Development and application of white-noise modeling techniques for studies of insect visual nervous system

Marmarelis, Panos Z.; McCann, G. D.

doi:10.1007/bf00272463

Cited by 95 publications

(32 citation statements)

References 9 publications

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“…3, 4) showed peak responses to fast timescale correlations with intervals of ~17 ms. This timescale for the delay, derived from direct measurements of correlation responses, is faster than delay filters considered in earlier work (Brinkworth and O’Carroll, 2009; Eichner et al, 2011; Shoemaker et al, 2005), but is consistent with other measurements, often made using methods similar to those used here (Clark et al, 2011; Harris et al, 1999; Marmarelis and McCann, 1973). It is also consistent with some measurements of the response properties of neural inputs to T4 and T5, which show differences in peak response timing of ~20 ms (Behnia et al, 2014).…”

Section: Discussionsupporting

confidence: 87%

Direct Measurement of Correlation Responses in Drosophila Elementary Motion Detectors Reveals Fast Timescale Tuning

Salazar-Gatzimas

Chen

Creamer

et al. 2016

Neuron

150

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Animals estimate visual motion by integrating light intensity information over time and space. The integration requires nonlinear processing, which makes motion estimation circuitry sensitive to specific spatiotemporal correlations that signify visual motion. Classical models of motion estimation weight these correlations to produce direction-selective signals. However, the correlational algorithms they describe have not been directly measured in elementary motion detecting neurons (EMDs). Here, we employed stimuli to directly measure responses to pairwise correlations in Drosophila’s EMD neurons, T4 and T5. Activity in these neurons was required for behavioral responses to pairwise correlations and was predictive of those responses. The pattern of neural responses in the EMDs was inconsistent with one classical model of motion detection, and the timescale and selectivity of correlation responses constrained the temporal filtering properties in potential models. These results reveal how neural responses to pairwise correlations drive visual behavior in this canonical motion detecting circuit.

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Section: Discussionsupporting

confidence: 87%

Direct Measurement of Correlation Responses in Drosophila Elementary Motion Detectors Reveals Fast Timescale Tuning

Salazar-Gatzimas

Chen

Creamer

et al. 2016

Neuron

150

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“…Thus, in both cases, there was a small but significant temporal offset, with Mi1 exhibiting a delayed response as compared to Tm3, and Tm1 being delayed relative to Tm2. Notably, these peak delay differences are not much smaller than delays inferred from some LPTC recordings and behavioral experiments 3,23,24 .…”

mentioning

confidence: 60%

Processing properties of ON and OFF pathways for Drosophila motion detection

Behnia

Clark

Carter

et al. 2014

Nature

240

326

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The algorithms and neural circuits that process spatiotemporal changes in luminance to extract visual motion cues have been the focus of intense research. An influential model, the Hassenstein-Reichardt correlator1 (HRC), 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 (Figure 1A). 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. Using in vivo patch-clamp recordings, we demonstrate 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 to Tm2. Remarkably, constraining HRC models using these measurements produces outputs consistent with previously measured properties of motion detectors, including temporal frequency tuning and specificity for light vs. 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 shows that specific medulla neurons possess response properties that allow them to implement the algorithmic steps that precede the correlative operation in the HRC, revealing elements of the long-sought neural substrates of motion detection in the fly.

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“…A powerful approach for studying the properties of visual circuits is reverse correlationbased system identification (43). However, motion circuits present a particular challenge, because motion detection relies on nonlinear interactions and structured spatiotemporal correlations.…”

Section: Discussionmentioning

confidence: 99%

Neural correlates of illusory motion perception in Drosophila

Tuthill

Chiappe

Reiser

2011

Proc. Natl. Acad. Sci. U.S.A.

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When the contrast of an image flickers as it moves, humans perceive an illusory reversal in the direction of motion. This classic illusion, called reverse-phi motion, has been well-characterized using psychophysics, and several models have been proposed to account for its effects. Here, we show that Drosophila melanogaster also respond behaviorally to the reverse-phi illusion and that the illusion is present in dendritic calcium signals of motion-sensitive neurons in the fly lobula plate. These results closely match the predictions of the predominant model of fly motion detection. However, high flicker rates cause an inversion of the reverse-phi behavioral response that is also present in calcium signals of lobula plate tangential cell dendrites but not predicted by the model. The fly's behavioral and neural responses to the reverse-phi illusion reveal unexpected interactions between motion and flicker signals in the fly visual system and suggest that a similar correlation-based mechanism underlies visual motion detection across the animal kingdom.fly vision | Drosophila behavior | calcium imaging | visual illusion A mong visual organisms, the ability to detect motion is nearly universal. Animals as diverse as weevils (1) and wallabies (2) compute visual motion from time-varying patterns of brightness received by an array of photoreceptors. However, the mechanisms by which the visual system detects motion are not wellunderstood in any animal (3). Here, we use a visual illusion to probe the mechanisms of motion detection in the fly, Drosophila melanogaster.Sequential flashes at neighboring spatial positions cause humans to perceive motion in the direction of the second flash, an effect called phi or apparent motion (4). A related phenomenon, reverse-phi motion, also relies on sequential luminance changes to evoke a motion percept (5); however, in the reverse-phi stimulus, the contrast polarity of the stimulus inverts as it moves, causing a reversal in the direction of perceived motion (Movie S1). For example, when a black random dot pattern turns to white as it moves right across a gray background, human subjects perceive left motion (6).The reverse-phi effect is not a subtle illusion. Humans exhibit nearly equal sensitivity and comparable spatial and temporal tuning for standard and reverse-phi motion (7). Sensitivity to reverse-phi has also been shown for other vertebrates such as primates (8) and zebrafish (9). Directional responses to reversephi motion are present in cat striate cortex (10), the nucleus of the optic tract in the wallaby (2), and the middle temporal area (MT) of primate visual cortex (8, 11). These psychophysical and neurophysiological data suggest that sensitivity to reverse-phi motion may be a common feature of motion detection in the vertebrate visual system, and for this reason, reverse-phi has been an important tool for building models of visual motion detection (8,(12)(13)(14)(15)(16).The prevailing simple model for motion detection in the fly is the Hassenstein-Reichardt elementary moti...

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Development and application of white-noise modeling techniques for studies of insect visual nervous system

Cited by 95 publications

References 9 publications

Direct Measurement of Correlation Responses in Drosophila Elementary Motion Detectors Reveals Fast Timescale Tuning

Direct Measurement of Correlation Responses in Drosophila Elementary Motion Detectors Reveals Fast Timescale Tuning

Processing properties of ON and OFF pathways for Drosophila motion detection

Neural correlates of illusory motion perception in Drosophila

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