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

Nature Neuroscience volume 19, pages 706–715 (2016)Cite this article

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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.

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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.

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  • 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.

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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.

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Author notes

  1. Armin Bahl

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

  2. Aljoscha Leonhardt and Georg Ammer: These authors contributed equally to this work.

Authors and Affiliations

  1. Max Planck Institute of Neurobiology, Martinsried, Germany

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

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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.

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Correspondence to Aljoscha Leonhardt.

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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).

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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

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