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.

Processing properties of ON and OFF pathways for Drosophila motion detection

Nature volume 512, pages 427–430 (2014)Cite this article

Subjects

Abstract

The algorithms and neural circuits that process spatio-temporal changes in luminance to extract visual motion cues have been the focus of intense research. An influential model, the Hassenstein–Reichardt correlator1, relies on differential temporal filtering of two spatially separated input channels, delaying one input signal with respect to the other. Motion in a particular direction causes these delayed and non-delayed luminance signals to arrive simultaneously at a subsequent processing step in the brain; these signals are then nonlinearly amplified to produce a direction-selective response. Recent work in Drosophila has identified two parallel pathways that selectively respond to either moving light or dark edges2,3. Each of these pathways requires two critical processing steps to be applied to incoming signals: differential delay between the spatial input channels, and distinct processing of brightness increment and decrement signals. Here we demonstrate, using in vivo patch-clamp recordings, that four medulla neurons implement these two processing steps. The neurons Mi1 and Tm3 respond selectively to brightness increments, with the response of Mi1 delayed relative to Tm3. Conversely, Tm1 and Tm2 respond selectively to brightness decrements, with the response of Tm1 delayed compared with Tm2. Remarkably, constraining Hassenstein–Reichardt correlator models using these measurements produces outputs consistent with previously measured properties of motion detectors, including temporal frequency tuning and specificity for light versus dark edges. We propose that Mi1 and Tm3 perform critical processing of the delayed and non-delayed input channels of the correlator responsible for the detection of light edges, while Tm1 and Tm2 play analogous roles in the detection of moving dark edges. Our data show that specific medulla neurons possess response properties that allow them to implement the algorithmic steps that precede the correlative operation in the Hassenstein–Reichardt correlator, revealing elements of the long-sought neural substrates of motion detection in the fly.

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

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Learn more

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

Figure 1: Motion detection and the fly optic lobe.
Figure 2: Mi1/Tm3 respond selectively to brightness increments while Tm1/Tm2 respond selectively to brightness decrements.
Figure 3: Mi1/Tm3 and Tm1/Tm2 respond with different delays and nonlinearities to a Gaussian noise stimulus.
Figure 4: Modelling Mi1/Tm3 and Tm1/Tm2 as the delayed and non-delayed channels of light edges and dark edges correlators.

References

  1. Hassenstein, V. & Reichardt, W. System theoretical analysis of time, sequence and sign analysis of the motion perception of the snout-beetle Chlorophanus. Z. Naturforsch. B 11, 513–524 (1956)

    Article  Google Scholar 

  2. 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  ADS  Google Scholar 

  3. 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  Google Scholar 

  4. Borst, A. & Egelhaaf, M. Principles of visual motion detection. Trends Neurosci. 12, 297–306 (1989)

    Article  CAS  Google Scholar 

  5. Buchner, E. Elementary movement detectors in an insect visual system. Biol. Cybern. 24, 85–101 (1976)

    Article  Google Scholar 

  6. Meinertzhagen, I. & O’Neil, S. Synaptic organization of columnar elements in the lamina of the wild type in Drosophila melanogaster. J. Comp. Neurol. 305, 232–263 (1991)

    Article  CAS  Google Scholar 

  7. Rister, J. et al. Dissection of the peripheral motion channel in the visual system of Drosophila melanogaster. Neuron 56, 155–170 (2007)

    Article  CAS  Google Scholar 

  8. Katsov, A. Y. & Clandinin, T. R. Motion processing streams in Drosophila are behaviorally specialized. Neuron 59, 322–335 (2008)

    Article  CAS  Google Scholar 

  9. Tuthill, J. C., Nern, A., Holtz, S. L., Rubin, G. M. & Reiser, M. B. Contributions of the 12 neuron classes in the fly lamina to motion vision. Neuron 79, 128–140 (2013)

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  11. Bausenwein, B., Dittrich, A. & Fischbach, K.-F. The optic lobe of Drosophila melanogaster. Cell Tissue Res. 267, 17–28 (1992)

    Article  CAS  Google Scholar 

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

    Article  CAS  ADS  Google Scholar 

  13. 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. 198, 389–395 (2012)

    Article  Google Scholar 

  14. Borst, A., Haag, J. & Reiff, D. F. Fly motion vision. Annu. Rev. Neurosci. 33, 49–70 (2010)

    Article  CAS  Google Scholar 

  15. Bausenwein, B. & Fischbach, K.-F. Activity labeling patterns in the medulla of Drosophila melanogaster caused by motion stimuli. Cell Tissue Res. 270, 25–35 (1992)

    Article  CAS  Google Scholar 

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

    Article  CAS  ADS  Google Scholar 

  17. Shinomiya, K. et al. Candidate neural substrates for off-edge motion detection in Drosophila. Curr. Biol. 24, 1–9 (2014)

    Article  Google Scholar 

  18. Meier, M. et al. Neural circuit components of the Drosophila OFF motion vision pathway. Current Biol. (2014)

  19. 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  Google Scholar 

  20. 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  Google Scholar 

  21. Chichilnisky, E. J. A simple white noise analysis of neuronal light responses. Network 12, 199–213 (2001)

    Article  CAS  Google Scholar 

  22. Baccus, S. A. & Meister, M. Fast and slow contrast adaptation in retinal circuitry. Neuron 36, 909–919 (2002)

    Article  CAS  Google Scholar 

  23. Harris, R. A., O’Carroll, D. C. & Laughlin, S. B. Contrast gain reduction in fly motion adaptation. Neuron 28, 595–606 (2000)

    Article  CAS  Google Scholar 

  24. Marmarelis, P. Z. & McCann, G. D. Development and application of white-noise modeling techniques for studies of insect visual nervous system. Kybernetik 12, 74–89 (1973)

    Article  CAS  Google Scholar 

  25. Franceschini, N., Riehle, A. & Le Nestour, A. in Facets of Vision (eds Stavenga, D. G. & Hardie, R. C. ) Ch. 17, 360–390 (Springer, 1989)

    Book  Google Scholar 

  26. Borst, A. & Bahde, S. What kind of movement detector is triggering the landing response of the housefly? Biol. Cybern. 55, 59–69 (1986)

    Article  Google Scholar 

  27. Wässle, H. Parallel processing in the mammalian retina. Nature Rev. Neurosci. 5, 747–757 (2004)

    Article  Google Scholar 

  28. Barlow, H. & Levick, W. R. The mechanism of directionally selective units in rabbit’s retina. J. Physiol. (Lond.) 178, 477–504 (1965)

    Article  CAS  Google Scholar 

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

    Article  CAS  ADS  Google Scholar 

  30. Fischbach, K.-F. & Dittrich, A. The optic lobe of Drosophila melanogaster. I. A Golgi analysis of wild-type structure. Cell Tissue Res. 258, 441–475 (1989)

    Article  Google Scholar 

  31. Hasegawa, E. et al. Concentric zones, cell migration and neuronal circuits in the Drosophila visual center. Development 138, 983–993 (2011)

    Article  CAS  Google Scholar 

  32. Jenett, A. et al. A GAL4-driver line resource for Drosophila neurobiology. Cell Rep. 2, 991–1001 (2012)

    Article  CAS  Google Scholar 

  33. Morante, J. & Desplan, C. The color-vision circuit in the medulla of Drosophila. Curr. Biol. 18, 553–565 (2008)

    Article  CAS  Google Scholar 

  34. 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  Google Scholar 

  35. Freifeld, L., Clark, D. A., Schnitzer, M. J., Horowitz, M. A. & Clandinin, T. R. GABAergic lateral interactions tune the early stages of visual processing in Drosophila. Neuron 78, 1075–1089 (2013)

    Article  CAS  Google Scholar 

  36. Brainard, D. H. The psychophysics toolbox. Spat. Vis. 10, 433–436 (1997)

    Article  CAS  Google Scholar 

  37. Stavenga, D. Angular and spectral sensitivity of fly photoreceptors. II. Dependence on facet lens F-number and rhabdomere type in Drosophila. J. Comp. Physiol. A 189, 189–202 (2003)

    CAS  Google Scholar 

  38. Morante, J. & Desplan, C. Dissection and staining of Drosophila optic lobes at different stages of development. Cold Spring Harb. Protoc. 2011, 652–656 (2011)

    Article  Google Scholar 

Download references

Acknowledgements

We thank G. Turner for his initial help on whole-cell recordings; X. Li for providing the confocal image of the 686-Gal4 flip-out clone, and N. Vogt, J. Rister and members of the Clandinin laboratory, as well as H. S. Seung and S. A. Baccus, for reading of the manuscript. We also thank D. B. Chklovskii for suggesting recording from Tm3 and T. Erclick for identifying the Tm3 Gal4 line. This work was supported by a grant from the National Institutes of Health (NIH) (R01EY017916) and a grant from New York University Abu Dhabi Institute (G1205C) to C.D.; R.B. was supported by fellowships from EMBO and the Human Frontier Science Program. This work was also supported by grants from the NIH to T.R.C. (R01EY022638 and DP1 OD003530). D.A.C. was partly supported by an NIH T32 Vision Training Grant and a Jane Coffin Childs postdoctoral fellowship. A.G.C. was supported by a Scholar Award from The McKnight Foundation.

Author information

Authors and Affiliations

  1. Department of Biology, Center for Developmental Genetics, New York University, New York, 10003-6688, New York, USA

    Rudy Behnia & Claude Desplan

  2. Department of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, 06511, Connecticut, USA

    Damon A. Clark

  3. Department of Neurobiology, Stanford University, Stanford, 94305, California, USA

    Damon A. Clark & Thomas R. Clandinin

  4. Center for Neural Science, New York University, New York, 10003, New York, USA

    Adam G. Carter

  5. Center for Genomics & Systems Biology, New York University Abu Dhabi Institute, Abu Dhabi, United Arab Emirates

    Claude Desplan

Authors
  1. Rudy Behnia
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Damon A. Clark
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Adam G. Carter
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Thomas R. Clandinin
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Claude Desplan
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

R.B. designed experiments, performed electrophysiological recordings and analysed the data. D.A.C. designed visual stimuli and experiments, analysed the data and performed modelling. A.G.C. provided electrophysiological training and advice to R.B. T.R.C. and C.D. contributed to the design of experiments. R.B., D.A.C., T.R.C. and C.D. wrote the manuscript.

Corresponding authors

Correspondence to Rudy Behnia or Damon A. Clark.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Extended data figures and tables

Extended Data Figure 1 Representative raw traces of responses to flashes of light of different duration from dark.

a, Top: response of an Mi1 neuron to 200 ms, four consecutive 250 ms and 1 s full-field flashes of light from dark. Bottom: same as top for a Tm3 neuron. b, Same as a for a Tm1 neuron and a Tm2 neuron. c, Box plots illustrating the distribution of the OFF response as a percentage of the ON response for Mi1 (n = 7) and Tm3 (n = 10) and the ON response as a percentage of the OFF response for Tm1 (n = 10) and Tm2 (n = 11) averaged in Fig. 1. Black line, median; coloured line, average.

Extended Data Figure 2 Mi1, Tm3, Tm1 and Tm2 neurons encode stable information about luminance.

a, Top left: averaged evoked responses (± s.e.m.) of Mi1 (n = 7) in response to 5 s steps of light from dark to grey (0.5 intensity) to light to grey to dark. Top right: excerpts from the left trace where the pre-contrast change voltages have been matched. Bottom left and right: same as above for Tm3 (n = 10). b, Tonic component (average difference in membrane potential between post- and pre-contrast change between 4 and 5 s after contrast change) as a percentage of the maximum peak response for brightness increments of the corresponding contrast difference. Error bars, s.e.m. c, d, Same as a and b for Tm1 (n = 9) and Tm2 (n = 7). The tonic component was measured as a percentage of the peak response for brightness decrements of the corresponding contrast difference. In all cases, expect for those marked with a cross, the distributions are significantly different from zero (P < 0.05).

Extended Data Figure 3 Mi1, Tm3, Tm1 and Tm2 are not direction selective.

a, Top: response of an Mi1 neuron to a white bar moving rightwards, upwards, leftwards and downwards at 100° s−1 on a dark background. Bottom: same as top for a Tm3 neuron. b, Top: response of a Tm1 neuron to a black bar moving rightwards, upwards, leftwards and downwards at 100° s−1 on a light background. Bottom: same as top for a Tm2 neuron. c, Average amplitude of the voltage response as a function of angle (0°, 90°, 180° and 270°) for Mi1 (n = 2), Tm3 (n = 2), Tm1 (n = 2) and Tm2 (n = 3) for a bar moving at 100° s−1 (solid lines) and 400° s−1 (dashed lines). The response amplitude was independent of the direction of motion in all cases.

Extended Data Figure 4 The response of Mi1, Tm3, Tm1 and Tm2 to a Gaussian noise stimulus is very reliable.

a, Left: response of an Mi1 neuron to three consecutive 10 s presentations of an approximate Gaussian noise stimulus with 50% standard deviation and correlation time of 10 ms. Right: same as left for Tm3. b, Same as a for Tm1 (left) and Tm2 (right). c, Coherence of the measured responses in the four cell types. Deviations from 1 mean that variance in the output is not entirely accounted for by a linear transformation of the input. This can be caused by noise in the response unrelated to the input, or by the nonlinearities in the system response that we measured. The linear filter amplitude for each frequency is distinct from coherence, and those amplitudes as a function of frequency are plotted in Extended Data Fig. 7a, b.

Extended Data Figure 5 Individual filters and nonlinearities from the Gaussian noise analysis of Mi1, Tm3, Tm1 and Tm2.

a, Individual filters (in grey) overlaid on the average filter (± s.e.m.) for Mi1 neurons. b, Individual nonlinearities (in grey) overlaid on the averaged nonlinearity (± s.e.m.) for Mi1 neurons. c, d, Same as a and b for Tm3. e, f, Same as a and b for Tm1. g, h, Same as a and b for Tm2.

Extended Data Figure 6 Spatio-temporal analysis of Mi, Tm3, Tm1 and Tm2.

a, Representative receptive fields of Mi1, Tm3, Tm1 and Tm2 neurons shown as a heat map of 256 pixels using the r value of linear prediction for each pixel intensity. b, Average temporal filters (± s.e.m.) extracted from the highest responding pixels for each neuron for Mi1 (n = 4) and Tm3 (n = 8) (see Methods). The peaks of the filters, with the average timing, are enlarged in the inset. c, Average nonlinearities over several neurons for both Mi1 and Tm3. To obtain each neuron’s nonlinearity, the neuron’s measured response was plotted against the linear prediction from the relevant pixels. Error bars, s.e.m. A line of slope 1 is shown in black. d, e, Equivalent to b and c for Tm1 (n = 8) and Tm2 (n = 7).

Extended Data Figure 7 Numerical and analytical HRC responses.

a, b, We plot three terms in equation (2) of the Methods, and the total HRC response, using the empirical measurements for Tm1/Tm2 and Mi1/Tm3 as the two input arms for the correlator (f2(t) and f1(t), respectively). The analytical results computed here match the numerical ones shown in Fig. 4. Here and in all subsequent plots, we normalize the filter values so that they have a maximum of 1, and compute the relative HRC response from those normalized filters and the phase term. c, The same three components of equation (2) are plotted in the special case where f1(t) = δ(t) and . We plot the result with τ = 150 ms, so that the peak response occurs at ∼1 Hz. d, The same components of equation (2) are plotted in the case where both f1(t) and f2(t) are first-order low-pass filters, with time constants of 40 ms and 55 ms, respectively. e, False-colour plot of the temporal frequency optimum for various combinations of τ1 and τ2. Many combinations result in frequency optima near 1 Hz. f, The value of the relative HRC response at the optimal frequency in e is plotted for those same combinations of τ1 and τ2. To compute this, temporal filters have a maximum gain of 1, as in a–d. The responses become small primarily when the phase term becomes small. When the phase term is very small, the subtraction performed by the HRC is susceptible to noise, since it can be subtracting two larger numbers to yield the small difference. Therefore, filter combinations with very small differences seem less biologically plausible than those with larger phase terms. The phase terms for the two model HRCs in a and b are between 0.2 and 0.4 in the 1-Hz region, larger than for the toy model shown in d.

Extended Data Figure 8 686-Gal4 labels Mi1 neurons and R13E12-Gal4 is specific to Tm3 neurons.

a, Confocal image of a single Mi1 neuron obtained through a flip-out clone procedure with 686-Gal4. Mi1 neurons present processes at the level of M1 and M5 and terminate in the most proximal layers of the medulla. This line also sparsely labels Tm2 neurons, which were distinguishable both visually and functionally. b, Confocal image of twin-spot MARCM clones obtained using R13E12-Gal4. Tm3 neurons present processes at the medulla layers M1 and M5 and project to proximal layers of the medulla and superficial layers of the lobula.

Extended Data Figure 9 Evoked response of ‘tonic’ Mi1 neurons.

a, Average evoked responses (± s.e.m.) of ‘tonic’ Mi1 (n = 9) in response to 200 ms, four consecutive 250 ms and 1 s full-field flashes of light from dark. b, Average evoked responses (± s.e.m.) of ‘tonic’ Mi1 (n = 7) in response to 5 s steps of light. c, Top: 2 s excerpt of the intensity signal from the 10 s full-field Gaussian noise stimulus. Correlation time is 10 ms. Bottom: average voltage response (± s.e.m.) of ‘tonic’ Mi1 (n = 8) in response to the 2 s noise stimulus on top. The black trace corresponds to the average predicted linear response obtained by convolving the stimulus with the filters in d (± s.e.m.). d, Average temporal filters (± s.e.m.) extracted from the data in c that best predict the measured response of ‘tonic’ Mi1 as a function of contrast history. Individual filters are shown in grey. e, Nonlinearities for ‘tonic’ Mi1 cells. Actual responses are plotted against their linear predicted responses. Individual cell nonlinearities in grey; mean and s.e.m. are represented by the coloured line and patch. A line of slope 1 is shown in black. f, Average temporal filters (± s.e.m.) extracted from the highest-responding pixels for each ‘tonic’ Mi1 neuron in the spatio-temporal experiments. g, Averaged actual responses of ‘tonic’ Mi1 plotted against their averaged linear predicted responses in the spatio-temporal experiments. Error bars, s.e.m. A line of slope 1 is shown in black.

PowerPoint slides

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Behnia, R., Clark, D., Carter, A. et al. Processing properties of ON and OFF pathways for Drosophila motion detection. Nature 512, 427–430 (2014). https://doi.org/10.1038/nature13427

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nature13427

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

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