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A neural circuit architecture for angular integration in Drosophila

Abstract

Many animals keep track of their angular heading over time while navigating through their environment. However, a neural-circuit architecture for computing heading has not been experimentally defined in any species. Here we describe a set of clockwise- and anticlockwise-shifting neurons in the Drosophila central complex whose wiring and physiology provide a means to rotate an angular heading estimate based on the fly’s angular velocity. We show that each class of shifting neurons exists in two subtypes, with spatiotemporal activity profiles that suggest different roles for each subtype at the start and end of tethered-walking turns. Shifting neurons are required for the heading system to properly track the fly’s heading in the dark, and stimulation of these neurons induces predictable shifts in the heading signal. The central features of this biological circuit are analogous to those of computational models proposed for head-direction cells in rodents and may shed light on how neural systems, in general, perform integration.

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Figure 1: The activity of three cell types in the protocerebral bridge tracks the fly’s heading.
Figure 2: P-EN neurons in the left and right bridge are asymmetrically active when the fly turns, consistent with an anatomically inspired model for neural integration.
Figure 3: The P-EN1 activity peak leads, and the P-EN2 peak trails, a rotating E-PG peak in the ellipsoid body, as predicted by their activity in the bridge.
Figure 4: P-EN1 and P-EN2 bridge asymmetries respectively lead and lag phase shifts in time, and impairing P-ENs impairs E-PG phase updating in the dark.
Figure 5: P-EN neurons medially excite E-PG neurons in the bridge, consistent with a model of neural integration.

References

  1. O’Keefe, J . & Nadel, L. The Hippocampus as a Cognitive Map (Clarendon, 1978)

  2. Tolman, E. C., Ritchie, B. F. & Kalish, D. Studies in spatial learning: orientation and the short-cut. J. Exp. Psychol. Gen. 36, 13–24 (1946)

    CAS  Article  Google Scholar 

  3. Etienne, A. S. & Jeffery, K. J. Path integration in mammals. Hippocampus 14, 180–192 (2004)

    PubMed  Article  Google Scholar 

  4. Mittelstaedt, M. L. & Mittelstaedt, H. Homing by path integration in a mammal. Naturwissenschaften 67, 566–567 (1980)

    Article  ADS  Google Scholar 

  5. Collett, T. S. & Collett, M. Path integration in insects. Curr. Opin. Neurobiol. 10, 757–762 (2000)

    CAS  PubMed  MATH  Article  Google Scholar 

  6. Wehner, R. & Srinivasan, M. in The Neurobiology of Spatial Behaviour (ed. Jeffery, K. J. ) Ch. 1 (Oxford Univ. Press, 2003)

  7. Von Frisch, K. The Dance Language and Orientation of Bees (Harvard Univ. Press, 1967)

  8. Taube, J. S., Muller, R. U. & Ranck, J. B. Jr. Head-direction cells recorded from the postsubiculum in freely moving rats. I. Description and quantitative analysis. J. Neurosci. 10, 420–435 (1990)

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  9. Taube, J. S., Muller, R. U. & Ranck, J. B. Jr. Head-direction cells recorded from the postsubiculum in freely moving rats. II. Effects of environmental manipulations. J. Neurosci. 10, 436–447 (1990)

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  10. Varga, A. G. & Ritzmann, R. E. Cellular basis of head direction and contextual cues in the insect brain. Curr. Biol. 26, 1816–1828 (2016)

    CAS  PubMed  Article  Google Scholar 

  11. Heinze, S. & Homberg, U. Maplike representation of celestial E-vector orientations in the brain of an insect. Science 315, 995–997 (2007)

    CAS  PubMed  Article  ADS  Google Scholar 

  12. Finkelstein, A. et al. Three-dimensional head-direction coding in the bat brain. Nature 517, 159–164 (2015)

    CAS  PubMed  Article  ADS  Google Scholar 

  13. Robertson, R. G., Rolls, E. T., Georges-François, P. & Panzeri, S. Head direction cells in the primate pre-subiculum. Hippocampus 9, 206–219 (1999)

    CAS  PubMed  Article  Google Scholar 

  14. Seelig, J. D. & Jayaraman, V. Neural dynamics for landmark orientation and angular path integration. Nature 521, 186–191 (2015)

    CAS  PubMed  PubMed Central  Article  ADS  Google Scholar 

  15. Skaggs, W. E., Knierim, J. J., Kudrimoti, H. S. & McNaughton, B. L. A model of the neural basis of the rat’s sense of direction. Adv. Neural Inf. Process. Syst. 7, 173–180 (1995)

    CAS  PubMed  Google Scholar 

  16. Zhang, K. Representation of spatial orientation by the intrinsic dynamics of the head-direction cell ensemble: a theory. J. Neurosci. 16, 2112–2126 (1996)

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  17. Redish, A. D., Elga, A. N. & Touretzky, D. S. A coupled attractor model of the rodent head direction system. Network 7, 671–685 (1996)

    MATH  Article  Google Scholar 

  18. Sharp, P. E., Blair, H. T. & Brown, M. Neural network modeling of the hippocampal formation spatial signals and their possible role in navigation: a modular approach. Hippocampus 6, 720–734 (1996)

    CAS  PubMed  Article  Google Scholar 

  19. McNaughton, B. L., Battaglia, F. P., Jensen, O., Moser, E. I. & Moser, M.-B. Path integration and the neural basis of the ‘cognitive map’. Nat. Rev. Neurosci. 7, 663–678 (2006)

    CAS  PubMed  Article  Google Scholar 

  20. Kakaria, K. S. & De Bivort, B. L. Ring attractor dynamics emerge from a spiking model of the entire protocerebral bridge. Front. Behav. Neurosci. 11, 6706–6713 (2017)

    Article  Google Scholar 

  21. Wolff, T., Iyer, N. A. & Rubin, G. M. Neuroarchitecture and neuroanatomy of the Drosophila central complex: A GAL4-based dissection of protocerebral bridge neurons and circuits. J. Comp. Neurol. 523, 997–1037 (2015)

    PubMed  Article  Google Scholar 

  22. Lin, C.-Y. et al. A comprehensive wiring diagram of the protocerebral bridge for visual information processing in the Drosophila brain. Cell Reports 3, 1739–1753 (2013)

    CAS  PubMed  Article  ADS  Google Scholar 

  23. Chen, T.-W. et al. Ultrasensitive fluorescent proteins for imaging neuronal activity. Nature 499, 295–300 (2013)

    CAS  PubMed  PubMed Central  Article  ADS  Google Scholar 

  24. Maimon, G., Straw, A. D. & Dickinson, M. H. Active flight increases the gain of visual motion processing in Drosophila. Nat. Neurosci. 13, 393–399 (2010)

    CAS  PubMed  Article  Google Scholar 

  25. Seelig, J. D. et al. Two-photon calcium imaging from head-fixed Drosophila during optomotor walking behavior. Nat. Methods 7, 535–540 (2010)

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  26. Moore, R. J. D. et al. FicTrac: a visual method for tracking spherical motion and generating fictive animal paths. J. Neurosci. Methods 225, 106–119 (2014)

    PubMed  Article  Google Scholar 

  27. Reiser, M. B. & Dickinson, M. H. A modular display system for insect behavioral neuroscience. J. Neurosci. Methods 167, 127–139 (2008)

    PubMed  Article  Google Scholar 

  28. Dana, H. et al. Sensitive red protein calcium indicators for imaging neural activity. eLife 5, 413 (2016)

    Article  CAS  Google Scholar 

  29. Poodry, C. A. & Edgar, L. Reversible alteration in the neuromuscular junctions of Drosophila melanogaster bearing a temperature-sensitive mutation, shibire. J. Cell Biol. 81, 520–527 (1979)

    CAS  PubMed  Article  Google Scholar 

  30. Zemelman, B. V., Nesnas, N., Lee, G. A. & Miesenbock, G. Photochemical gating of heterologous ion channels: remote control over genetically designated populations of neurons. Proc. Natl Acad. Sci. USA 100, 1352–1357 (2003)

    CAS  PubMed  Article  ADS  PubMed Central  Google Scholar 

  31. Turner-Evans, D. et al. Angular velocity integration in a fly heading circuit. eLife http://dx.doi.org/10.7554/eLife.23496 (2017)

  32. Taube, J. S. The head direction signal: origins and sensory-motor integration. Annu. Rev. Neurosci. 30, 181–207 (2007)

    MathSciNet  CAS  PubMed  Article  Google Scholar 

  33. Stackman, R. W. & Taube, J. S. Firing properties of rat lateral mammillary single units: head direction, head pitch, and angular head velocity. J. Neurosci. 18, 9020–9037 (1998)

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  34. Blair, H. T., Cho, J. & Sharp, P. E. Role of the lateral mammillary nucleus in the rat head direction circuit: a combined single unit recording and lesion study. Neuron 21, 1387–1397 (1998)

    CAS  PubMed  Article  Google Scholar 

  35. Hafting, T., Fyhn, M., Molden, S., Moser, M.-B. & Moser, E. I. Microstructure of a spatial map in the entorhinal cortex. Nature 436, 801–806 (2005)

    CAS  Article  ADS  PubMed  Google Scholar 

  36. Sargolini, F. et al. Conjunctive representation of position, direction, and velocity in entorhinal cortex. Science 312, 758–762 (2006)

    CAS  Article  ADS  PubMed  Google Scholar 

  37. Robinson, D. A. Integrating with neurons. Annu. Rev. Neurosci. 12, 33–45 (1989)

    CAS  PubMed  Article  Google Scholar 

  38. Major, G. & Tank, D. Persistent neural activity: prevalence and mechanisms. Curr. Opin. Neurobiol. 14, 675–684 (2004)

    CAS  PubMed  Article  Google Scholar 

  39. Pastor, A. M., De la Cruz, R. R. & Baker, R. Eye position and eye velocity integrators reside in separate brainstem nuclei. Proc. Natl Acad. Sci. USA 91, 807–811 (1994)

    CAS  PubMed  Article  ADS  PubMed Central  Google Scholar 

  40. Aksay, E., Gamkrelidze, G., Seung, H. S., Baker, R. & Tank, D. W. In vivo intracellular recording and perturbation of persistent activity in a neural integrator. Nat. Neurosci. 4, 184–193 (2001)

    CAS  PubMed  Article  Google Scholar 

  41. Mizumori, S. J. & Williams, J. D. Directionally selective mnemonic properties of neurons in the lateral dorsal nucleus of the thalamus of rats. J. Neurosci. 13, 4015–4028 (1993)

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  42. McNaughton, B. L., Chen, L. L. & Markus, E. J. ‘Dead reckoning,’ landmark learning, and the sense of direction: a neurophysiological and computational hypothesis. J. Cog. Neurosci. 3, 190–202 (1991)

    CAS  Article  Google Scholar 

  43. Nern, A., Pfeiffer, B. D. & Rubin, G. M. Optimized tools for multicolor stochastic labeling reveal diverse stereotyped cell arrangements in the fly visual system. Proc. Natl Acad. Sci. USA 112, E2967–E2976 (2015)

    CAS  PubMed  PubMed Central  Article  ADS  Google Scholar 

  44. Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012)

    CAS  PubMed  Article  Google Scholar 

  45. Fisher, N. I. & Lee, A. J. A correlation coefficient for circular data. Biometrika 70, 327–332 (1983)

    MathSciNet  MATH  Article  Google Scholar 

Download references

Acknowledgements

We thank the Ruta and Rubin laboratories for fly stocks, and C. Kirst, C. Lyu, V. Vijayan, L. Fenk, A. Katsov, C. Bargmann, V. Ruta, S. Simon and members of the Maimon laboratory for discussions. Stocks obtained from the Bloomington Drosophila Stock Center (NIH P40OD018537) were used in this study. Stocks obtained from the Vienna Drosophila Resource Center were used in this study. Research reported in this publication was supported by the New York Stem Cell Foundation (NYSCF-R-NI13), Searle Scholars Foundation (12-SSP-153), McKnight Foundation, and the National Institute on Drug Abuse of the NIH (DP2DA035148). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

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Authors and Affiliations

Authors

Contributions

J.G., A.A. and G.M. designed the experiments. J.G. performed, analysed and interpreted all physiological experiments, with input from G.M. A.A. performed and analysed all immunohistochemistry experiments. K.K.S. and J.G. fabricated parts for the experimental setup. J.D.H. developed the pipeline for image registration and rendered the sample videos. P.S.M. adapted FicTrac to our closed-loop setup. J.G. and G.M. wrote the paper.

Corresponding author

Correspondence to Gaby Maimon.

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The authors declare no competing financial interests.

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Reviewer Information Nature thanks A. Cheung, R. Ritzmann and the other anonymous reviewer(s) for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Figure 1 Processing of protocerebral bridge signals from E-PG, P-EN1 and P-EN2 neurons in the presence of a closed-loop bar.

a, Each z slice, averaged over an entire E-PG>GCaMP6m recording, with glomeruli outlined. b, Processing of the EPG>GCaMP6m signal to generate the plot in Fig. 1g. From left to right: raw mean signal in each glomerulus over time, z-score normalization for each glomerulus independently, ∆F/F normalization for each glomerulus independently, power spectrum of the ∆F/F signal computed for each time point (row) independently. The E-PG phase extracted from the Fourier component with a period of eight glomeruli of the ∆F/F bridge signal is overlaid on each GCaMP plot in black. c, E-PG phase (blue) shifted with a constant offset to best match the bar position (dark grey). d, e, Same as a, b but for P-EN1 neurons originally plotted in Fig. 1h. That P-EN cells do not innervate the middle two glomeruli of the bridge slightly complicates the power spectrum analysis. Specifically, black arrows highlight transient peaks in the power spectrum at approximately 16 glomeruli, which are artefacts of the P-EN GCaMP peaks crossing the centre of the bridge. f, From left to right: P-EN1 ∆F/F signal with the middle two glomeruli filled in by averaging signals located one period (8 glomeruli) away, power spectrum of the ‘filled in’ ∆F/F bridge signal (note absence of artefactual peaks at 16 glomeruli), P-EN phase extracted from the Fourier component with a period of 8 glomeruli of the ‘filled in’ ∆F/F bridge signal (orange), shifted with a constant offset to best match the bar position (dark grey). gi, Same as df but for P-EN2 neurons originally plotted in Fig. 1i. In all plots showing bar position over time, the gap in the arena where the bar is not displayed is shown in grey. jl, Periodicity of the bridge signal at peak power in the power spectrum for each cell type. Each circle represents one fly. The mean and s.d. are shown. mo, Offsets that minimize the distance between GCaMP phase and bar position for all 50-s trials for each cell type. Only data for which the bar was visible were included in computing the offsets. In o, fly 10 had only three bar trials. See Methods for details. a.u., arbitrary units; DFT, discrete Fourier transform.

Extended Data Figure 2 Processing of protocerebral bridge signals from E-PG, P-EN1 and P-EN2 neurons in the dark.

a, Processing of the E-PG>GCaMP6m signal to generate the plot in Fig. 2g. Bridge signals are plotted over time as in Extended Data Fig. 1b, but in the dark. b, Phase from the ∆F/F signal and ball position. Because the phase and ball position drift over time in the dark, we did not align the two signals by finding the best offset over the entire trial; rather, we nulled the offset between the GCaMP phase and ball heading at time zero, letting the signals drift naturally over time. For display purposes, we applied a constant gain to the ball position signal, which we determined from the slope of a linear regression between the GCaMP phase and ball velocity. c, d, P-EN1 signals (originally plotted in Fig. 4a) over time as in Extended Data Fig. 1e, f, but in the dark. e, f, Same as c, d, but for P-EN2 signals (originally plotted in Fig. 4b). The ball position gains are 0.75 for E-PG (b), 1.0 for P-EN1 (d) and 0.89 for P-EN2 (f). For P-EN1, the slope of the linear regression between phase and ball velocity was poorly estimated (see Supplementary Discussion) and thus we hand-picked the gain (1.0) in this case. That these gains are not all equal does not mean that each cell type has its own gain (see Supplementary Discussion). Note the different timescale compared to Extended Data Fig. 1. Also note that the time window was expanded in a, b, compared to Fig. 2g, to be the same length as in cf. gi, Periodicity of the bridge signal at peak power in the power spectrum for each cell type. Each circle represents one fly. The mean and s.d. are shown.

Extended Data Figure 3 Example visual tuning curves in E-PG, P-EN1 and P-EN2 neurons across glomeruli in the protocerebral bridge.

ac, Tuning curves of GCaMP activity as a function of bar position for each glomerulus in a sample fly for E-PGs (a), P-EN1s (b) and P-EN2s (c). Data associated with bar positions in the 90° gap in the back of the arena (not visible) are not shown. The mean and s.d. across time points for each 22.5° bar position bin are shown.

Extended Data Figure 4 P-EN neuroanatomy: explanation for the numbering scheme, sytGFP localization, and multicolour single cell labelling.

a, Numbering used in the literature for the protocerebral bridge and ellipsoid body. b, Rearrangement of the left and right bridges and a linearized ellipsoid body that highlights the pattern of anatomical projections for E-PGs and P-ENs. Arrows indicate the expected direction of signalling (dendrite to axon) for each cell21,22 (also see e, f). The dashed line in a shows where the ellipsoid body is opened to display it linearly. Tile 1 is repeated as a visual aid, as the ellipsoid body is circular. c, d, Same as a, b but using a modified numbering scheme. In d, the numbers are constant along each column (with the exception that glomerulus 9 from either side of the bridge matches up with ellipsoid body tile 1), highlighting the fact that E-PGs project within the same column, whereas left-bridge P-ENs project to the right (+1, or clockwise) and right-bridge P-ENs project to the left (−1, or anticlockwise). e, f, Sample images of synaptotagmin–GFP (sytGFP, labelling putative axonal terminals) and tdTomato (labelling the entire cell) expressed in P-EN1 (e) and P-EN2 (f) neurons. These data are consistent with P-ENs having extensive presynaptic terminals in the ellipsoid body and noduli but few in the protocerebral bridge. gl, Sample multicolour flip-out images for P-EN neurons driven by VT032906–Gal4 (P-EN1, g, h), VT020739–Gal4 (P-EN2, i, j), and 12D09–Gal4 (P-EN2, k, l). The multicolour flip-out method41 allows one to visualize single randomly selected cells from a Gal4 driver line (which might label a dense thicket of cells) in their entirety, like a multicoloured Golgi stain. The neuropil is shown in grey. Single neurons are coloured. Glomerulus numbers, including L for left and R for right, are shown in the bridge. After tracing each neuron from the bridge to the ellipsoid body, we labelled the terminals in the ellipsoid body with the bridge glomerulus from which they originated, using our revised numbering scheme (c, d). VT032906–Gal4 stains a neuron type that passes near the bridge, but does not innervate the bridge, ellipsoid body or noduli (for example, the green neuron in g). VT020739–Gal4 stains a neuron type that innervates the noduli, but not the ellipsoid body or bridge (for example, the blue neurons innervating the noduli from the sides in j). Virtually all neurons labelled in the bridge and ellipsoid body were consistent with P-ENs (see Supplementary Information Table 1). 12D09–Gal4 very rarely revealed flip-outs of protocerebral bridge local neurons, not shown here (see Supplementary Information Table 1). eb, ellipsoid body; no, noduli; pb, protocerebral bridge.

Extended Data Figure 5 P-EN1 and P-EN2 bridge asymmetry during turns in closed-loop bar and dark conditions, computed with z-score and ∆F/F normalization.

ac, Right–left bridge activity (bottom) and the fly’s turning velocity (top), averaged over multiple turns, for P-EN1s (a), P-EN2s (b) and E-PGs (c), as in Fig. 2h–j, in closed-loop bar conditions. The right–left GCaMP signal is computed from z-score normalized data. df, Same as ac but in constant darkness. gl, Same as af, except that the right–left GCaMP signal is computed from ∆F/F normalized data. The mean and s.e.m. across turns are shown. Only data for which the bar was visible on the front 270° of the LED arena were included for closed-loop bar plots. See Methods for details.

Extended Data Figure 6 P-EN1 and P-EN2 asymmetries are driven in part by optic flow.

a, Sample trajectory of one of hundreds of dots used to create our optic flow stimulus. Each dot appeared at a random location, travelled 4 azimuthal pixels (7.5°), and then disappeared. The dashed circle is drawn as a point of reference, and was not presented on the screen. b, c, Right–left P-EN1 bridge activity during open-loop optic flow to the left (b) and to the right (c) at 45°/s (left column) and 90°/s (right column) during trials in which the fly did not, on average, turn ( ± 10°/s) in response to the optic flow stimulus. d, e, Same as b, c but for P-EN2 neurons. fi, Same as be, except that trials were included only if the fly turned with the direction of optic flow (>10°/s in the direction of optic flow). The mean and s.e.m. across trials are shown. For display, the stimulus position was nulled at time zero to highlight the movement of the stimulus. In trials in which flies turned with the direction of optic flow, the direction of visual motion experienced on their retinas was opposite to that expected from their own turning behavior. That is, the visual optic flow inputs (presented in open loop) indicated an angular velocity with the opposite sign to that indicated by proprioceptive/efference-copy inputs. The fact that we observe a weaker asymmetry in fi compared to be, argues that optic flow and proprioceptive/efference-copy inputs are combined to generate the P-EN bridge asymmetry.

Extended Data Figure 7 Co-labelling of P-EN1 and P-EN2 driver lines.

ac, Maximum z-projection of a brain with 12D09-driven neurons expressing GFP and VT032906-driven neurons expressing tdTomato. a, GFP (12D09) signal. b, tdTomato (VT032906) signal. c, Composite of a and b. Physiological experiments suggest that VT032906 primarily labels P-EN1 neurons, whereas 12D09 primarily labels P-EN2 neurons (Fig. 3, Extended Data Fig. 8b, d). As expected, most P-EN neurons are primarily labelled by one of the two drivers, but some neurons are labelled by both (examples denoted with asterisks). df, Same as ac but with VT020739-driven neurons expressing tdTomato. Physiological experiments suggest that both 12D09 and VT020739 primarily label P-EN2 neurons (Fig. 3, Extended Data Fig. 8c, d). As expected, almost all labelled P-EN neurons are labelled by both P-EN2 drivers. P-ENs often showed fluorescent signals whose strength varied across glomeruli, which could reflect varying innervation densities across the bridge. They could also reflect incomplete targeting of P-ENs by our driver lines.

Extended Data Figure 8 Simultaneous imaging of the protocerebral bridge and ellipsoid body for each cell type separately and dual-colour imaging of GCaMP6f and jRGECO1a in E-PGs in the ellipsoid body.

a, We imaged the bridge and ellipsoid body in the same fly, at the same time, using a tall z-stack that encompassed both structures, to determine the relationship between the signals measured in each structure. b, Phase-nulled P-EN1 signals measured in the bridge (orange) and ellipsoid body (grey). The signals measured in the bridge were replotted onto the ellipsoid body using the P-EN projection pattern. c, d, Same as b but for P-EN2 signals from VT020739-Gal4 (c) and 12D09-Gal4 (d). e, As in a but for imaging E-PGs, with the bridge in blue. f, Same as b for E-PGs, with the left and right bridge in blue. In bd and f, the mean and s.e.m. across flies are shown (in f, the s.e.m. for the bridge curves (blue) are omitted for clarity). Both the bridge and ellipsoid body signals were nulled using the ellipsoid body phase. Note that the positions of the left- and right-bridge peaks are inverted between P-EN1 and P-EN2. These results are consistent with the dual-imaging experiments in Fig. 3, and support the idea that the results in Fig. 3 were not due to crosstalk between the red and green channels. g, Schematic illustrating imaging from the ellipsoid body. hj, Phase-nulled ellipsoid body signals of GCaMP6f and jRGECO1a co-expressed in E-PGs, computed for when the fly turned left (h, −300°/s), walked straight (i, 0°/s) or turned right (j, +300°/s), 300 ms before the calcium signal, as in Fig. 3k–p. The mean and s.e.m. across flies are shown. We observed no consistent, strong asymmetries in the jRGECO1a and GCaMP6f signals during left or right turns when both indicators were expressed in E-PGs. These data argue that the asymmetries we observed in dual imaging of P-ENs and E-PGs in the ellipsoid body (Fig. 3m–p) were not an artefact of indicator kinetics. Data are averaged over bar and dark conditions.

Extended Data Figure 9 Analysis of ellipsoid body asymmetry in P-EN1s and P-EN2s relative to E-PGs in the ellipsoid body.

a, Mean E-PG and P-EN1 activity in the ellipsoid body triggered on when the fly was turning to the left (−300°/s, upper panel) or right (+300°/s, lower panel), as in Fig. 3m–p, but over time. The P-EN1 and E-PG signals were phase-nulled using the E-PG phase. b, Same as a but for P-EN2 activity. c, d, When analysing the two-colour imaging experiments in Fig. 3i–p, we calculated the cross correlation between the ellipsoid body asymmetry in P-EN1 (c) or P-EN2 (d) and the E-PG phase velocity in the ellipsoid body. A positive correlation indicates an increased P-EN signal in the direction in which the E-PG peak is moving. A positive lag indicates that the P-EN asymmetry comes after the change in the E-PG phase. Thus, the P-EN1 peak tends to lead the E-PG peak whereas the P-EN2 peak tends to lag behind the E-PG peak. Note that we also observed a smaller, negative, late peak in the signal driven by the P-EN1 Gal4 and a smaller positive, early peak in the signal driven by the P-EN2 Gal4, suggesting that each Gal4 line contains some number of both P-EN1 and P-EN2 cells, but with more of one than the other. e, f, Same as c, d, except that the P-EN ellipsoid body asymmetry is correlated with the fly’s turning velocity. A positive lag indicates that the P-EN asymmetry comes after the fly turns. Arrows indicate the lag where the mean correlation was greatest. In cf, thin lines represent single flies and thick lines represent the mean across flies. Data are averaged over bar and dark conditions.

Extended Data Figure 10 The effects of blocking P-EN synaptic transmission on the ability of E-PGs to track a landmark, and controls for the P2X2 experiments.

a, b, E-PG activity in the bridge from the same fly as in Fig. 4e, f (P-EN1>shibirets), except with a closed-loop bar, at 22 °C (a) and 32 °C (b). c, Correlations between phase and bar velocity, for three P-EN-Gal4 lines driving shibirets, with parental controls. Each circle represents one fly. The mean and s.e.m. across flies are shown. d, Same as c, but plotting circular correlations between phase and bar position. Only data in which the bar was visible in the front 270° of the arena were used for calculating correlations. Trials during which the bar was visible for less than 10 s were excluded, with some flies having no trials passing this criterion. The total number of flies (without excluding flies that did not pass the above criterion) for each genotype is shown. The mean and s.e.m. across included flies are shown. Only VT020739–Gal4 seems to affect the ability of the E-PG signal to track a visual landmark, suggesting that perhaps this effect is due to non-P-EN neurons targeted by this line, for example visual lobe neurons, or that this effect requires stronger Gal4 expression in P-ENs in this line. e, The change in the phase-nulled E-PG>GCaMP6f and ATP (Alexa594) signals during an ATP pulse, with P-EN1s expressing P2X2. We subtracted the average E-PG signal at −0.3 to 0.0 s from the average at 0.7 to 1.0 s with respect to the time of the pressure pulse, highlighting the effect of the stimulation. We subtracted the average Alexa594 (ATP) signal at −0.3 to 0.0 s from the average immediately (1 frame) after stimulation. The irregular dips in the E-PG signal are due to the fact that the E-PG phase was not uniformly distributed immediately before stimulation. Both signals were phase-nulled using the position of the pipette. f, Same as e, but without ATP in the same flies. g, h, Same as e, f, but with P-EN2s expressing P2X2. i, j, Same as e, f but with no Gal4 as a control for the specificity of P2X2 expression. In ej, thin lines represent single flies, and thick lines represent the means across flies.

Supplementary information

Supplementary Information

This file contains the Supplementary Discussion and Supplementary Table 1. (PDF 369 kb)

Sample raw GCaMP6f signals from E-PG neurons in the protocerebral bridge shown as the fly performs tethered walking behaviour (E-PG>GCaMP6f raw 1.00x fixation Sample)

Sample raw GCaMP6f signals from E-PG neurons in the protocerebral bridge are shown as the fly performs tethered walking behaviour. The fly is controlling the azimuthal position of a bright blue bar in closed loop during this experiment, and tends to maintain its heading within this virtual environment such that the bar remains in front of the fly. Note that the 2-3 peaks of GCaMP signal in the protocerebral bridge accurately track the position of the bar over time. Shown is the maximum z-projection across 3 z-slices. The resulting z-projection was smoothed with a 0.65-pixel (~0.5 µm) gaussian. Since the fly is displayed from the front, the right bridge is displayed on the left, and the left bridge on the right, contrary to all Figures and Extended Data Figures. The video is played at real time-speed. (MP4 1101 kb)

Sample raw GCaMP6m signals from E-PG neurons in the protocerebral bridge shown as the fly performs tethered walking behaviour (E-PG>GCaMP6m_raw_1.00x Sample)

Sample raw GCaMP6m signals from E-PG neurons in the protocerebral bridge are shown as the fly performs tethered walking behaviour. The fly is controlling the azimuthal position of a bright blue bar in closed loop during this experiment. Note that the 2-3 peaks of GCaMP signal in the protocerebral bridge accurately track the position of the bar over time. Shown is the maximum z-projection across 3 z-slices. The resulting z-projection was smoothed with a 0.65-pixel (~0.5 µm) gaussian. Since the fly is displayed from the front, the right bridge is displayed on the left, and the left bridge on the right, contrary to all Figures and Extended Data Figures. The video is played at real time-speed. (MP4 1575 kb)

Sample raw GCaMP6m signals from E-PG neurons in the protocerebral bridge shown as the fly performs tethered walking behaviour (E-PG>GCaMP6m_raw_0.50x Sample)

Sample raw GCaMP6m signals from E-PG neurons in the protocerebral bridge are shown as the fly performs tethered walking behaviour. The fly is controlling the azimuthal position of a bright blue bar in closed loop during this experiment. Note that the 2-3 peaks of GCaMP signal in the protocerebral bridge accurately track the position of the bar over time. Shown is the maximum z-projection across 3 z-slices. The resulting z-projection was smoothed with a 0.65-pixel (~0.5 µm) gaussian. Since the fly is displayed from the front, the right bridge is displayed on the left, and the left bridge on the right, contrary to all Figures and Extended Data Figures. The video is played at half-speed. (MP4 1970 kb)

Sample raw GCaMP6m signals from P-EN1 neurons in the protocerebral bridge shown as the fly performs tethered walking behaviour (P-EN1>GCaMP6m_raw_1.00x Sample)

Sample raw GCaMP6m signals from P-EN1 neurons in the protocerebral bridge are shown as the fly performs tethered walking behaviour. The fly is controlling the azimuthal position of a bright blue bar in closed loop during this experiment. Note that the 2-3 peaks of GCaMP signal in the protocerebral bridge are only strongly active when the fly turns, and that these peaks are asymmetrically active during these turns. In this video, the fly expresses only one copy of VT032906-Gal4 and UAS-GCaMPm, compared to the two copies of each used in generating Figs. 1-2. The P-EN1 signal in the single copy flies highlights the transient nature of P-EN1 activity during turns. Shown is the maximum z-projection across 3 z-slices. The resulting z-projection was smoothed with a 0.65-pixel (~0.5 µm) gaussian. Since the fly is displayed from the front, the right bridge is displayed on the left, and the left bridge on the right, contrary to all Figures and Extended Data Figures. The video is played at real time-speed. (MP4 2011 kb)

Sample raw GCaMP6m signals from P-EN1 neurons in the protocerebral bridge shown as the fly performs tethered walking behaviour (P-EN1>GCaMP6m_raw_0.50x Sample)

Sample raw GCaMP6m signals from P-EN1 neurons in the protocerebral bridge are shown as the fly performs tethered walking behaviour. The fly is controlling the azimuthal position of a bright blue bar in closed loop during this experiment. Note that the 2-3 peaks of GCaMP signal in the protocerebral bridge are only strongly active when the fly turns, and that these peaks are asymmetrically active during these turns. In this video, the fly expresses only one copy of VT032906-Gal4 and UAS-GCaMPm, compared to the two copies of each used in generating Figs. 1-2. The P-EN1 signal in the single copy flies highlights the transient nature of P-EN1 activity during turns. Shown is the maximum z-projection across 3 z-slices. The resulting z-projection was smoothed with a 0.65-pixel (~0.5 µm) gaussian. Since the fly is displayed from the front, the right bridge is displayed on the left, and the left bridge on the right, contrary to all Figures and Extended Data Figures. The video is played at half-speed. (MP4 2408 kb)

Sample raw GCaMP6m signals from P-EN2 neurons in the protocerebral bridge shown as the fly performs tethered walking behaviour (P-EN2>GCaMP6m_raw_1.00x Sample)

Sample raw GCaMP6m signals from P-EN2 neurons in the protocerebral bridge are shown as the fly performs tethered walking behaviour. The fly is controlling the azimuthal position of a bright blue bar in closed loop during this experiment. Note that the 2-3 peaks of GCaMP signal in the protocerebral bridge accurately track the position of the bar over time. The turn-related asymmetries are not as evident in the raw P-EN2 videos as they are in the P-EN1 videos - they are, however, evident from a quantitative analysis of the P-EN2 signals (Fig. 2-3). Shown is the maximum z-projection across 3 z-slices. The resulting z-projection was smoothed with a 0.65-pixel (~0.5 µm) gaussian. Since the fly is displayed from the front, the right bridge is displayed on the left, and the left bridge on the right, contrary to all Figures and Extended Data Figures. The video is played at real time-speed. (MP4 1918 kb)

Sample raw GCaMP6m signals from P-EN2 neurons in the protocerebral bridge shown as the fly performs tethered walking behaviour (P-EN2>GCaMP6m_raw_0.50x Sample)

Sample raw GCaMP6m signals from P-EN2 neurons in the protocerebral bridge are shown as the fly performs tethered walking behaviour. The fly is controlling the azimuthal position of a bright blue bar in closed loop during this experiment. Note that the 2-3 peaks of GCaMP signal in the protocerebral bridge accurately track the position of the bar over time. The turn-related asymmetries are not as evident in the raw P-EN2 videos as they are in the P-EN1 videos - they are, however, evident from a quantitative analysis of the P-EN2 signals (Fig. 2-3). Shown is the maximum z-projection across 3 z-slices. The resulting z-projection was smoothed with a 0.65-pixel (~0.5 µm) gaussian. Since the fly is displayed from the front, the right bridge is displayed on the left, and the left bridge on the right, contrary to all Figures and Extended Data Figures. The video is played at half-speed. (MP4 2384 kb)

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Green, J., Adachi, A., Shah, K. et al. A neural circuit architecture for angular integration in Drosophila. Nature 546, 101–106 (2017). https://doi.org/10.1038/nature22343

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