Object preference by walking fruit flies, <i>Drosophila melanogaster</i>, is mediated by vision and graviperception

Robie, Alice A.; Straw, Andrew; Dickinson, Michael H.

doi:10.1242/jeb.041749

Cited by 72 publications

(82 citation statements)

References 36 publications

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“…For vision, we performed experiments using Canton-S flies in dim far-red light (850 nm), to which the flies are not sensitive (20). Similar to what has been reported for courtship behavior (21,22), the absence of visual cues did not affect overall movement or the percentage of reciprocal interactions ( Fig. 2 A-D).…”

supporting

confidence: 69%

Social structures depend on innate determinants and chemosensory processing inDrosophila

Schneider

Dickinson

Levine

2012

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

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174

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Flies display transient social interactions in groups. However, whether fly-fly interactions are stochastic or structured remains unknown. We hypothesized that groups of flies exhibit patterns of social dynamics that would manifest as nonrandom social interaction networks. To test this, we applied a machine vision system to track the position and orientation of flies in an arena and designed a classifier to detect interactions between pairs of flies. We show that the vinegar fly, Drosophila melanogaster, forms nonrandom social interaction networks, distinct from virtual network controls (constructed from the intersections of individual locomotor trajectories). In addition, the formation of interaction networks depends on chemosensory cues. Gustatory mutants form networks that cannot be distinguished from their virtual network controls. Olfactory mutants form networks that are greatly disrupted compared with control flies. Different wild-type strains form social interaction networks with quantitatively different properties, suggesting that the genes that influence this network phenotype vary across and within wild-type populations. We have established a paradigm for studying social behaviors at a group level in Drosophila and expect that a genetic dissection of this phenomenon will identify conserved molecular mechanisms of social organization in other species.social network | emergent phenotypes | complex systems F lies engage in a variety of social behaviors that include courtship, aggression, mating, and egg-laying (1-5). Recent studies also indicate that individuals have the ability to recognize others (6) and regulate their behavior according to group membership (7). The composition of such Drosophila groups forms a social environment that influences gene transcription, pheromone displays, and mating frequency among group members (7-9). However, the genetic and sensory determinants that underlie these observations are unknown.To explore the possibility that Drosophila form organized social interaction networks (SINs), we quantified interactions formed by pairs of individuals within groups of 12 flies of the same sex placed in a circular arena (10). Using video sequences, three classes of behavior suggesting social interactions were identified: frontal approach, rear approach, and social preening (also known as "grooming," in which a fly rubs its legs together, or uses the leg(s) to rub its own wings, head, or abdomen). Social preening has been previously associated with social interactions in Drosophila (11, 12). These behaviors were verified in detail using video sequences captured at high spatial and temporal resolution (Movie S1). Inspection of video sequences indicated that these classes of behaviors often involve physical contact between the fore-and middle legs of the interacting flies, and these were scored to evaluate their relative frequency of occurrence (Table S1). Drosophila legs harbor both touch-and tastesensitive sensillae (13), suggesting that flies might exchange somatosensory and gustatory ...

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supporting

confidence: 69%

Social structures depend on innate determinants and chemosensory processing inDrosophila

Schneider

Dickinson

Levine

2012

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

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174

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“…To explore the effect of small target motion on retinal circuit dynamics, we developed a stimulus based on the naturalistic motion patterns of small moving prey (Branson et al, 2009;Robie et al, 2010). A small target was moved at a fixed bearing and speed, with periodic discrete changes in these parameters (Figs.…”

Section: Resultsmentioning

confidence: 99%

“…Prey tends to move continuously over large distances, with periodic turns (Robie et al, 2010). To simulate these motion patterns, a reasonably large amount of visual space is needed.…”

Section: Methodsmentioning

confidence: 99%

“…One-dimensional reversals of motion (180°t urns) were poorly tracked because in these cases the target would move directly into the region of inhibition created by the contrast gain control circuit, eliciting weak ganglion cell activity and producing a position estimate that was dominated by noise. The significance of these reversal-of-motion effects for behavior is unclear, as real prey rarely turn so sharply (Robie et al, 2010). …”

Section: Target Tracking With Fast-off Cellsmentioning

confidence: 99%

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Nonlinear Dynamics Support a Linear Population Code in a Retinal Target-Tracking Circuit

Leonardo

Meister

2013

J. Neurosci.

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A basic task faced by the visual system of many organisms is to accurately track the position of moving prey. The retina is the first stage in the processing of such stimuli; the nature of the transformation here, from photons to spike trains, constrains not only the ultimate fidelity of the tracking signal but also the ease with which it can be extracted by other brain regions. Here we demonstrate that a population of fast-OFF ganglion cells in the salamander retina, whose dynamics are governed by a nonlinear circuit, serve to compute the future position of the target over hundreds of milliseconds. The extrapolated position of the target is not found by stimulus reconstruction but is instead computed by a weighted sum of ganglion cell outputs, the population vector average (PVA). The magnitude of PVA extrapolation varies systematically with target size, speed, and acceleration, such that large targets are tracked most accurately at high speeds, and small targets at low speeds, just as is seen in the motion of real prey. Tracking precision reaches the resolution of single photoreceptors, and the PVA algorithm performs more robustly than several alternative algorithms. If the salamander brain uses the fast-OFF cell circuit for target extrapolation as we suggest, the circuit dynamics should leave a microstructure on the behavior that may be measured in future experiments. Our analysis highlights the utility of simple computations that, while not globally optimal, are efficiently implemented and have close to optimal performance over a limited but ethologically relevant range of stimuli.

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“…In nature, however, most organisms incorporate at least some degree of movement in 3D, which influences ecological interactions [3) . Tracking systems designed for 2D can provide some resolution for behavior in a third spatial dimension [34), but ultimately developers must produce tracking systems that can successfully track large numbers of animals in 3D space (Movie S8 in the supplementary material online). Tracking unconstrained flying or swimming animals can be achieved in several ways, but most often multiple cameras are employed [18,29,[35][36][37][38][39][40][41][42][43][44][45] (Movie S6, Movie 88, and Movie S22 in the supplementary material online).…”

Section: Tracking In Three Dimensionsmentioning

confidence: 99%

Automated image-based tracking and its application in ecology

Dell

Bender²,

Branson

et al. 2014

Trends in Ecology & Evolution

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454

484

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The behavior of individuals determines the strength and outcome of ecological interactions, which drive population, community, and ecosystem organization. Bio-logging, such as telemetry and animal-borne imaging, provides essential individual viewpoints, tracks, and life histories, but requires capture of individuals and is often impractical to scale. Recent developments in automated image-based tracking offers opportunities to remotely quantify and understand individual behavior at scales and resolutions not previously possible, providing an essential supplement to other tracking methodologies in ecology. Automated image-based tracking should continue to advance the field of ecology by enabling better understanding of the linkages between individual and higher-level ecological processes, via high-throughput quantitative analysis of complex ecological patterns and processes across scales, including analysis of environmental drivers. Measuring behaviorIndividual behavior (see Glossary) underlies almost all aspects of ecology [1][2][3][4][5]. Accurate and highly resolved behavioral data are therefore critical for obtaining a mechanistic and predictive understanding of ecological systems [5]. Historically, direct observation by trained biologists was used to quantify behavior [6,7]. However, the extent and resolution to which direct observations can be made is highly constrained [8] and the number of individuals that can be observed simultaneously is small. In addition, an exact record of events is not preserved, only the biologist's subjective account of them.Recent technological advances in tracking now make it possible to collect large amounts of highly precise and accurate behavioral data. For many organisms equipment can be attached that provide information about the Glossary Background subtraction: a method used by software to compare the current video frame with a stored picture of the background; any pixel of the current frame that is significantly different from the corresponding pixel in the background is likely to be associated with the body of an animal. Useful in situations where the background is unchanging, for example, when the surface of the background is rigid and lighting does not change. Behavior: the actions of individuals, often in response to stimuli. Behavior can involve movement of the individual's body through space, such as walking or chasing, or can occur while the animal is stationary, such as grooming or eating. Bio-logging: attachment or implantation of equipment to organisms to provide information about their identity, location, behavior, or physiology (e.g., global positioning systems, accelerometers, video cameras, telemetry tags). Ecological interaction: any interaction between an organism and its environment, or between two organisms (i.e., including interactions between conspecifics). Fingerprinting: a method used to identify unmarked individuals using natural variation in their physical and/or behavioral appearance. The method works by transforming the images of each individual ...

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Object preference by walking fruit flies, Drosophila melanogaster, is mediated by vision and graviperception

Cited by 72 publications

References 36 publications

Social structures depend on innate determinants and chemosensory processing inDrosophila

Social structures depend on innate determinants and chemosensory processing inDrosophila

Nonlinear Dynamics Support a Linear Population Code in a Retinal Target-Tracking Circuit

Automated image-based tracking and its application in ecology

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