Computations underlying Drosophila photo-taxis, odor-taxis, and multi-sensory integration
- New York University, United States
Figures
Figure 1
Identifying computations underlying the decision to initiate a turn.
(A) Computation: on the basis of sensory input (light or odor in this work) the larva decides whether or not to end a run and begin a turn. (B) LNP model of the computation: Sensory input is …
Figure 2 with 1 supplement
Unimodal reverse-correlation experiments.
Top row, Berlin wild-type larvae were stimulated with blue (λpeak = 448 nm; max intensity = 74 μW/cm2) light. All other rows, larvae of indicated genotype were stimulated with red light (λpeak = 655 …
Figure 2—figure supplement 1
LNP model parameters are stable for duration of 20 min experiments.
Experiments of Figure 2 analyzed separately using data only from the first 10 min of experiment (teal) or only from the second 10 min of experiment (purple) or from entire 20 min data set (black). …
Figure 3 with 3 supplements
Multi-modal reverse-correlation experiments suggest attractive odor and light signals are combined linearly and early.
Or42a>CsChrimson larvae were presented with independently varying Brownian light intensities. Reverse-correlation analysis was carried out as in Figure 1. (A) TTA. Average change in red (fictive …
Figure 3—figure supplement 1
Graphical explanation of the independent pathways model.
https://doi.org/10.7554/eLife.06229.007
Figure 3—figure supplement 2
Graphical explanation of the early linear combination model.
https://doi.org/10.7554/eLife.06229.008
Figure 3—figure supplement 3
Visual and fictive olfactory stimuli do not cross-talk.
Larvae were presented with same red and blue Brownian light stimuli as in Figure 3. TTA of red and blue stimuli are shown on same axes as in Figure 3A. (A) Reproduced from Figure 3A: Larvae …
Figure 4
Multi-modal step responses support early linear combination of odor and light signals.
Turn rates vs time for Or42a>CsChrimson larvae responding to coordinated increases and decreases of red and blue light. All steps occur at t = 0. Left column: no change in fictive odor, center …
Figure 5
All navigational decisions appear to be based on a single linear combination of odor and light inputs.
(A–C) Reverse correlation in rotated coordinate system. μ,ν are linear combinations of the raw input stimuli according to the same scaling as used to combine filtered odor and light signals in Figure…
Figure 6
Probing for attentional shifts during multi-modal noise experiments.
(A) Turn-triggered ensemble (duplicated from Figure 3B), with quadrants highlighted. Color scale the same as in 3B. Quadrants I–IV indicate which stimulus or stimuli likely provoked the larva to …
Videos
Video 1
Calculating the turn-triggered average.
Left panel: annotated video image of individual larva. Thin white line: larva's path (past and future). Gold dots: markers along midline of animal, used to determine posture. Upper left corner: time …
Tables
Table 1
Numbers of experiments, animals, turns, and head sweeps for all figures
| Genotype | #expts | #animals | hours | #turns | rms turn size | #large turns | #small turns | #accepted head sweeps | #rejected head sweeps |
|---|---|---|---|---|---|---|---|---|---|
| Uni-modal reverse-correlation experiments (Figure 2A,B,D,E, Figure 2—figure supplement 1) | |||||||||
| Berlin | 6 | 150 | 52.6 | 6594 | 76.4 | 2462 | 4132 | 4139 | 2455 |
| Canton-S | 7 | 334 | 117.7 | 8824 | 66.0 | 3086 | 5738 | 5570 | 3254 |
| Or42a>CsChrimson | 5 | 180 | 61.1 | 6971 | 68.9 | 2531 | 4440 | 4122 | 2849 |
| Or42b>CsChrimson | 6 | 246 | 64.4 | 9565 | 73.3 | 3480 | 6085 | 6215 | 3350 |
| Gr21a>CsChrimson | 5 | 227 | 54.2 | 8760 | 75.1 | 3392 | 5368 | 5424 | 3336 |
| Uni-modal step experiments (Figure 2C) | |||||||||
| Berlin | 4 | 95 | 34.7 | 3674 | – | – | – | – | – |
| Or42a>CsChrimson | 2 | 107 | 36.6 | 3905 | – | – | – | – | – |
| Or42b>CsChrimson | 2 | 99 | 21.5 | 2599 | – | – | – | – | – |
| Gr21a>CsChrimson | 2 | 111 | 22.1 | 2384 | – | – | – | – | – |
| Multi-modal reverse-correlation experiments (Figure 3, 5, 6, Figure 3—figure supplement 3) | |||||||||
| Or42a>CsChrimson | 12 | 608 | 136 | 21,075 | 66.6 | 7225 | 13,850 | 12,795 | 8280 |
| quadrant I | – | – | – | 10,363 | – | – | – | 6216 | 4147 |
| quadrant II | – | – | – | 3301 | – | – | – | 2086 | 1215 |
| quadrant III | – | – | – | 1684 | – | – | – | 1088 | 596 |
| quadrant IV | – | – | – | 5727 | – | – | – | 3405 | 2322 |
| GMR-Hid, Or42a>CsChrimson | 3 | 121 | 28.9 | 4842 | – | – | – | – | – |
| Canton-S | 3 | 166 | 39.9 | 3412 | – | – | – | – | – |
| Multi-modal step experiments (Figure 4) | |||||||||
| Or42a>CsChrimson | 5 | 250 | 50 | 7859 | – | – | – | – | – |
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#expts: Number of 20 min experiments. For reverse-correlation experiments, each experiment presented a different stimulus sequence with the same statistical properties; for step experiments, the same stimulus pattern was presented in each experiment.
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#animals: Approximate number of animals, taken by finding the maximum number of animals tracked in a 30-s window during each experiment.
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#hours: total observation time in units of larva-hours. Observing 3 larvae for 20 min each would equal 1 larva-hour.
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#turns: total number of turns observed and used in analysis.
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rms turn size: root mean square turn size in degrees (defined as angular difference in run heading immediately before and after a turn) for the set of experiments.
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#large/small turns: number of turns with angular changes larger/smaller than the rms turn size.
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#accepted head sweeps: number of times the first head sweep of a turn was accepted, ending in a new run.
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#rejected head sweeps: number of times the first head sweep of a turn was rejected, leading to another head sweep.
Table 2
Kullback-Leibler divergences for Figure 3
| KL divergence | k-NN | model data as normally distributed | Szegö-PSD method |
|---|---|---|---|
| Figure 3B: | 0.351 | 0.325 | – |
| Figure 3B: | 0.236 | 0.223 | 0.235 |
| Figure 3B: | 0.103 | 0.100 | 0.104 |
| Figure 3B: | 0.334 | 0.324 | 0.333 |
| Figure 3B: | 0.006 | 0.0002 | 0.003 |
| Figure 3C: | 0.062 | 0.035 | – |
| Figure 3D: | 0.030 | 0.007 | – |
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KL divergence: the divergence to be calculated. k-NN: divergence calculated using the k-nearest neighbors algorithm. This value is displayed in Figure 3. model data as normal distributed: the distributions are modeled as Gaussians, whose divergence is calculated analytically. Szegö-PSD method: divergence between 1D distributions calculated by an alternate method.
