Andrew D. Straw

Active flight increases the gain of visual motion processing in Drosophila

We developed a technique for performing whole-cell patch-clamp recordings from genetically identified neurons in behaving Drosophila. We focused on the properties of visual interneurons during tethered flight, but this technique generalizes to different cell types and behaviors. We found that the peak-to-peak responses of a class of visual motion–processing interneurons, the vertical-system visual neurons (VS cells), doubled when flies were flying compared with when they were at rest. Thus, the gain of the VS cells is not fixed, but is instead behaviorally flexible and changes with locomotor state. Using voltage clamp, we found that the passive membrane resistance of VS cells was reduced during flight, suggesting that the elevated gain was a result of increased synaptic drive from upstream motion-sensitive inputs. The ability to perform patch-clamp recordings in behaving Drosophila promises to help unify the understanding of behavior at the gene, cell and circuit levels.

Vision Egg: an open-source library for realtime visual stimulus generation

Modern computer hardware makes it possible to produce visual stimuli in ways not previously possible. Arbitrary scenes, from traditional sinusoidal gratings to naturalistic 3D scenes can now be specified on a frame-by-frame basis in realtime. A programming library called the Vision Egg that aims to make it easy to take advantage of these innovations. The Vision Egg is a free, open-source library making use of OpenGL and written in the high-level language Python with extensions in C. Careful attention has been paid to the issues of luminance and temporal calibration, and several interfacing techniques to input devices such as mice, movement tracking systems, and digital triggers are discussed. Together, these make the Vision Egg suitable for many psychophysical, electrophysiological, and behavioral experiments. This software is available for free download at visionegg.org.

Automated image-based tracking and its application in ecology

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.

Multi-camera real-time three-dimensional tracking of multiple flying animals

Automated tracking of animal movement allows analyses that would not otherwise be possible by providing great quantities of data. The additional capability of tracking in real time—with minimal latency—opens up the experimental possibility of manipulating sensory feedback, thus allowing detailed explorations of the neural basis for control of behaviour. Here, we describe a system capable of tracking the three-dimensional position and body orientation of animals such as flies and birds. The system operates with less than 40 ms latency and can track multiple animals simultaneously. To achieve these results, a multi-target tracking algorithm was developed based on the extended Kalman filter and the nearest neighbour standard filter data association algorithm. In one implementation, an 11-camera system is capable of tracking three flies simultaneously at 60 frames per second using a gigabit network of nine standard Intel Pentium 4 and Core 2 Duo computers. This manuscript presents the rationale and details of the algorithms employed and shows three implementations of the system. An experiment was performed using the tracking system to measure the effect of visual contrast on the flight speed of Drosophila melanogaster. At low contrasts, speed is more variable and faster on average than at high contrasts. Thus, the system is already a useful tool to study the neurobiology and behaviour of freely flying animals. If combined with other techniques, such as ‘virtual reality’-type computer graphics or genetic manipulation, the tracking system would offer a powerful new way to investigate the biology of flying animals.

A Simple Vision-Based Algorithm for Decision Making in Flying Drosophila

Animals must quickly recognize objects in their environment and act accordingly. Previous studies indicate that looming visual objects trigger avoidance reflexes in many species [1-5]; however, such reflexes operate over a close range and might not detect a threatening stimulus at a safe distance. We analyzed how fruit flies (Drosophila melanogaster) respond to simple visual stimuli both in free flight and in a tethered-flight simulator. Whereas Drosophila, like many other insects, are attracted toward long vertical objects [6-10], we found that smaller visual stimuli elicit not weak attraction but rather strong repulsion. Because aversion to small spots depends on the vertical size of a moving object, and not on looming, it can function at a much greater distance than expansion-dependent reflexes. The opposing responses to long stripes and small spots reflect a simple but effective object classification system. Attraction toward long stripes would lead flies toward vegetative perches or feeding sites, whereas repulsion from small spots would help them avoid aerial predators or collisions with other insects. The motion of flying Drosophila depends on a balance of these two systems, providing a foundation for studying the neural basis of behavioral choice in a genetic model organism.

Integrative Model of Drosophila Flight

This paper presents a framework for simulating the flight dynamics and control strategies of the fruit fly Drosophila melanogaster. The framework consists of five main components: an articulated rigid-body simulation, a model of the aerodynamic forces and moments, a sensory systems model, a control model, and an environment model. In the rigid-body simulation the fly is represented by a system of three rigid bodies connected by a pair of actuated ball joints. At each instant of the simulation, the aerodynamic forces and moments acting on the wings and body of the fly are calculated using an empirically derived quasi-steady model. The pattern of wing kinematics is based on data captured from high-speed video sequences. The forces and moments produced by the wings are modulated by deforming the base wing kinematics along certain characteristic actuation modes. Models of the fly’s visual and mechanosensory systems are used to generate inputs to a controller that sets the magnitude of each actuation mode, thus modulating the forces produced by the wings. This simulation framework provides a quantitative test bed for examining the possible control strategies employed by flying insects. Examples demonstrating pitch rate, velocity, altitude, and flight speed control, as well as visually guided centering in a corridor are presented.

Visual Control of Altitude in Flying Drosophila

Unlike creatures that walk, flying animals need to control their horizontal motion as well as their height above the ground. Research on insects, the first animals to evolve flight, has revealed several visual reflexes that are used to govern horizontal course. For example, insects orient toward prominent vertical features in their environment [1-5] and generate compensatory reactions to both rotations [6, 7] and translations [1, 8-11] of the visual world. Insects also avoid impending collisions by veering away from visual expansion [9, 12-14]. In contrast to this extensive understanding of the visual reflexes that regulate horizontal course, the sensory-motor mechanisms that animals use to control altitude are poorly understood. Using a 3D virtual reality environment, we found that Drosophila utilize three reflexes--edge tracking, wide-field stabilization, and expansion avoidance--to control altitude. By implementing a dynamic visual clamp, we found that flies do not regulate altitude by maintaining a fixed value of optic flow beneath them, as suggested by a recent model [15]. The results identify a means by which insects determine their absolute height above the ground and uncover a remarkable correspondence between the sensory-motor algorithms used to regulate motion in the horizontal and vertical domains.

FlyMAD: rapid thermogenetic control of neuronal activity in freely walking Drosophila

Rapidly and selectively modulating the activity of defined neurons in unrestrained animals is a powerful approach in investigating the circuit mechanisms that shape behavior. In Drosophila melanogaster, temperature-sensitive silencers and activators are widely used to control the activities of genetically defined neuronal cell types. A limitation of these thermogenetic approaches, however, has been their poor temporal resolution. Here we introduce FlyMAD (the fly mind-altering device), which allows thermogenetic silencing or activation within seconds or even fractions of a second. Using computer vision, FlyMAD targets an infrared laser to freely walking flies. As a proof of principle, we demonstrated the rapid silencing and activation of neurons involved in locomotion, vision and courtship. The spatial resolution of the focused beam enabled preferential targeting of neurons in the brain or ventral nerve cord. Moreover, the high temporal resolution of FlyMAD allowed us to discover distinct timing relationships for two neuronal cell types previously linked to courtship song.

Contrast sensitivity of insect motion detectors to natural images.

How do animals regulate self-movement despite large variation in the luminance contrast of the environment? Insects are capable of regulating flight speed based on the velocity of image motion, but the mechanisms for this are unclear. The Hassenstein-Reichardt correlator model and elaborations can accurately predict responses of motion detecting neurons under many conditions but fail to explain the apparent lack of spatial pattern and contrast dependence observed in freely flying bees and flies. To investigate this apparent discrepancy, we recorded intracellularly from horizontal-sensitive (HS) motion detecting neurons in the hoverfly while displaying moving images of natural environments. Contrary to results obtained with grating patterns, we show these neurons encode the velocity of natural images largely independently of the particular image used despite a threefold range of contrast. This invariance in response to natural images is observed in both strongly and minimally motion-adapted neurons but is sensitive to artificial manipulations in contrast. Current models of these cells account for some, but not all, of the observed insensitivity to image contrast. We conclude that fly visual processing may be matched to commonalities between natural scenes, enabling accurate estimates of velocity largely independent of the particular scene.

Flying Drosophila stabilize their vision-based velocity controller by sensing wind with their antennae

Significance Insects are widely appreciated for their aerial agility, but the organization of their control system is not well understood. In particular, it is not known how they rapidly integrate information from different sensory systems—such as their eyes and antennae—to regulate flight speed. Although vision may provide an estimate of the true groundspeed in the presence of wind, delays inherent in visual processing compromise the performance of the flight speed regulator and make the animal unstable. Mechanoreceptors on the antennae of flies cannot measure groundspeed directly, but can detect changes in airspeed more quickly. By integrating information from both senses, flies achieve stable regulation of flight speed that is robust to perturbations such as gusts of wind. Flies and other insects use vision to regulate their groundspeed in flight, enabling them to fly in varying wind conditions. Compared with mechanosensory modalities, however, vision requires a long processing delay (~100 ms) that might introduce instability if operated at high gain. Flies also sense air motion with their antennae, but how this is used in flight control is unknown. We manipulated the antennal function of fruit flies by ablating their aristae, forcing them to rely on vision alone to regulate groundspeed. Arista-ablated flies in flight exhibited significantly greater groundspeed variability than intact flies. We then subjected them to a series of controlled impulsive wind gusts delivered by an air piston and experimentally manipulated antennae and visual feedback. The results show that an antenna-mediated response alters wing motion to cause flies to accelerate in the same direction as the gust. This response opposes flying into a headwind, but flies regularly fly upwind. To resolve this discrepancy, we obtained a dynamic model of the fly’s velocity regulator by fitting parameters of candidate models to our experimental data. The model suggests that the groundspeed variability of arista-ablated flies is the result of unstable feedback oscillations caused by the delay and high gain of visual feedback. The antenna response drives active damping with a shorter delay (~20 ms) to stabilize this regulator, in exchange for increasing the effect of rapid wind disturbances. This provides insight into flies’ multimodal sensory feedback architecture and constitutes a previously unknown role for the antennae.