Article | Published:

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

Nature Neuroscience volume 19, pages 706715 (2016) | Download Citation

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.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

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. 1.

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

  2. 2.

    & Statistics of natural images: Scaling in the woods. Phys. Rev. Lett. 73, 814–817 (1994).

  3. 3.

    & Natural image statistics and neural representation. Annu. Rev. Neurosci. 24, 1193–1216 (2001).

  4. 4.

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

  5. 5.

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

  6. 6.

    & Independent component filters of natural images compared with simple cells in primary visual cortex. Proc. Biol. Sci. 265, 359–366 (1998).

  7. 7.

    , & Visual processing of informative multipoint correlations arises primarily in V2. eLife 4, e06604 (2015).

  8. 8.

    , & Benefits of pathway splitting in sensory coding. J. Neurosci. 34, 12127–12144 (2014).

  9. 9.

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

  10. 10.

    , , , & Internal structure of the fly elementary motion detector. Neuron 70, 1155–1164 (2011).

  11. 11.

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

  12. 12.

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

  13. 13.

    , , , & Retina is structured to process an excess of darkness in natural scenes. Proc. Natl. Acad. Sci. USA 107, 17368–17373 (2010).

  14. 14.

    , , & Symmetries in stimulus statistics shape the form of visual motion estimators. Proc. Natl. Acad. Sci. USA 108, 12909–12914 (2011).

  15. 15.

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

  16. 16.

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

  17. 17.

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

  18. 18.

    , , , & ON and OFF pathways in Drosophila motion vision. Nature 468, 300–304 (2010).

  19. 19.

    , , , & Defining the computational structure of the motion detector in Drosophila. Neuron 70, 1165–1177 (2011).

  20. 20.

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

  21. 21.

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

  22. 22.

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

  23. 23.

    , , , & Functional specialization of neural input elements to the Drosophila ON motion detector. Curr. Biol. 25, 2247–2253 (2015).

  24. 24.

    , , , & Processing properties of ON and OFF pathways for Drosophila motion detection. Nature 512, 427–430 (2014).

  25. 25.

    , , & Optogenetic control of fly optomotor responses. J. Neurosci. 33, 13927–13934 (2013).

  26. 26.

    , , , & Cellular mechanisms for integral feedback in visually guided behavior. Proc. Natl. Acad. Sci. USA 111, 5700–5705 (2014).

  27. 27.

    , , & Object tracking in motion-blind flies. Nat. Neurosci. 16, 730–738 (2013).

  28. 28.

    , , & Response properties of motion-sensitive visual interneurons in the lobula plate of Drosophila melanogaster. Curr. Biol. 18, 368–374 (2008).

  29. 29.

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

  30. 30.

    , , & 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).

  31. 31.

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

  32. 32.

    , , , & Targeted expression of tetanus toxin light chain in Drosophila specifically eliminates synaptic transmission and causes behavioral defects. Neuron 14, 341–351 (1995).

  33. 33.

    , & Contrast sensitivity of insect motion detectors to natural images. J. Vis. 8, 32.1–32.9 (2008).

  34. 34.

    et al. Ultrasensitive fluorescent proteins for imaging neuronal activity. Nature 499, 295–300 (2013).

  35. 35.

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

  36. 36.

    , & Neural correlates of illusory motion perception in Drosophila. Proc. Natl. Acad. Sci. USA 108, 9685–9690 (2011).

  37. 37.

    , & Accuracy of velocity estimation by Reichardt correlators. J. Opt. Soc. Am. A Opt. Image Sci. Vis. 18, 241–252 (2001).

  38. 38.

    , , & Flies see second-order motion. Curr. Biol. 18, R464–R465 (2008).

  39. 39.

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

  40. 40.

    , , & Walking modulates speed sensitivity in Drosophila motion vision. Curr. Biol. 20, 1470–1475 (2010).

  41. 41.

    , , , & A higher order visual neuron tuned to the spatial amplitude spectra of natural scenes. Nat. Commun. 6, 8522 (2015).

  42. 42.

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

  43. 43.

    & Functional asymmetries in ON and OFF ganglion cells of primate retina. J. Neurosci. 22, 2737–2747 (2002).

  44. 44.

    , & Symmetry breakdown in the ON and OFF pathways of the retina at night: functional implications. J. Neurosci. 30, 10006–10014 (2010).

  45. 45.

    & Normalization as a canonical neural computation. Nat. Rev. Neurosci. 13, 51–62 (2012).

  46. 46.

    & Spatiotemporal energy models for the perception of motion. J. Opt. Soc. Am. A 2, 284–299 (1985).

  47. 47.

    & Elaborated Reichardt detectors. J. Opt. Soc. Am. A 2, 300–321 (1985).

  48. 48.

    , & Depth information in natural environments derived from optic flow by insect motion detection system: a model analysis. Front. Comput. Neurosci. 8, 83 (2014).

  49. 49.

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

  50. 50.

    & Optimal speed estimation in natural image movies predicts human performance. Nat. Commun. 6, 7900 (2015).

  51. 51.

    , , , & Cellular organization of the neural circuit that drives Drosophila courtship behavior. Curr. Biol. 20, 1602–1614 (2010).

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

Author notes

    • Armin Bahl

    Present address: Department of Molecular and Cell Biology, Harvard University, Cambridge, Massachusetts, USA.

    • Aljoscha Leonhardt
    •  & Georg Ammer

    These authors contributed equally to this work.

Affiliations

  1. Max Planck Institute of Neurobiology, Martinsried, Germany.

    • Aljoscha Leonhardt
    • , Georg Ammer
    • , Matthias Meier
    • , Etienne Serbe
    • , Armin Bahl
    •  & Alexander Borst

Authors

  1. Search for Aljoscha Leonhardt in:

  2. Search for Georg Ammer in:

  3. Search for Matthias Meier in:

  4. Search for Etienne Serbe in:

  5. Search for Armin Bahl in:

  6. Search for Alexander Borst in:

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.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Aljoscha Leonhardt.

Integrated supplementary information

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary Figures 1–6 and Supplementary Tables 1–7

  2. 2.

    Supplementary Methods Checklist

About this article

Publication history

Received

Accepted

Published

DOI

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

Further reading