Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Stereotyped connectivity and computations in higher-order olfactory neurons



In the first brain relay of the olfactory system, odors are encoded by combinations of glomeruli, but it is not known how glomerular signals are ultimately integrated. In Drosophila melanogaster, the majority of glomerular projections target the lateral horn. Here we show that lateral horn neurons (LHNs) receive input from sparse and stereotyped combinations of glomeruli that are coactivated by odors, and certain combinations of glomeruli are over-represented. One morphological LHN type is broadly tuned and sums input from multiple glomeruli. These neurons have a broader dynamic range than their individual glomerular inputs do. By contrast, a second morphological type is narrowly tuned and receives prominent odor-selective inhibition through both direct and indirect pathways. We show that this wiring scheme confers increased selectivity. The biased stereotyped connectivity of the lateral horn contrasts with the probabilistic wiring of the mushroom body, reflecting the distinct roles of these regions in innate as compared to learned behaviors.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Prices vary by article type



Prices may be subject to local taxes which are calculated during checkout

Figure 1: Morphological types of lateral horn projection neurons.
Figure 2: Odor selectivity in type I and type II neurons.
Figure 3: Paired recordings identify convergent glomerular inputs.
Figure 4: Summing excitatory input from multiple glomeruli.
Figure 5: LHN odor responses are sensitive to single glomeruli.
Figure 6: Paired recordings identify excitation from one glomerulus.
Figure 7: Odor-selective inhibition gates excitation from glomerulus DP1m.
Figure 8: Circuit origin of inhibition.


  1. Bargmann, C.I. Comparative chemosensation from receptors to ecology. Nature 444, 295–301 (2006).

    Article  CAS  Google Scholar 

  2. Vosshall, L.B. & Stocker, R.F. Molecular architecture of smell and taste in Drosophila. Annu. Rev. Neurosci. 30, 505–533 (2007).

    Article  CAS  Google Scholar 

  3. Masse, N.Y., Turner, G.C. & Jefferis, G.S. Olfactory information processing in Drosophila. Curr. Biol. 19, R700–R713 (2009).

    Article  CAS  PubMed Central  Google Scholar 

  4. Luo, S.X., Axel, R. & Abbott, L.F. Generating sparse and selective third-order responses in the olfactory system of the fly. Proc. Natl. Acad. Sci. USA 107, 10713–10718 (2010).

    Article  CAS  Google Scholar 

  5. Miyamichi, K. et al. Cortical representations of olfactory input by trans-synaptic tracing. Nature 472, 191–196 (2011).

    Article  CAS  Google Scholar 

  6. Sosulski, D.L., Bloom, M.L., Cutforth, T., Axel, R. & Datta, S.R. Distinct representations of olfactory information in different cortical centres. Nature 472, 213–216 (2011).

    Article  CAS  PubMed Central  Google Scholar 

  7. de Belle, J.S. & Heisenberg, M. Associative odor learning in Drosophila abolished by chemical ablation of mushroom bodies. Science 263, 692–695 (1994).

    Article  CAS  Google Scholar 

  8. Connolly, J.B. et al. Associative learning disrupted by impaired Gs signaling in Drosophila mushroom bodies. Science 274, 2104–2107 (1996).

    Article  CAS  Google Scholar 

  9. Stocker, R.F. The organization of the chemosensory system in Drosophila melanogaster: a review. Cell Tissue Res. 275, 3–26 (1994).

    Article  CAS  Google Scholar 

  10. Tanaka, N.K., Endo, K. & Ito, K. Organization of antennal lobe-associated neurons in adult Drosophila melanogaster brain. J. Comp. Neurol. 520, 4067–4130 (2012).

    Article  Google Scholar 

  11. Marin, E.C., Jefferis, G.S., Komiyama, T., Zhu, H. & Luo, L. Representation of the glomerular olfactory map in the Drosophila brain. Cell 109, 243–255 (2002).

    Article  CAS  Google Scholar 

  12. Wong, A.M., Wang, J.W. & Axel, R. Spatial representation of the glomerular map in the Drosophila protocerebrum. Cell 109, 229–241 (2002).

    Article  CAS  Google Scholar 

  13. Jefferis, G.S. et al. Comprehensive maps of Drosophila higher olfactory centers: spatially segregated fruit and pheromone representation. Cell 128, 1187–1203 (2007).

    Article  CAS  PubMed Central  Google Scholar 

  14. Ruta, V. et al. A dimorphic pheromone circuit in Drosophila from sensory input to descending output. Nature 468, 686–690 (2010).

    Article  CAS  Google Scholar 

  15. Gupta, N. & Stopfer, M. Functional analysis of a higher olfactory center, the lateral horn. J. Neurosci. 32, 8138–8148 (2012).

    Article  CAS  PubMed Central  Google Scholar 

  16. Masuda-Nakagawa, L.M., Tanaka, N.K. & O'Kane, C.J. Stereotypic and random patterns of connectivity in the larval mushroom body calyx of Drosophila. Proc. Natl. Acad. Sci. USA 102, 19027–19032 (2005).

    Article  CAS  Google Scholar 

  17. Caron, S.J., Ruta, V., Abbott, L.F. & Axel, R. Random convergence of olfactory inputs in the Drosophila mushroom body. Nature 497, 113–117 (2013).

    Article  CAS  PubMed Central  Google Scholar 

  18. Tanaka, N.K., Awasaki, T., Shimada, T. & Ito, K. Integration of chemosensory pathways in the Drosophila second-order olfactory centers. Curr. Biol. 14, 449–457 (2004).

    Article  CAS  Google Scholar 

  19. Gruntman, E. & Turner, G.C. Integration of the olfactory code across dendritic claws of single mushroom body neurons. Nat. Neurosci. 16, 1821–1829 (2013).

    Article  CAS  PubMed Central  Google Scholar 

  20. Murthy, M., Fiete, I. & Laurent, G. Testing odor response stereotypy in the Drosophila mushroom body. Neuron 59, 1009–1023 (2008).

    Article  CAS  PubMed Central  Google Scholar 

  21. Pfeiffer, B.D. et al. Tools for neuroanatomy and neurogenetics in Drosophila. Proc. Natl. Acad. Sci. USA 105, 9715–9720 (2008).

    Article  CAS  Google Scholar 

  22. de Bruyne, M., Foster, K. & Carlson, J.R. Odor coding in the Drosophila antenna. Neuron 30, 537–552 (2001).

    Article  CAS  Google Scholar 

  23. Hallem, E.A. & Carlson, J.R. Coding of odors by a receptor repertoire. Cell 125, 143–160 (2006).

    Article  CAS  PubMed Central  Google Scholar 

  24. Larsson, M.C. et al. Or83b encodes a broadly expressed odorant receptor essential for Drosophila olfaction. Neuron 43, 703–714 (2004).

    Article  CAS  Google Scholar 

  25. Silbering, A.F. et al. Complementary function and integrated wiring of the evolutionarily distinct Drosophila olfactory subsystems. J. Neurosci. 31, 13357–13375 (2011).

    Article  CAS  PubMed Central  Google Scholar 

  26. Ai, M. et al. Acid sensing by the Drosophila olfactory system. Nature 468, 691–695 (2010).

    Article  CAS  PubMed Central  Google Scholar 

  27. Liang, L. et al. GABAergic projection neurons route selective olfactory inputs to specific higher-order neurons. Neuron 79, 917–931 (2013).

    Article  CAS  Google Scholar 

  28. Parnas, M., Lin, A.C., Huetteroth, W. & Miesenbock, G. Odor discrimination in Drosophila: from neural population codes to behavior. Neuron 79, 932–944 (2013).

    Article  CAS  PubMed Central  Google Scholar 

  29. Couto, A., Alenius, M. & Dickson, B.J. Molecular, anatomical, and functional organization of the Drosophila olfactory system. Curr. Biol. 15, 1535–1547 (2005).

    Article  CAS  Google Scholar 

  30. Jefferis, G.S., Marin, E.C., Stocker, R.F. & Luo, L. Target neuron prespecification in the olfactory map of Drosophila. Nature 414, 204–208 (2001).

    Article  CAS  Google Scholar 

  31. Jefferis, G.S. et al. Developmental origin of wiring specificity in the olfactory system of Drosophila. Development 131, 117–130 (2004).

    Article  CAS  Google Scholar 

  32. Yaksi, E. & Wilson, R.I. Electrical coupling between olfactory glomeruli. Neuron 67, 1034–1047 (2010).

    Article  CAS  PubMed Central  Google Scholar 

  33. Lin, H.H., Lai, J.S., Chin, A.L., Chen, Y.C. & Chiang, A.S. A map of olfactory representation in the Drosophila mushroom body. Cell 128, 1205–1217 (2007).

    Article  CAS  Google Scholar 

  34. Kreher, S.A., Mathew, D., Kim, J. & Carlson, J.R. Translation of sensory input into behavioral output via an olfactory system. Neuron 59, 110–124 (2008).

    Article  CAS  PubMed Central  Google Scholar 

  35. Asahina, K., Louis, M., Piccinotti, S. & Vosshall, L.B. A circuit supporting concentration-invariant odor perception in Drosophila. J. Biol. 8, 9 (2009).

    Article  PubMed Central  Google Scholar 

  36. Borst, A. Computation of olfactory signals in Drosophila melanogaster. J. Comp. Physiol. A Neuroethol. Sens. Neural Behav. Physiol. 152, 373–383 (1983).

    Article  Google Scholar 

  37. Dayan, P. & Abbott, L.F. Theoretical Neuroscience (MIT Press, Cambridge, MA, 2001).

  38. Rust, N.C., Mante, V., Simoncelli, E.P. & Movshon, J.A. How MT cells analyze the motion of visual patterns. Nat. Neurosci. 9, 1421–1431 (2006).

    Article  CAS  Google Scholar 

  39. Simoncelli, E.P. Vision and the statistics of the visual environment. Curr. Opin. Neurobiol. 13, 144–149 (2003).

    Article  CAS  Google Scholar 

  40. Wilson, R.I. & Laurent, G. Role of GABAergic inhibition in shaping odor-evoked spatiotemporal patterns in the Drosophila antennal lobe. J. Neurosci. 25, 9069–9079 (2005).

    Article  CAS  PubMed Central  Google Scholar 

  41. Goodman, M.B. & Lockery, S.R. Pressure polishing: a method for re-shaping patch pipettes during fire polishing. J. Neurosci. Methods 100, 13–15 (2000).

    Article  CAS  Google Scholar 

  42. Wilson, R.I., Turner, G.C. & Laurent, G. Transformation of olfactory representations in the Drosophila antennal lobe. Science 303, 366–370 (2004).

    Article  CAS  Google Scholar 

  43. Olsen, S.R., Bhandawat, V. & Wilson, R.I. Divisive normalization in olfactory population codes. Neuron 66, 287–299 (2010).

    Article  CAS  PubMed Central  Google Scholar 

  44. Patterson, G.H. & Lippincott-Schwartz, J. A photoactivatable GFP for selective photolabeling of proteins and cells. Science 297, 1873–1877 (2002).

    Article  CAS  PubMed Central  Google Scholar 

  45. Datta, S.R. et al. The Drosophila pheromone cVA activates a sexually dimorphic neural circuit. Nature 452, 473–477 (2008).

    Article  CAS  Google Scholar 

  46. Pologruto, T.A., Sabatini, B.L. & Svoboda, K. ScanImage: flexible software for operating laser scanning microscopes. Biomed. Eng. Online 2, 13 (2003).

    Article  PubMed Central  Google Scholar 

  47. Perez-Orive, J. et al. Oscillations and sparsening of odor representations in the mushroom body. Science 297, 359–365 (2002).

    Article  CAS  PubMed Central  Google Scholar 

  48. Glantz, S.A. Primer of Biostatistics (McGraw Hill, New York, 2005).

Download references


We are grateful to G. Technau (Johannes Gutenberg University Mainz) for the gift of Mz671-Gal4, J. Simpson (Janelia Farm Research Campus) for c315a-Gal4, G. Rubin and B. Pfeiffer (Janelia Farm Research Campus) for pJFRC7-20XUAS-IVS-mCD8GFP(attp40) and S.R. Datta (Harvard Medical School) for UAS-C3PA-GFP and UAS-SPA-GFP. We thank members of the Wilson laboratory for comments on the manuscript. This work was supported by a research project grant from the US National Institutes of Health (R01DC008174). R.I.W. is a Howard Hughes Medical Institute Investigator. M.F. was supported in part by a Howard Hughes Medical Institute International Student Research Fellowship.

Author information

Authors and Affiliations



M.F. and R.I.W. designed the experiments. M.F. performed the experiments and analyzed the data. M.F. and R.I.W. wrote the manuscript.

Corresponding author

Correspondence to Rachel I Wilson.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Spontaneous activity modulates excitability in Mz671 neurons.

The spike counts recorded in Mz671 neurons in triple recording experiments were substantially lower than those obtained in recordings from the same neurons with olfactory stimulation (compare Figs. 4 and 5), even when the PNs were firing at similar rates. This discrepancy may be attributable to several causes. First, glomeruli other than DM4 may be recruited by low concentrations of the odor (methyl acetate), and these glomeruli might be presynaptic to these LHNs. If so, then the LHN would fire at a higher rate than we would expect based on the activity of DM4 alone. There is evidence that methyl acetate is specific for DM4 at these concentrations (ref. 43), but this idea is still difficult to completely exclude. Second, there could be more than one DM4 PN. If so, we would be stimulating more PNs with odor versus with current injection. This is unlikely, because when we expressed PAGFP pan-neuronally and photoactivated the DM4 glomerulus, we found only one DM4 PN (data not shown). Third, LHNs may exhibit different excitability in the two types of experiments. In support of this idea, there was a systematic difference in the stimulus-evoked change in LHN spike rate for a given change in LHN membrane potential (panel a). Moreover, both spontaneous EPSPs and spikes were systematically reduced in the triple recordings versus (panels b and c; each point in panel b is a different experiment). In the triple recordings, we hyperpolarized the two PNs below their normal resting potential, thereby preventing them from spiking outside the stimulation window. In any given triple recording, we are thereby silencing two of the four known PN inputs to the LHN. This likely explains why spontaneous EPSPs are suppressed. In sum, we conclude that the LHN dendrites are likely somewhat hyperpolarized in the triple recordings, due in part to reduced spontaneous PN input, which diminishes the recruitment of voltage-dependent conductances in LHNs, thereby decreasing postsynaptic depolarization in response to PN spikes, and inhibiting postsynaptic spike generation. Methods for analysis in panel a: LHN spikes were detected and then the LHN membrane potential was low-pass filtered to remove spikes. Trials were then sorted according to LHN spike count, binned into groups of 10, and averaged within each bin. Using these trial-averaged traces, we measured the membrane potential over a window of time. For the triple recordings, this was a 400-ms window starting 100 ms after the onset of current injection in the PN. In odor delivery experiments, there is a variable response delay, so we used a variable window. To find this window, we set a threshold (equal to 3 SDs above the baseline) for each trial-averaged trace, and we defined a 500 ms window starting at the first threshold crossing. Each point in panel a plots the average spike count and average membrane potential for a single bin.

Supplementary Figure 2 Inhibition in NP6099 neurons does not depend on total network activity.

a, This panel contains the same data as in Fig. 7a (top, magenta symbols), but with stimuli color-coded. The smooth curve reproduces the fitted line from Fig. 7a (bottom). b, The total amount of olfactory receptor neuron activity for each stimulus, obtained by summing across the firing rates for all receptor types measured by ref. 23. Color codes are the same as in panel a. (For the four numbered stimuli in black from panel a, olfactory receptor neuron data are not available.) Note that the largest relative suppression in the LHN response (relative to the DP1m response) is observed for E2-hexenal (10-2), valeraldehyde (10-2), and 2-butanone (10-2). By comparison, relatively little suppression is observed for 1-penten-3-ol (10-2) and 1-octen-3-ol (10-2), There is no systematic relationship between the amount of suppression and the amount of total activity elicited in ORNs. Thus, the inhibition which suppresses these responses must be selective for the chemical composition of an odor (presumably therefore reflecting input from a small number of glomeruli), and is not simply driven by total network activity.

Supplementary Figure 3 GABAergic local neurons in the lateral horn.

Single confocal sections through the lateral horn of a brain triple immunostained for neuropil (nc82), CD8, and GABA. Dorsal is up, lateral is right. Expression of CD8:GFP is driven by a Gal4 line with a restricted expression pattern (GMR23F06-Gal4). Single-cell dye fills from this Gal4 line demonstrate that these neurons are local to the lateral horn (Figure 8). Their somata lie in a cluster just lateral and slightly ventral to the lateral horn neuropil (arrowhead), and all these somata are GABA-positive, although not all GABA-positive somata in this cluster express CD8:GFP. In the overlay, a magenta circle indicates the approximate boundary of the lateral horn (as in Figures 1 and 8). Similar results were obtained in three brains.

Supplementary Figure 4 Type II LHNs, but not type I LHNs, receive prominent odor-evoked inhibition.

a, Responses of a typical NP6099 neuron to a stimulus that elicits prominent hyperpolarization. In different trials, we manipulated the membrane potential of the LHN by injecting different amounts of current via the patch pipette. Note that odor-evoked hyperpolarization increases at more depolarized holding potentials, indicating that inhibition is at least partly postsynaptic.`b, In a single case, we observed odor-evoked hyperpolarization in a type I LHN. This was noted as part of our general survey of type I LHNs labeled by GMR48F03-Gal4 and GMR73B12-Gal4 (Figs. 1b and 2a). In addition to the core odor set shown in Fig. 2a, we used 26 additional odors in various recordings in the course of this survey, in order to determine if any of them elicited hyperpolarization. We held cells at a depolarized potential during these trials (-40 to -30 mV) to better reveal any inhibition that might be present. Shown here are responses to these 26 odors, only the last of which elicited any hyperpolarization. This example shows that type I cells can receive odor-evoked inhibition, but is still consistent with the conclusion this inhibition is much less prominent than in type II cells. (Odors are : 2,3-butanedione, 2-heptanone, 2-octanone, 3-octanol, α-pinene, benzaldehyde, butyric acid, cis-3-hexen-1-ol, beta-citronellal, cyclohexanone, ethyl butyrate, ethyl caproate, ethyl cinnamate, ethyl lactate, ethyl propionate, fenchone, γ-octalactone, ginger oil, hexyl acetate, linalool, nerol, phenethyl acetate, pyrrolidine, triethylamine, valeraldehyde, isoamylamine; all dilutions are 10-2; horizontal bar in first panel shows 500 ms odor stimulus period.)

Supplementary Figure 5 Spike detection in LHNs.

Action potentials recorded from LHN somata tend to be unusually small. In order to verify that the fastest events we observe in the voltage trace are indeed action potentials, we confirmed that these small rapid events can be elicited with depolarizing current injection, and that they are sensitive to TTX (panel a). We also noted that the rate of these events grows with increasing depolarizing current injection, as expected. In order to detect and count these events, we used a two-threshold algorithm. We identified the events that were both the fastest the rise and the fastest to decay, using an algorithm that thresholded the second and first derivatives of the voltage trace with positive and negative thresholds, respectively (see traces below panels b and c). Thresholds were determined on a cell-by-cell basis. Spikes that were automatically detected are marked with vertical bars. Automated detection was robust at low firing rates (panel b, left). However, at high firing rates, action potential height decreased. Therefore, thresholds were adjusted at high firing rates, and each trace was also individually inspected, and missed spikes were corrected (panel c). Arrows point to missed spikes that were missed by the algorithm and then subsequently corrected. For comparison, arrowheads point to fluctuations in the voltage that were not subsequently marked as spikes. These examples show that there was a clear separation between spikes and non-spikes based on the rates of change in the voltage traces. In order to ensure that the subjectivity of this process does not bias our results in Fig. 5b, we blinded the person performing the analysis to stimulus concentrations.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–5, Supplementary Table 1 (PDF 2457 kb)

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Fişek, M., Wilson, R. Stereotyped connectivity and computations in higher-order olfactory neurons. Nat Neurosci 17, 280–288 (2014).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:

This article is cited by


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing