Skip to main content

Thank you for visiting nature.com. 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.

Synaptic and circuit mechanisms promoting broadband transmission of olfactory stimulus dynamics

Nature Neuroscience volume 18, pages 56–65 (2015)Cite this article

Subjects

Abstract

Sensory stimuli fluctuate on many timescales. However, short-term plasticity causes synapses to act as temporal filters, limiting the range of frequencies that they can transmit. How synapses in vivo might transmit a range of frequencies in spite of short-term plasticity is poorly understood. The first synapse in the Drosophila olfactory system exhibits short-term depression, but can transmit broadband signals. Here we describe two mechanisms that broaden the frequency characteristics of this synapse. First, two distinct excitatory postsynaptic currents transmit signals on different timescales. Second, presynaptic inhibition dynamically updates synaptic properties to promote accurate transmission of signals across a wide range of frequencies. Inhibition is transient, but grows slowly, and simulations reveal that these two features of inhibition promote broadband synaptic transmission. Dynamic inhibition is often thought to restrict the temporal patterns that a neuron responds to, but our results illustrate a different idea: inhibition can expand the bandwidth of neural coding.

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

Get just this article for as long as you need it

$39.95

Learn more

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

Figure 1: Mismatch between predictions of a simple depression model and PN odor responses.
Figure 2: Two distinct components of EPSCs at ORN-to-PN synapses.
Figure 3: Inhibition truncates responses to brief pulses and stabilizes responses to long pulses.
Figure 4: Inhibition truncates responses to sparse stimuli and stabilizes responses to dense stimuli.
Figure 5: LNs respond transiently to odor stimuli.
Figure 6: Inhibition grows slowly relative to LN spiking.
Figure 7: The dynamics of inhibition are functionally relevant.
Figure 8: Inhibition flattens the frequency response of synaptic transmission.

References

  1. Fairhall, A. Adaptation and natural stimulus statistics. in The Cognitive Neurosciences (ed. M.S. Gazzaniga) 283–294 (MIT Press, Cambridge, Massachusetts, 2014).

  2. Fackrell, J.E. & Robins, A.G. Concentration fluctuations and fluxes in plumes from point sources in a turbulent boundary layer. J. Fluid Mech. 117, 1–26 (1982).

    Article  CAS  Google Scholar 

  3. Crimaldi, J.P. & Koseff, J.R. High-resolution measurements of the spatial and temporal structure of a turbulent plume. Exp. Fluids 31, 90–102 (2001).

    Article  Google Scholar 

  4. Zucker, R.S. & Regehr, W.G. Short-term synaptic plasticity. Annu. Rev. Physiol. 64, 355–405 (2002).

    Article  CAS  PubMed  Google Scholar 

  5. Abbott, L.F. & Regehr, W.G. Synaptic computation. Nature 431, 796–803 (2004).

    Article  CAS  PubMed  Google Scholar 

  6. Silver, R.A. Neuronal arithmetic. Nat. Rev. Neurosci. 11, 474–489 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Abbott, L.F., Varela, J.A., Sen, K. & Nelson, S.B. Synaptic depression and cortical gain control. Science 275, 220–224 (1997).

    Article  CAS  PubMed  Google Scholar 

  8. Tsodyks, M.V. & Markram, H. The neural code between neocortical pyramidal neurons depends on neurotransmitter release probability. Proc. Natl. Acad. Sci. USA 94, 719–723 (1997).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Ozuysal, Y. & Baccus, S.A. Linking the computational structure of variance adaptation to biophysical mechanisms. Neuron 73, 1002–1015 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Oesch, N.W. & Diamond, J.S. Ribbon synapses compute temporal contrast and encode luminance in retinal rod bipolar cells. Nat. Neurosci. 14, 1555–1561 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Ke, J.B. et al. Adaptation to background light enables contrast coding at rod bipolar cell synapses. Neuron 81, 388–401 (2014).

    Article  CAS  PubMed  Google Scholar 

  12. Kazama, H. & Wilson, R.I. Homeostatic matching and nonlinear amplification at genetically-identified central synapses. Neuron 58, 401–413 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Taschenberger, H., Leao, R.M., Rowland, K.C., Spirou, G.A. & von Gersdorff, H. Optimizing synaptic architecture and efficiency for high-frequency transmission. Neuron 36, 1127–1143 (2002).

    Article  CAS  PubMed  Google Scholar 

  14. Chen, C. & Regehr, W.G. Developmental remodeling of the retinogeniculate synapse. Neuron 28, 955–966 (2000).

    Article  CAS  PubMed  Google Scholar 

  15. Murphy, G.J., Glickfeld, L.L., Balsen, Z. & Isaacson, J.S. Sensory neuron signaling to the brain: properties of transmitter release from olfactory nerve terminals. J. Neurosci. 24, 3023–3030 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Carandini, M., Horton, J.C. & Sincich, L.C. Thalamic filtering of retinal spike trains by postsynaptic summation. J. Vis. 7, 20.1–11 (2007).

    Article  Google Scholar 

  17. Kraaij, D.A., Spekreijse, H. & Kamermans, M. The open- and closed-loop gain-characteristics of the cone/horizontal cell synapse in goldfish retina. J. Neurophysiol. 84, 1256–1265 (2000).

    Article  CAS  PubMed  Google Scholar 

  18. Boudreau, C.E. & Ferster, D. Short-term depression in thalamocortical synapses of cat primary visual cortex. J. Neurosci. 25, 7179–7190 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Young, E.D. & Sachs, M.B. Auditory nerve inputs to cochlear nucleus neurons studied with cross-correlation. Neuroscience 154, 127–138 (2008).

    Article  CAS  PubMed  Google Scholar 

  20. Lorteije, J.A., Rusu, S.I., Kushmerick, C. & Borst, J.G. Reliability and precision of the mouse calyx of Held synapse. J. Neurosci. 29, 13770–13784 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Brenowitz, S. & Trussell, L.O. Minimizing synaptic depression by control of release probability. J. Neurosci. 21, 1857–1867 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Ohliger-Frerking, P., Wiebe, S.P., Staubli, U. & Frerking, M. GABA(B) receptor-mediated presynaptic inhibition has history-dependent effects on synaptic transmission during physiologically relevant spike trains. J. Neurosci. 23, 4809–4814 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Manu, M. & Baccus, S.A. Disinhibitory gating of retinal output by transmission from an amacrine cell. Proc. Natl. Acad. Sci. USA 108, 18447–18452 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  24. Nikolaev, A., Leung, K.M., Odermatt, B. & Lagnado, L. Synaptic mechanisms of adaptation and sensitization in the retina. Nat. Neurosci. 16, 934–941 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Mazor, O. & Laurent, G. Transient dynamics vs. fixed points in odor representations by locust antennal lobe projection neurons. Neuron 48, 661–673 (2005).

    Article  CAS  PubMed  Google Scholar 

  26. Vickers, N.J., Christensen, T.A., Baker, T.C. & Hildebrand, J.G. Odour-plume dynamics influence the brain's olfactory code. Nature 410, 466–470 (2001).

    Article  CAS  PubMed  Google Scholar 

  27. Geffen, M.N., Broome, B.M., Laurent, G. & Meister, M. Neural encoding of rapidly fluctuating odors. Neuron 61, 570–586 (2009).

    Article  CAS  PubMed  Google Scholar 

  28. Kazama, H. & Wilson, R.I. Origins of correlated activity in an olfactory circuit. Nat. Neurosci. 12, 1136–1144 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  30. Thany, S.H., Lenaers, G., Raymond-Delpech, V., Sattelle, D.B. & Lapied, B. Exploring the pharmacological properties of insect nicotinic acetylcholine receptors. Trends Pharmacol. Sci. 28, 14–22 (2007).

    Article  CAS  PubMed  Google Scholar 

  31. Jones, A.K., Brown, L.A. & Sattelle, D.B. Insect nicotinic acetylcholine receptor gene families: from genetic model organism to vector, pest and beneficial species. Invert. Neurosci. 7, 67–73 (2007).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  33. Olsen, S.R. & Wilson, R.I. Lateral presynaptic inhibition mediates gain control in an olfactory circuit. Nature 452, 956–960 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Murphy, G.J., Darcy, D.P. & Isaacson, J.S. Intraglomerular inhibition: signaling mechanisms of an olfactory microcircuit. Nat. Neurosci. 8, 354–364 (2005).

    Article  CAS  PubMed  Google Scholar 

  35. Aroniadou-Anderjaska, V., Zhou, F.M., Priest, C.A., Ennis, M. & Shipley, M.T. Tonic and synaptically evoked presynaptic inhibition of sensory input to the rat olfactory bulb via GABA(B) heteroreceptors. J. Neurophysiol. 84, 1194–1203 (2000).

    Article  CAS  PubMed  Google Scholar 

  36. Wachowiak, M. et al. Inhibition of olfactory receptor neuron input to olfactory bulb glomeruli mediated by suppression of presynaptic calcium influx. J. Neurophysiol. 94, 2700–2712 (2005).

    Article  CAS  PubMed  Google Scholar 

  37. Liu, W.W. & Wilson, R.I. Glutamate is an inhibitory neurotransmitter in the Drosophila olfactory system. Proc. Natl. Acad. Sci. USA 110, 10294–10299 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  38. Root, C.M. et al. A presynaptic gain control mechanism fine-tunes olfactory behavior. Neuron 59, 311–321 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Bagnall, M.W., McElvain, L.E., Faulstich, M. & du Lac, S. Frequency-independent synaptic transmission supports a linear vestibular behavior. Neuron 60, 343–352 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Telgkamp, P., Padgett, D.E., Ledoux, V.A., Woolley, C.S. & Raman, I.M. Maintenance of high-frequency transmission at purkinje to cerebellar nuclear synapses by spillover from boutons with multiple release sites. Neuron 41, 113–126 (2004).

    Article  CAS  PubMed  Google Scholar 

  41. Feldman, D.E. & Knudsen, E.I. NMDA and non-NMDA glutamate receptors in auditory transmission in the barn owl inferior colliculus. J. Neurosci. 14, 5939–5958 (1994).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Zhang, H. & Kelly, J.B. AMPA and NMDA receptors regulate responses of neurons in the rat's inferior colliculus. J. Neurophysiol. 86, 871–880 (2001).

    Article  CAS  PubMed  Google Scholar 

  43. Blitz, D.M. & Regehr, W.G. Retinogeniculate synaptic properties controlling spike number and timing in relay neurons. J. Neurophysiol. 90, 2438–2450 (2003).

    Article  PubMed  Google Scholar 

  44. Hollmann, M. & Heinemann, S. Cloned glutamate receptors. Annu. Rev. Neurosci. 17, 31–108 (1994).

    Article  CAS  PubMed  Google Scholar 

  45. Beierlein, M., Gibson, J.R. & Connors, B.W. Two dynamically distinct inhibitory networks in layer 4 of the neocortex. J. Neurophysiol. 90, 2987–3000 (2003).

    Article  PubMed  Google Scholar 

  46. Stokes, C.C. & Isaacson, J.S. From dendrite to soma: dynamic routing of inhibition by complementary interneuron microcircuits in olfactory cortex. Neuron 67, 452–465 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Pouille, F. & Scanziani, M. Routing of spike series by dynamic circuits in the hippocampus. Nature 429, 717–723 (2004).

    CAS  PubMed  Google Scholar 

  48. Wehr, M. & Zador, A.M. Balanced inhibition underlies tuning and sharpens spike timing in auditory cortex. Nature 426, 442–446 (2003).

    Article  CAS  PubMed  Google Scholar 

  49. Tan, A.Y., Zhang, L.I., Merzenich, M.M. & Schreiner, C.E. Tone-evoked excitatory and inhibitory synaptic conductances of primary auditory cortex neurons. J. Neurophysiol. 92, 630–643 (2004).

    Article  PubMed  Google Scholar 

  50. Isaacson, J.S. & Scanziani, M. How inhibition shapes cortical activity. Neuron 72, 231–243 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Lee, T. & Luo, L. Mosaic analysis with a repressible cell marker for studies of gene function in neuronal morphogenesis. Neuron 22, 451–461 (1999).

    CAS  PubMed  Google Scholar 

  52. Olsen, S.R., Bhandawat, V. & Wilson, R.I. Excitatory interactions between olfactory processing channels in the Drosophila antennal lobe. Neuron 54, 89–103 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Stocker, R.F., Heimbeck, G., Gendre, N. & de Belle, J.S. Neuroblast ablation in Drosophila P[GAL4] lines reveals origins of olfactory interneurons. J. Neurobiol. 32, 443–456 (1997).

    Article  CAS  PubMed  Google Scholar 

  54. Chou, Y.H. et al. Diversity and wiring variability of olfactory local interneurons in the Drosophila antennal lobe. Nat. Neurosci. 13, 439–449 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Hwang, R.Y. et al. Nociceptive neurons protect Drosophila larvae from parasitoid wasps. Curr. Biol. 17, 2105–2116 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Baird, D.H., Schalet, A.P. & Wyman, R.J. The Passover locus in Drosophila melanogaster: complex complementation and different effects on the giant fiber neural pathway. Genetics 126, 1045–1059 (1990).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. 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  PubMed Central  Google Scholar 

  58. Gouwens, N.W. & Wilson, R.I. Signal propagation in Drosophila central neurons. J. Neurosci. 29, 6239–6249 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. de Bruyne, M., Clyne, P.J. & Carlson, J.R. Odor coding in a model olfactory organ: the Drosophila maxillary palp. J. Neurosci. 19, 4520–4532 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

  62. Hille, B. Ionic Channels of Excitable Membranes (Sinauer Associates, Sunderland, Massachusetts, 1992).

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

    Article  CAS  PubMed  Google Scholar 

  64. Nagel, K.I. & Wilson, R.I. Biophysical mechanisms underlying olfactory receptor neuron dynamics. Nat. Neurosci. 14, 208–216 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Huang, J., Zhang, W., Qiao, W., Hu, A. & Wang, Z. Functional connectivity and selective odor responses of excitatory local interneurons in Drosophila antennal lobe. Neuron 67, 1021–1033 (2010).

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We thank members of the Wilson laboratory, D. Schoppik, G. Murphy and A. Liu for helpful discussions and/or comments on the manuscript. E. Yaksi performed preliminary experiments on LN temporal properties that inspired parts of this work. This work was supported by a research project grant from the US National Institutes of Health (R01 DC008174) and a Pathway to Independence Award from the US National Institutes of Health (K99 DC012065, to K.I.N.). R.I.W. receives funding from the Howard Hughes Medical Institute.

Author information

Author notes

  1. Katherine I Nagel

    Present address: Present address: Neuroscience Institute, New York University School of Medicine, New York, New York, USA.,

Authors and Affiliations

  1. Department of Neurobiology, Harvard Medical School, Boston, Massachusetts, USA

    Katherine I Nagel, Elizabeth J Hong & Rachel I Wilson

Authors
  1. Katherine I Nagel
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Elizabeth J Hong
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rachel I Wilson
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All experiments were designed by K.I.N. and R.I.W., and performed and analyzed by K.I.N., except for those shown in Figure 6c–f, which were designed by E.J.H. and R.I.W., performed by E.J.H., and analyzed by K.I.N. and E.J.H. Modeling was performed by K.I.N. K.I.N. and R.I.W. wrote the manuscript with input and critical feedback from E.J.H.

Corresponding author

Correspondence to Rachel I Wilson.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Short-term depression is an intrinsic property of ORN-to-PN synapses.

ORN axons in the antennal nerve were stimulated with a train of 20 stimuli at 10 Hz (as in Figure 1b,c). Evoked EPSCs were recorded from PNs in whole-cell voltage clamp mode, both before and after blocking synaptic inhibition (with bath application of 25 – 50 μM CGP54626 and 5 μM picrotoxin). a) EPSC amplitudes evoked by each stimulus in a 10 Hz train, normalized to the first EPSC in the train before averaging across PNs, mean ± s.e.m., n = 4 PNs). Blocking inhibition had little effect on the dynamics of short-term depression at this presynaptic firing rate. b) EPSCs evoked by the entire 10 Hz train (averaged across 4 PNs, 2-5 trials per PN). EPSCs were normalized to the amplitude of the first EPSC prior to averaging. c) The first EPSC evoked by the train (arrow indicates stimulus time; stimulus artifact is blanked and mended by interpolation. When inhibition was blocked, some EPSCs were contaminated with currents arising from unclamped spikes in the PN. We excluded experiments where the rate of unclamped spikes was particularly high, but it should be noted that the occurrence of some unclamped spikes makes it difficult to precisely estimate EPSC amplitudes under these conditions.

Supplementary Figure 2 Intrinsic properties of PNs.

a) When odor pulses are delivered in a dense train, or a long pulse, the PN membrane potential response depresses over time (black traces). Injecting depolarizing current into a PN in the same pattern elicits a membrane potential response that does not depress (green traces). Thus, these PN odor response dynamics do not arise from intrinsic properties of the PN, but rather are due to dynamics in the synaptic input to the PN. b) The dynamics of PN spiking responses to odors resemble the dynamics of their membrane potential responses. Shown here is a typical example PN, where the odor stimulus was a dense random train of pulses. Both the membrane potential and the firing rate are able to encode even brief pulses, and both can follow a long dense train. As the membrane potential response depresses, the firing rate also depresses. (Note there is a burst of PN spikes at stimulus onset which is not mirrored in the membrane potential; as in many neurons, firing rates in PNs depend to some degree on the rate of change of membrane potential as well as its absolute value. However, a static relationship between membrane potential and spiking captures much of the data, as described below.) c) Left: in each time bin, PN firing rate is plotted versus membrane potential for one typical recording. Red trace shows average firing rate binned by membrane potential. A static threshold nonlinearity describes much of the data, with a fairly linear relationship above threshold. Right: binned data for 5 typical PNs. These results show that PN firing rates are sensitive to changes in membrane potential over a wide range of membrane potential values. The curve in magenta is the example PN on the left.

Supplementary Figure 3 Models of PNs and ORNs.

a) Simple synaptic depression model. In this model, synaptic input to a PN depresses with a single set of parameters (f, τ), and PN membrane potential is modeled using a passive single compartment model. This illustration shows model current and voltage responses to a single ORN spike (left) and a train of spikes in a single ORN at 10 Hz (right). Parameters of the PN membrane potential model were chosen such that a single spike in one ORN elicited an EPSC with a peak amplitude of about 13.5 pA and an EPSP with a peak depolarization of about 7 mV, consistent with published data (see Methods and ref. 12). Synaptic depression parameters (f, τ) were fit to peak EPSC amplitudes in response to 10 Hz antennal nerve stimulation (Figure 1c). The dynamics of simulated responses to 10 Hz spike trains closely resemble our data. This model was used in Figure 1. b) Model with two postsynaptic components. A two-component model was used in Figure 2g-h, Figure 7, and Figure 8. Here each unitary EPSC was composed of a fast and a slow component. The amplitude of the unitary fast and slow conductances were chosen such that the combined EPSC had an amplitude of about 13.5 pA (in agreement with published data). The relative magnitudes of the two conductances were determined by fitting a biexponential decay to the normalized first EPSC in control conditions. Depression parameters (f, τ) for the fast component were fit to EPSCs in IMI. Depression parameters for the slow component were either fit to EPSCs in curare (shown here, Figure 2g-h) or else fit to PN odor responses with inhibition blocked (Figure 7 and Figure 8). c) ORN filter. To model ORN spike rates in Figure 8, we convolved the binary stimulus waveform with the filter shown here. The filter was extracted by cross-correlating ORN spikes with simultaneously-recorded measurements of odor concentration using a fast photoionization detector, followed by decorrelation by the power spectrum of the odor concentration fluctuations (ref. 64). ORN spikes were recorded from ORNs presynaptic to glomerulus VM7. d) Model ORN responses to short and long odor pulses. Firing rates were obtained by convolving the ORN filter with the stimulus waveform. All stimuli have an amplitude of 1.

Supplementary Figure 4 Pharmacology of acetylcholine-evoked currents.

a) Mean acetylcholine-evoked currents recorded in voltage-clamp recordings from PNs (glomerulus VM2, DM6, or VM7, n=5 PNs, average of 5-6 trials per PN). Acetylcholine was iontophoresed into the neuropil for 5 ms at the time indicated (arrows). Bath application of curare (10 μM) slows the acetylcholine-evoked current. Adding IMI (100 nM) together with curare blocks a substantial fraction of the evoked current. Note that IMI (100 nM) increases the basal holding current, consistent with reports that it can act as a partial agonist (ref. 30). b) Same as panel a, but with baselines aligned to better illustrate changes in the timecourse of the evoked current.

We note that the two drugs did not completely block the response to iontophoresis of acetylcholine, likely because some of the current evoked by iontophoresis is due to metabotropic receptors. The two drugs together blocked 94.1+/-2.7% of the response to iontophoresis of nicotine (10 ms stimulus duration, n=4). In the presence of atropine (10 μM), the two drugs blocked 81% of the response to iontophoresis of acetylcholine (n=1). Metabotropic receptor antagonists (atropine at 10 μM and scopolamine at 10 μM) had no effect on responses to nerve stimulation. Together, these results imply that curare and IMI act on nicotinic acetylcholine receptors, and that most of the current evoked by nerve stimulation is nicotinic. Metabotropic receptors are evidently present on PNs and are activated by iontophoresis of acetylcholine but not by our nerve stimulation protocol.

Acetylcholine chloride was prepared as a 1M solution in water, nicotine hydrogen tartrate as a 0.25-1 M solution in water, atropine as a 5 mM stock in water, and scopolamine as a 1 mM stock in water. All drugs were obtained from Sigma and stored at −4°C as aqueous stocks. Iontophoretic ejection currents were +350-800 nA, and the constant holding current was -10 nA; these stimuli were delivered with a constant current generator (World Precision Instruments, Model 260).

Supplementary Figure 5 The slow decay in the odor responses of disinhibited PNs is primarily due to synaptic dynamics, with a minor contribution from ORN firing rate dynamics.

a) ORN firing rates evoked by a dense randomly fluctuating stimulus (valve open 50% of the time). Data represent the average of several recordings from VM7 ORNs. These ORN firing rates decay slowly over time. b) Mean PN membrane potential responses to the same odor stimuli (n = 7 PNs in glomerulus VM7). This is a subset of the data shown in Figure 1g, focusing here on VM7 PNs only. c) Mean responses of the same cells following blockade of inhibition. This should represent the purely feedforward response of the PN. Note that when ORN responses have reached steady-state, PN responses are still slowly decaying. Thus, much of the slow decay in PN responses cannot be inherited from ORN firing rates, and likely arises from the dynamics of ORN-to-PN synapses. This slow decay is normally masked by inhibition. d) Output of the single component model without inhibition (same as in Figure 1). Note the slow decay in ORN firing rates is not transmitted to the PNs, because at high presynaptic firing rates, PNs are relatively insensitive to small changes in presynaptic rate. Instead, PN response dynamics are dominated by the dynamics of synaptic transmission. e) Output of a two-component model without inhibition (acting on the ORN spike rates shown in panel a). Parameters of the fast component were the same as in Figure 2 (fit to EPSCs in IMI). Parameters of the slow component were adjusted to maximize the fit between model output and the disinhibited PN membrane potential shown in panel c. This model captures the slow decay in PN odor responses when inhibition is blocked. This model was used in Figures 7 and 8.

Supplementary Figure 6 Quasi-steady-state responses of our models.

a) Responses of model PNs after 2 s of stimulation, with and without tonic presynaptic inhibition. In this model, ORN-to-PN synapses have both fast and slow components. (This model is equivalent to the one shown in Figure 8, but with tonic inhibition fixed at the pre-odor level, instead of dynamic inhibition.) Here, simulated stimuli were 2-s steps of steady ORN firing rates from zero to a range of levels (1-200 spikes/s). The time-averaged PN membrane potential was measured over the window from 1 s to 2 s after the onset of the stimulus, and was plotted as a function of presynaptic firing rate. As shown in previous theoretical studies (refs. 7,8) the postsynaptic response saturates rapidly as a function of presynaptic firing rate, and adding increasing amounts of tonic presynaptic inhibition causes the postsynaptic response to saturate at a higher presynaptic firing rate. Small amounts of presynaptic inhibition have a more profound effect on the response to low presynaptic firing rates than on the response to high presynaptic firing rates.

b) Model responses after 2 s of stimulation (calculated as in panel a), as a function of presynaptic firing rate for the different model components. The fast component of excitation at ORN-to-PN synapses has a relatively small dynamic range for encoding tonic changes in presynaptic firing rate, while the slow component shows a larger dynamic range. A model with both fast and slow components shows a larger dynamic range but saturates at a low presynaptic firing rate. Both tonic and dynamic inhibition cause the postsynaptic response to saturate more gradually as a function of presynaptic firing rate. Here dynamic inhibition was modeled as in Figure 8, and tonic inhibition was fixed at the pre-odor level of inhibition in the dynamic model.

Supplementary Figure 7 Testing the speed and stability of the odor delivery device.

In this test, the valve that delivers odor to the preparation was opened for 20 ms, 100 ms, 500 ms, or 2.5 s. Traces at top show these command signals. The odor was 2-hepanone, here used at 1:10 in order to increase the signal-to-noise ratio of detector measurements. A photoionization detector (miniPID 200B, Aurora Scientific) was placed at the position of the preparation in order to measure the time course of the odor concentration change at the preparation. These measurements showed that the odor delivery device could reliably deliver square pulses over a wide range of durations, and that the concentration of the odor at the fly’s location was reasonably stable.

Supplementary information

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nagel, K., Hong, E. & Wilson, R. Synaptic and circuit mechanisms promoting broadband transmission of olfactory stimulus dynamics. Nat Neurosci 18, 56–65 (2015). https://doi.org/10.1038/nn.3895

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nn.3895

This article is cited by

Search

Advanced search

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