1. Volume 92, Issue 1, Pages 227-239 (October 2016)
Direct Measurement of Correlation Responses in Drosophila Elementary Motion Detectors Reveals Fast Timescale Tuning 
Emilio Salazar-Gatzimas, Juyue Chen, Matthew S. Creamer, Omer Mano, Holly B. Mandel, Catherine A. Matulis, Joseph Pottackal, Damon A. Clark 
Neuron 
Volume 92, Issue 1, Pages (October 2016)
DOI: /j.neuron
Copyright © 2016 Elsevier Inc. Terms and Conditions

2. Figure 1
Psychophysical Responses to Correlated Noise Stimuli Map the Correlation Interval Receptive Field of Drosophila’s Optomotor Response
(A) We obtained psychophysical responses in Drosophila by tethering a fly above an air-suspended ball and examining the fly’s attempted rotation in response to visual stimuli displayed on a panoramic screen around it.
(B) On the screens, we displayed correlated noise stimuli, which elicited turning behavior in the flies.
(C) Correlated noise stimuli contain correlations at specific offsets in time and space (see Supplemental Information). Example space-time correlograms show a rightward offset and positive correlation with temporal offset of one frame (left), and a negative correlation with a leftward displacement and a temporal offset of two frames (right). The red rectangles at the origins exist because the stimulus is perfectly correlated with itself when there is no offset in time or space.
(D) Correlated noise stimuli were presented for 1 s beginning at time 0 (marked by gray box). Flies turned steadily in response to these stimuli and modulated the strength of their response with the correlation sign and interval. Intervals times are reported as the time in milliseconds between correlations.
(E) Mean turning responses were computed to the different signs and intervals of correlations. Flies responded most strongly to correlations with intervals of 11–22 ms. N = 15 flies. Curves are mean ± SEM.
(F) When neurons T4 and T5 were silenced using shibirets, turning in response to correlated noise stimuli was abolished. Genetic control flies responded in patterns similar to wild-type. N = 15, 15, 20 flies for the T4T5 > shits, T4T5/+, and +/shits curves. Curves are mean ± SEM (see also Figure S1)
Neuron  , DOI: ( /j.neuron )
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3. Figure 2 Distinguishing T4 and T5 by Their Response Properties
(A) Different circuitry detects light and dark edge motion in the fly eye. Distinct neural substrates relay information from photoreceptors (PRs) to the neurons T4 and T5, which respond selectively to light and dark edges in each of the four cardinal directions.
(B) Two-photon microscopy was used to image the calcium activity of T4 and T5 in the lobula plate, while the fly was presented with visual stimuli.
(C) In the lobula plate, the neurons T4 and T5 stratify into four layers, each of which responds to one cardinal direction of motion.
(D) Individual puncta selected in the progressive and regressive layers of the lobula plate are color coded according to their edge selectivity (see Supplemental Information). The DS layers are outlined.
(E) Calcium traces of four sample puncta (numbered in D) responding to progressive and regressive light and dark edges. The edges swept through the fly’s full visual field at 30°/s.
(F) Puncta were assigned both a progressive/regressive direction selectivity index (DSI) and a light/dark edge selectivity index (ESI), which indicated the directional tuning and edge tuning of each punctum (see Supplemental Information). A punctum with a DSI = 1 (–1) responded solely to progressive (regressive) moving edges. Likewise, a punctum with ESI = 1 (–1) responded solely to light (dark) edges. Thresholds for ESI and DSI were used to classify axon terminals into progressive and regressive T4 and T5. Histograms at bottom and side show the distribution of DSI and ESI values. This panel contains 2,242 puncta from 48 flies (see also Figure S2).
Neuron  , DOI: ( /j.neuron )
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4. Figure 3
Linear and Second-Order Receptive Fields of T4 and T5 Progressive and Regressive Puncta
(A) Average linear RFs of the four puncta types, aligned to center each punctum’s RF at 50° azimuth before averaging. Dotted lines align with the preferred direction (PD) for the cell type. Pixels are vertical bars, so there is only one spatial dimension in this filter.
(B) For each of the four puncta types, average second-order RFs for nearest neighbor pixel pairs (see Supplemental Information). Second-order RFs showed strong responses to correlations with short intervals (near the diagonal) and with displacements in the punctum’s PD. Progressive displacements are above the diagonal and regressive ones below.
(C) Elements along each diagonal of the second-order RF correspond to responses to a single correlation interval. These diagonal elements were averaged over the earliest 333 ms to quantify responses to different correlation intervals (see Supplemental Information). These averages show significant responses to 17 and 33 ms intervals in the PD in all four cell types (∗p < 0.01, Bonferroni corrected, z-test relative to shuffled filters). Shading represents the 1 SEM error in the mean measurement. The gray horizontal dotted lines represent one standard deviation of the shuffled filter controls. From left to right, throughout the plot, the number of flies (number of puncta) is 14 (37), 12 (26), 23 (127), and 27 (103) (see also Figure S3).
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5. Figure 4
Correlation Interval Receptive Fields for Progressive Layer T4 and T5 Puncta
(A and B) Flies were shown correlated noise stimuli (Figure 1; Supplemental Information) for durations of 4 s (marked by gray box) and the responses of progressive T4 (A) and T5 (B) puncta were measured. Traces are averages over flies. The puncta responded strongly to uncorrelated stimuli and this response was enhanced/suppressed by positive/negative correlations in the PD. The number of flies (number of puncta) is 17 (102), 18 (85), 19 (148), and 15 (140) for T4-positive correlations, T4-negative correlations, T5-positive correlations, and T5-negative correlations.
(C and D) Mean responses of progressive T4 (C) and T5 (D) to positive (top) and negative (bottom) correlations with varying intervals in the preferred and null directions (PD and ND). For both neurons, responses showed significant differences in the PD compared to the uncorrelated stimulus (∗p < 0.05, ∗∗p < 0.01, Bonferroni corrected, Wilcoxon signed rank test) (see also Figure S4).
Neuron  , DOI: ( /j.neuron )
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6. Figure 5 Computed Net Neural Signal Compared to Behavioral Responses
(A) A net neural response was computed by combining PD and ND T4 and T5 responses from progressive and regressive layers. This combination computed first the opponent response within each eye, and then took the difference between eyes to generate the net neural response.
(B) The behavioral correlation interval receptive field (CIRF) and the computed net neural CIRF both responded positively to positive correlations and negatively to negative correlations. Neural and behavioral curves are normalized to have a maximum response magnitude of 1. Positive correlation neural data, 11 flies (400 puncta); negative correlation neural data, 10 flies (473 puncta); positive correlation behavioral data, 12 flies; negative correlation behavioral data, 9 flies.
Neuron  , DOI: ( /j.neuron )
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7. Figure 6
Comparing Correlation Responses to Circuit Models of Motion Estimation
(A) Progressive layer T4 and T5 responses to correlated noise stimuli at an interval of 17 ms showed significant modulation in the PD relative to uncorrelated (data from Figures 4C and 4D). Opponent responses (PD–ND) were calculated for positive and negative correlations. Red and blue arrows highlight major deviations from the uncorrelated response, which is normalized to a value of 1. (∗p < 0.05, ∗∗p < 0.01, Bonferroni corrected for multiple comparisons in Figure 4; Wilcoxon signed rank test for comparisons to uncorrelated; Wilcoxon sign test for opponent responses.)
(B) We computed the responses of 4 simple models of motion estimation to correlated noise stimuli (see Supplemental Information). For each model, individual layer signals were computed in addition to opponent signals. Responses of all but the HRC model were normalized so the uncorrelated response had a value of 1.
(C) An HRC model was constructed using measured input filters (left) to T4 (top) and T5 (bottom) cells (Behnia et al., 2014). The model responses were computed to positive (center) and negative (right) PD and ND correlated noise stimuli. The faded responses show data replotted from Figure 4, normalized by the uncorrelated response (see also Figure S5).
Neuron  , DOI: ( /j.neuron )
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