5 ORNs, which abolished attraction or aversion to different concentrations of apple cider vinegar in walking flies (Semmelhack and Wang, 2009Semmelhack J.L. Wang J.W. Select Drosophila glomeruli mediate innate olfactory attraction and aversion.Nature. 2009; 459: 218-223Crossref PubMed Scopus (236) Google Scholar), did not remove innate responses to this as well as other odors in our experiments (Figure 5A). To rule out the possibility of an incomplete effect of TNT, we expressed TNT in either DM1 or DM5 ORNs while expressing GCaMP3 (Tian et al., 2009Tian L. 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• W2431792021 created "2016-06-24" @default.
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• W2431792021 date "2016-07-01" @default.
• W2431792021 modified "2023-10-12" @default.
• W2431792021 title "Decoding of Context-Dependent Olfactory Behavior in Drosophila" @default.
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• W2431792021 doi "https://doi.org/10.1016/j.neuron.2016.05.022" @default.
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