Tracking small laboratory animals such as flies, fish, and worms is used for phenotyping in neuroscience, genetics, disease modelling, and drug discovery. An imaging system with sufficient throughput and spatiotemporal resolution would be capable of imaging a large number of animals, estimating their pose, and quantifying detailed behavioural differences at a scale where hundreds of treatments could be tested simultaneously. Here we report an array of six 12-megapixel cameras that record all the wells of a 96-well plate with sufficient resolution to estimate the pose of C. elegans worms and to extract high-dimensional phenotypic fingerprints. We use the system to study behavioural variability across wild isolates, the sensitisation of worms to repeated blue light stimulation, the phenotypes of worm disease models, and worms’ behavioural responses to drug treatment. Because the system is compatible with standard multiwell plates, it makes computational ethological approaches accessible in existing high-throughput pipelines.

Standard animal behavior paradigms incompletely mimic nature and thus limit our understanding of behavior and brain function. Virtual reality (VR) can help, but it poses challenges. Typical VR systems require movement restrictions but disrupt sensorimotor experience, causing neuronal and behavioral alterations. We report the development of FreemoVR, a VR system for freely moving animals. We validate immersive VR for mice, flies, and zebrafish. FreemoVR allows instant, disruption-free environmental reconfigurations and interactions between real organisms and computer-controlled agents. Using the FreemoVR platform, we established a height-aversion assay in mice and studied visuomotor effects in Drosophila and zebrafish. Furthermore, by photorealistically mimicking zebrafish we discovered that effective social influence depends on a prospective leader balancing its internally preferred directional choice with social interaction. FreemoVR technology facilitates detailed investigations into neural function and behavior through the precise manipulation of sensorimotor feedback loops in unrestrained animals.

Rapid technical advances in the field of computer animation (CA) and virtual reality (VR) have opened new avenues in animal behavior research. Animated stimuli are powerful tools as they offer standardization, repeatability, and complete control over the stimulus presented, thereby "reducing" and "replacing" the animals used, and "refining" the experimental design in line with the 3Rs. However, appropriate use of these technologies raises conceptual and technical questions. In this review, we offer guidelines for common technical and conceptual considerations related to the use of animated stimuli in animal behavior research. Following the steps required to create an animated stimulus, we discuss (I) the creation, (II) the presentation, and (III) the validation of CAs and VRs. Although our review is geared toward computer-graphically designed stimuli, considerations on presentation and validation also apply to video playbacks. CA and VR allow both new behavioral questions to be addressed and existing questions to be addressed in new ways, thus we expect a rich future for these methods in both ultimate and proximate studies of animal behavior.

As part of a wide study on the role of neuropeptides in the visuo-motor behavior of Drosophila melanogaster, we exposed three fly strains with impaired neuropeptide degradation function, and corresponding controls, to different visual stimuli. Find further details in the provided README.

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.

Neuroscientists are using virtual reality systems, combined with other advances such as new molecular genetic tools and brain-recording technologies, to reveal how neuronal circuits process and act on visual information. The Web extra at http://youtu.be/e_BxdbNidyQ is an overview video showing the FlyVR system in operation, including four example experiments.

Event Abstract Back to Event Drosophila neurons involved in the integration of conflicting visual input Lisa M. Fenk1*, Karin Panser1, Andreas Poehlmann1, John Stowers1, 2 and Andrew D. Straw1 1 Research Institute of Molecular Pathology (IMP), Austria 2 Vienna University of Technology, Automation and Control Institute (ACIN), Austria Sensory input from different modalities, and even within a single sensory modality, can be conflicting. The desired resulting behavioral response may consist of a response to only one stimulus or a mixture of the responses that would be elicited by the individual stimuli alone. We investigated the interaction of two well characterized and potentially conflicting pathways in the fly: optomotor response and object approach. We measured the turning response of rigidly tethered Drosophila placed in a small arena and exposed to visual stimuli. In this study we confronted the flies with a random pixel stripe in artificial closed loop in front of a random pixel panoramic stimulus that is stationary for a few minutes and then starts to slowly oscillate sinusoidally. In a third phase the stripe is invisible and only the background movement is shown. As expected, flies display a pronounced stripe fixation behavior when the background is stationary, i.e. the stripe position is narrowly distributed around the position directly in front of the fly. In the conflict period, flies still show stripe fixation behavior but the stable position of the stripe is shifted about +/- 20 deg from the position in front of the fly, depending on the direction of the background movement. In addition, most flies switch from time to time to a more pronounced optomotor response resulting in the stripe to spin around the arena at a high velocity. With the Gal4-UAS system, we silenced specific visual interneurons and tested for associated deficits in the visually guided flight behavior. We describe a set of heterolateral interneurons that play a role in this switching behavior. If we inhibit chemical synapses by expressing the tetanus toxin light chain in the neurons, flies show a significantly better stripe fixation quality as compared to control flies, switching less to optomotor stabilization. Interestingly, our data suggest that the response to wide field motion in the absence of a target in closed loop is unaffected. This study pinpoints a set of visual interneurons that specifically influence the flies’ behavior in the conflict period and that might thus be interpreted as part of a visual decision making circuit in flies. Acknowledgements This work was supported by IMP core funding, ERC Starting Grant StG-2011-281884 and WWTF CS11-029 to ADS. Keywords: Drosophila, optomotor response, stripe fixation, neural circuits, conflicting stimuli Conference: International Conference on Invertebrate Vision, Fjälkinge, Sweden, 1 Aug - 8 Aug, 2013. Presentation Type: Poster presentation preferred Topic: The visual control of flight and locomotion Citation: Fenk LM, Panser K, Poehlmann A, Stowers J and Straw AD (2019). Drosophila neurons involved in the integration of conflicting visual input. Front. Physiol. Conference Abstract: International Conference on Invertebrate Vision. doi: 10.3389/conf.fphys.2013.25.00051 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 27 Feb 2013; Published Online: 09 Dec 2019. * Correspondence: Dr. Lisa M Fenk, Research Institute of Molecular Pathology (IMP), Vienna, 1030, Austria, lisa.fenk@rockefeller.edu Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Lisa M Fenk Karin Panser Andreas Poehlmann John Stowers Andrew D Straw Google Lisa M Fenk Karin Panser Andreas Poehlmann John Stowers Andrew D Straw Google Scholar Lisa M Fenk Karin Panser Andreas Poehlmann John Stowers Andrew D Straw PubMed Lisa M Fenk Karin Panser Andreas Poehlmann John Stowers Andrew D Straw Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

This paper describes a biologically inspired flight control strategy for unpiloted aerial vehicles (UAVs) using optical flow and depth information. The scene depth map is generated by measurement of optical flow in a uniform grid across the image. Grid regions are each analyzed singularly, each ones depth is estimated by comparing their observed motion with the motion of the craft as measured by the inertial measurement unit. Two control processes run simultaneously on the robot, a gross strategy uses divergence of optical flow vectors about the focus of expansion, balancing these according to a predefined divergence template. A fine strategy looks for outliers from the estimated depth map. The sum of these processes generates a control impulse to steer a quadrotor helicopter away from obstacles. The algorithm was tested against ground-truth datasets and real flight. In both situations it was able to control the craft heading and pitch during indoor operation, avoiding both corridor walls and oncoming obstacles.

Biologically inspired unpiloted aerial vehicle (UAV) flight control typically uses regulation of optical flow magnitude or distribution. These designs were motivated by research undertaken on insects that showed they too used optical flow in their flight. Recent research has suggested that optical flow alone can not sufficiently explain the altitude control behaviour of honeybees and houseflies; those insects must consider other environmental features like local edges. Inspired by these results, this paper presents a biomimetic altitude control and avoidance strategy which analyzes horizontal edges in the image and is able to successfully control the flight of a quadrotor helicopter in a simulated environment.

Reliable depth estimation is important to many autonomous robotic systems and visual control algorithms. The Microsoft Kinect is a new, low cost game controller peripheral that calculates a depth map of the environment with good accuracy and high rate. In this paper we use the calibrated output of the depth sensor to obtain an accurate absolute depth map. Subsequently by using a Randomized Hough Transform to detect the ground plane in this depth map we are able to autonomously hover a quadrotor helicopter.

Reliable depth estimation is a cornerstone of many autonomous robotic control systems. The Microsoft Kinect is a new, low cost, commodity game controller peripheral that calculates a depth map of the environment with good accuracy and high rate. In this paper we calibrate the Kinect depth and image sensors and then use the depth map to control the altitude of a quadrotor helicopter. This paper presents the first results of using this sensor in a real-time robotics control application.

This paper demonstrates that computer vision techniques can estimate the heading of a small fixed pitch four rotor helicopter. Heading estimates are computed using the optical flow technique of phase correlation on images captured using a down facing camera. The camera is fitted with an omnidirectional lens and the images are transformed into the log-polar domain before the main computational step. The vision algorithm runs at 10 Hz on a single board computer (SBC) mounted aboard the craft. Experimental performance of this system is compared with results obtained from a traditional inertial measurement unit (IMU). It is found that the yaw rate computed from the optical flow is comparable to the IMU and thus appropriate for use in controlling the helicopter.