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.

Asymmetry of Drosophila ON and OFF motion detectors enhances real-world velocity estimation

This article has been updated

Abstract

The reliable estimation of motion across varied surroundings represents a survival-critical task for sighted animals. How neural circuits have adapted to the particular demands of natural environments, however, is not well understood. We explored this question in the visual system of Drosophila melanogaster. Here, as in many mammalian retinas, motion is computed in parallel streams for brightness increments (ON) and decrements (OFF). When genetically isolated, ON and OFF pathways proved equally capable of accurately matching walking responses to realistic motion. To our surprise, detailed characterization of their functional tuning properties through in vivo calcium imaging and electrophysiology revealed stark differences in temporal tuning between ON and OFF channels. We trained an in silico motion estimation model on natural scenes and discovered that our optimized detector exhibited differences similar to those of the biological system. Thus, functional ON-OFF asymmetries in fly visual circuitry may reflect ON-OFF asymmetries in natural environments.

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

Relevant articles

Open Access articles citing this article.

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Figure 1: Flies stabilize their path in naturalistic environments using a combination of ON and OFF motion detectors.
Figure 2: ON and OFF channels are equally capable of estimating the velocity of natural scenes.
Figure 3: Physiological characterization of ON and OFF channels reveals tuning asymmetries.
Figure 4: Asymmetry between ON and OFF channels persists at the behavioral level.
Figure 5: ON-OFF detector models optimized for velocity estimation in natural scenes are tuned asymmetrically.
Figure 6: Luminance asymmetry in natural scenes is critically responsible for asymmetry of ON-OFF parameters in optimized motion detector.
Figure 7: LPTCs are sensitive to higher order correlation stimuli.
Figure 8: Behavioral sensitivity to higher order correlations depends on T4 and T5 and is predicted by an asymmetric ON-OFF model.

Change history

  • 07 March 2016

    In the version of this article initially published online, the second and third authors of ref. 40, J.D. Seelig and M.B. Reiser, were replaced by the second author of ref. 39, A. Borst. The error has been corrected for the print, PDF and HTML versions of this article.

References

  1. Borst, A. Fly visual course control: behavior, algorithms and circuits. Nat. Rev. Neurosci. 15, 590–599 (2014).

    Article  CAS  PubMed  Google Scholar 

  2. Ruderman, D.L. & Bialek, W. Statistics of natural images: Scaling in the woods. Phys. Rev. Lett. 73, 814–817 (1994).

    Article  CAS  PubMed  Google Scholar 

  3. Simoncelli, E.P. & Olshausen, B.A. Natural image statistics and neural representation. Annu. Rev. Neurosci. 24, 1193–1216 (2001).

    Article  CAS  PubMed  Google Scholar 

  4. Field, D.J. Relations between the statistics of natural images and the response properties of cortical cells. J. Opt. Soc. Am. A 4, 2379–2394 (1987).

    Article  CAS  PubMed  Google Scholar 

  5. Laughlin, S. A simple coding procedure enhances a neuron's information capacity. Z. Naturforsch. C 36, 910–912 (1981).

    Article  CAS  PubMed  Google Scholar 

  6. van Hateren, J.H. & van der Schaaf, A. Independent component filters of natural images compared with simple cells in primary visual cortex. Proc. Biol. Sci. 265, 359–366 (1998).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Yu, Y., Schmid, A.M. & Victor, J.D. Visual processing of informative multipoint correlations arises primarily in V2. eLife 4, e06604 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  8. Gjorgjieva, J., Sompolinsky, H. & Meister, M. Benefits of pathway splitting in sensory coding. J. Neurosci. 34, 12127–12144 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Borst, A. & Helmstaedter, M. Common circuit design in fly and mammalian motion vision. Nat. Neurosci. 18, 1067–1076 (2015).

    Article  CAS  PubMed  Google Scholar 

  10. Eichner, H., Joesch, M., Schnell, B., Reiff, D.F. & Borst, A. Internal structure of the fly elementary motion detector. Neuron 70, 1155–1164 (2011).

    Article  CAS  PubMed  Google Scholar 

  11. Hassenstein, B. & Reichardt, W. Systemtheoretische Analyse der Zeit-, Reihenfolgen- und Vorzeichenauswertung bei der Bewegungsperzeption des Rüsselkäfers Chlorophanus. Z. Naturforsch. B 11, 513–524 (1956).

    Article  Google Scholar 

  12. Clark, D.A. et al. Flies and humans share a motion estimation strategy that exploits natural scene statistics. Nat. Neurosci. 17, 296–303 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Ratliff, C.P., Borghuis, B.G., Kao, Y.-H., Sterling, P. & Balasubramanian, V. Retina is structured to process an excess of darkness in natural scenes. Proc. Natl. Acad. Sci. USA 107, 17368–17373 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Fitzgerald, J.E., Katsov, A.Y., Clandinin, T.R. & Schnitzer, M.J. Symmetries in stimulus statistics shape the form of visual motion estimators. Proc. Natl. Acad. Sci. USA 108, 12909–12914 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Hu, Q. & Victor, J.D. A set of high-order spatiotemporal stimuli that elicit motion and reverse-phi percepts. J. Vis. 10, 9.1–9.16 (2010).

    Article  Google Scholar 

  16. Takemura, S.-Y. et al. A visual motion detection circuit suggested by Drosophila connectomics. Nature 500, 175–181 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Shinomiya, K. et al. Candidate neural substrates for off-edge motion detection in Drosophila. Curr. Biol. 24, 1062–1070 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Joesch, M., Schnell, B., Raghu, S.V., Reiff, D.F. & Borst, A. ON and OFF pathways in Drosophila motion vision. Nature 468, 300–304 (2010).

    Article  CAS  PubMed  Google Scholar 

  19. Clark, D.A., Bursztyn, L., Horowitz, M.A., Schnitzer, M.J. & Clandinin, T.R. Defining the computational structure of the motion detector in Drosophila. Neuron 70, 1165–1177 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Silies, M. et al. Modular use of peripheral input channels tunes motion-detecting circuitry. Neuron 79, 111–127 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Maisak, M.S. et al. A directional tuning map of Drosophila elementary motion detectors. Nature 500, 212–216 (2013).

    Article  CAS  PubMed  Google Scholar 

  22. Meier, M. et al. Neural circuit components of the Drosophila OFF motion vision pathway. Curr. Biol. 24, 385–392 (2014).

    Article  CAS  PubMed  Google Scholar 

  23. Ammer, G., Leonhardt, A., Bahl, A., Dickson, B.J. & Borst, A. Functional specialization of neural input elements to the Drosophila ON motion detector. Curr. Biol. 25, 2247–2253 (2015).

    Article  CAS  PubMed  Google Scholar 

  24. Behnia, R., Clark, D.A., Carter, A.G., Clandinin, T.R. & Desplan, C. Processing properties of ON and OFF pathways for Drosophila motion detection. Nature 512, 427–430 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Haikala, V., Joesch, M., Borst, A. & Mauss, A.S. Optogenetic control of fly optomotor responses. J. Neurosci. 33, 13927–13934 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Schnell, B., Weir, P.T., Roth, E., Fairhall, A.L. & Dickinson, M.H. Cellular mechanisms for integral feedback in visually guided behavior. Proc. Natl. Acad. Sci. USA 111, 5700–5705 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Bahl, A., Ammer, G., Schilling, T. & Borst, A. Object tracking in motion-blind flies. Nat. Neurosci. 16, 730–738 (2013).

    Article  CAS  PubMed  Google Scholar 

  28. Joesch, M., Plett, J., Borst, A. & Reiff, D.F. Response properties of motion-sensitive visual interneurons in the lobula plate of Drosophila melanogaster. Curr. Biol. 18, 368–374 (2008).

    Article  CAS  PubMed  Google Scholar 

  29. Warzecha, A.-K. & Egelhaaf, M. Intrinsic properties of biological motion detectors prevent the optomotor control system from getting unstable. Phil. Trans. R. Soc. Lond. B 351, 1579–1591 (1996).

    Article  Google Scholar 

  30. Schnell, B., Raghu, S.V., Nern, A. & Borst, A. Columnar cells necessary for motion responses of wide-field visual interneurons in Drosophila. J. Comp. Physiol. A Neuroethol. Sens. Neural Behav. Physiol. 198, 389–395 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  31. Brand, A.H. & Perrimon, N. Targeted gene expression as a means of altering cell fates and generating dominant phenotypes. Development 118, 401–415 (1993).

    CAS  PubMed  Google Scholar 

  32. Sweeney, 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 14, 341–351 (1995).

    Article  CAS  PubMed  Google Scholar 

  33. Straw, A.D., Rainsford, T. & O'Carroll, D.C. Contrast sensitivity of insect motion detectors to natural images. J. Vis. 8, 32.1–32.9 (2008).

    Article  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Mauss, A.S. et al. Neural circuit to integrate opposing motions in the visual field. Cell 162, 351–362 (2015).

    Article  CAS  PubMed  Google Scholar 

  36. Tuthill, J.C., Chiappe, M.E. & Reiser, M.B. Neural correlates of illusory motion perception in Drosophila. Proc. Natl. Acad. Sci. USA 108, 9685–9690 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Dror, R.O., O'Carroll, D.C. & Laughlin, S.B. Accuracy of velocity estimation by Reichardt correlators. J. Opt. Soc. Am. A Opt. Image Sci. Vis. 18, 241–252 (2001).

    Article  CAS  PubMed  Google Scholar 

  38. Theobald, J.C., Duistermars, B.J., Ringach, D.L. & Frye, M.A. Flies see second-order motion. Curr. Biol. 18, R464–R465 (2008).

    Article  CAS  PubMed  Google Scholar 

  39. Jung, S. N., Borst, A. & Haag, J. Flight activity alters velocity tuning of fly motion-sensitive neurons. J. Neurosci. 31, 9231–9237 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Chiappe, M.E., Seelig, J.D., Reiser, M.B. & Jayaraman, V. Walking modulates speed sensitivity in Drosophila motion vision. Curr. Biol. 20, 1470–1475 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Dyakova, O., Lee, Y.-J., Longden, K.D., Kiselev, V.G. & Nordström, K. A higher order visual neuron tuned to the spatial amplitude spectra of natural scenes. Nat. Commun. 6, 8522 (2015).

    Article  CAS  PubMed  Google Scholar 

  42. Komban, S.J. et al. Neuronal and perceptual differences in the temporal processing of darks and lights. Neuron 82, 224–234 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Chichilnisky, E.J. & Kalmar, R.S. Functional asymmetries in ON and OFF ganglion cells of primate retina. J. Neurosci. 22, 2737–2747 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Pandarinath, C., Victor, J.D. & Nirenberg, S. Symmetry breakdown in the ON and OFF pathways of the retina at night: functional implications. J. Neurosci. 30, 10006–10014 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Carandini, M. & Heeger, D.J. Normalization as a canonical neural computation. Nat. Rev. Neurosci. 13, 51–62 (2012).

    Article  CAS  Google Scholar 

  46. Adelson, E.H. & Bergen, J.R. Spatiotemporal energy models for the perception of motion. J. Opt. Soc. Am. A 2, 284–299 (1985).

    Article  CAS  PubMed  Google Scholar 

  47. van Santen, J.P. & Sperling, G. Elaborated Reichardt detectors. J. Opt. Soc. Am. A 2, 300–321 (1985).

    Article  CAS  PubMed  Google Scholar 

  48. Schwegmann, A., Lindemann, J.P. & Egelhaaf, M. Depth information in natural environments derived from optic flow by insect motion detection system: a model analysis. Front. Comput. Neurosci. 8, 83 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  49. Schilling, T. & Borst, A. Local motion detectors are required for the computation of expansion flow-fields. Biol. Open 4, 1105–1108 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  50. Burge, J. & Geisler, W.S. Optimal speed estimation in natural image movies predicts human performance. Nat. Commun. 6, 7900 (2015).

    Article  CAS  PubMed  Google Scholar 

  51. Yu, J.Y., Kanai, M.I., Demir, E., Jefferis, G.S.X.E. & Dickson, B.J. Cellular organization of the neural circuit that drives Drosophila courtship behavior. Curr. Biol. 20, 1602–1614 (2010).

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

A. Nern and G.M. Rubin (Janelia Research Campus) generated and kindly provided the splitGal4 line targeting T4 and T5. We are grateful for fly work and behavioral experiments performed by R. Kutlesa, C. Theile and W. Essbauer. We thank A. Arenz and A. Mauss for carefully reading the manuscript, T. Schilling for fly illustrations, and all of the members of the Borst laboratory for extensive discussions. The Bernstein Center for Computational Neuroscience Munich supplied computing resources for our simulations. A.L., G.A., M.M., E.S., A. Bahl and A. Borst are members of the Graduate School for Systemic Neurosciences, Munich.

Author information

Authors and Affiliations

Authors

Contributions

A.L., G.A. and A. Borst designed the study. A.L. performed behavioral experiments, associated data analysis and all modeling work. G.A., M.M. and E.S. performed electrophysiological experiments. G.A. performed calcium imaging. A.L. and G.A. analyzed physiological data. A. Bahl designed the behavioral apparatuses and performed behavioral experiments. A.L. wrote the manuscript with help from all of the authors.

Corresponding author

Correspondence to Aljoscha Leonhardt.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Auxiliary data for Gal4 lines used throughout the study.

(a-d) UAS-mCD8GFP or UAS-GCaMP6f were driven by Gal4 driver lines used throughout the text and visualized using confocal images of the optic lobe. (a) GFP expression of splitGal4 line labeling T4 and T5. (b) GFP expression of Gal4 line labeling T4. (c) GFP expression of Gal4 line labeling T5. (d) GCaMP6f expression of combined Gal4 line labeling T4 and T5. See Online Methods for Gal4 line names and details of the immunohistochemistry procedures. (e-h) Locomotor integrity for each behavioral experiment was quantified as the mean forward velocity across conditions, with values close to control level indicating a general ability to respond to visual stimuli. (e) Walking speeds for closed-loop experiments (Fig. 1). (f) Walking speeds for open-loop experiments (Fig. 2). (g) Walking speeds for opposing edge experiments (Fig. 4). (h) Walking speeds for glider experiments (Fig. 8). Dots represent individual flies. Black bars mark the group mean for each genotype.

Supplementary Figure 2 Walking traces for open-loop velocity estimation experiment.

Binned response traces for all genotypes used throughout the stochastic open loop velocity estimation experiment (Fig. 2). In order to generate velocity-specific traces, stimulus velocities were sorted into bins spanning 5° s−1 centered about the value indicated above each column. The corresponding traces were then averaged for each fly. Shaded areas indicate the bootstrapped 68% confidence interval across flies (N as in main figure; Fig. 2). Nota bene, traces were not low-pass filtered and the sampling base for each fly decreases with distance from zero velocity due to the stimulus distribution. The black line in the top leftmost panel indicates the period over which we averaged in order to generate responses for main experiment (Fig. 2). See Online Methods for details. (a) Responses for pooled controls as in main experiment (Fig. 2b). (b-h) Responses for individual genotypes.

Supplementary Figure 3 Bayesian analysis of open-loop behavioral data.

Using open-loop behavioral data (Fig. 2), we generated Bayesian decoders according to the procedure outlined in the Online Methods. For details about quantification and subject numbers, refer to main experiment (Fig. 2). (a) Mapping error across image contrast values, quantified as the root-mean-square error after application to the test data set. With higher contrasts, the quality of the estimate improves; this resembles results based on linear correlation. For T4/T5 block flies, the error stays flat. T4 or T5 block cannot be distinguished from wild-type behavior. (b) Visualization of resulting mapping functions, transforming fly responses into Bayesian estimates of input image velocity. Each line corresponds to a single fly. No significance tests were performed.

Supplementary Figure 4 Physiological edge velocity tuning for fixed starting luminance.

Lobula plate tangential cell responses to ON and OFF edges for equalized initial mean luminance (N=16 by pooling 12 vertical system/4 horizontal system cells). See legend of main experiment (Fig. 3) as well as Online Methods for details. (a) Response traces for edges moving at various velocities. Note that the timescale depends on edge velocity. (b) Quantification of velocity tuning. (c) Quantification of response dynamics (with latency being defined as the time to maximal response during stimulation for onset or time to minimal response after stimulation for offset). (d) Quantification of polarization before and after stimulus presentation. No significance tests were performed.

Supplementary Figure 5 Opposing edge responses for varying stimulus durations.

Presentation and quantification are analogous to main experiment (Fig. 4; see Online Methods and associated legend for details). Depicted flies were T4/T5 control flies. (a-c) Turning responses for edge pulses of 500 ms (N=12), 250 ms (N=12), and 100 ms (N=14) duration, respectively. (d) Quantification of turning responses.

Supplementary Figure 6 Extended data for higher-order motion experiments and simulations.

(a-c) T4 block flies and T5 block flies show 2-point glider responses at control level. (a) Control responses for 2-point gliders of positive or negative parity. (b) Block fly responses. (c) Summary of average turning tendency. Shaded area indicates stimulation period (see Online Methods and legend of main experiment for details; Fig. 8). (d-i) Time- and instantiation-resolved output of the asymmetric detector for converging 3-point gliders. Black traces are arbitrarily scaled detector responses for five random starting conditions of the pattern. (d) Single traces for positive parity. (e) Average time-resolved output for positive parity across 100 instantiations of the stimulus. (f) Low-pass filtered trace from e (first order with time constant of 500 ms followed by multiplicative scaling with a factor of four, approximating the behavioral response). (g) Single traces for negative parity. (h) Average time-resolved output for negative parity across 100 instantiations of the stimulus. (i) Low-pass filtered and scaled trace from h (procedure as in f).

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–6 and Supplementary Tables 1–7 (PDF 1604 kb)

Supplementary Methods Checklist (PDF 626 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Leonhardt, A., Ammer, G., Meier, M. et al. Asymmetry of Drosophila ON and OFF motion detectors enhances real-world velocity estimation. Nat Neurosci 19, 706–715 (2016). https://doi.org/10.1038/nn.4262

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nn.4262

This article is cited by

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