The Dominant Role of Visual Motion Cues in Bumblebee Flight Control Revealed Through Virtual Reality

Brightness cues affect gap negotiation behaviours in zebra finches flying between perches

Flying animals have had to evolve robust and effective guidance strategies for dealing with habitat clutter. Birds and insects use optic flow expansion cues to sense and avoid obstacles, but orchid bees have also been shown to use brightness cues during gap negotiation. Such brightness cues might therefore be of general importance in structuring visually guided flight behaviours. To test the hypothesis that brightness cues also affect gap negotiation behaviours in birds, we presented captive zebra finches Taeniopygia guttata with a symmetric or asymmetric background brightness distribution on the other side of a tunnel. The background brightness conditions influenced both the birds' decision to enter the tunnel aperture, and their flight direction upon exit. Zebra finches were more likely to initiate flight through the tunnel if they could see a bright background through it; they were also more likely to fly to the bright side upon exiting. We found no evidence of the centring response that would be expected if optic flow cues were balanced bilaterally during gap negotiation. Instead, the birds entered the tunnel by targeting a clearance of approximately one wing length from its near edge. Brightness cues therefore affect how zebra finches structure their flight when negotiating gaps in enclosed environments.

Finding the gap: neuromorphic motion-vision in dense environments

Animals have evolved mechanisms to travel safely and efficiently within different habitats. On a journey in dense terrains animals avoid collisions and cross narrow passages while controlling an overall course. Multiple hypotheses target how animals solve challenges faced during such travel. Here we show that a single mechanism enables safe and efficient travel. We developed a robot inspired by insects. It has remarkable capabilities to travel in dense terrain, avoiding collisions, crossing gaps and selecting safe passages. These capabilities are accomplished by a neuromorphic network steering the robot toward regions of low apparent motion. Our system leverages knowledge about vision processing and obstacle avoidance in insects. Our results demonstrate how insects might safely travel through diverse habitats. We anticipate our system to be a working hypothesis to study insects' travels in dense terrains. Furthermore, it illustrates that we can design novel hardware systems by understanding the underlying mechanisms driving behaviour.

Efficient visual learning by bumble bees in virtual-reality conditions: Size does not matter

Recent developments allowed establishing virtual-reality (VR) setups to study multiple aspects of visual learning in honey bees under controlled experimental conditions. Here, we adopted a VR environment to investigate the visual learning in the buff-tailed bumble bee Bombus terrestris. Based on responses to appetitive and aversive reinforcements used for conditioning, we show that bumble bees had the proper appetitive motivation to engage in the VR experiments and that they learned efficiently elemental color discriminations. In doing so, they reduced the latency to make a choice, increased the proportion of direct paths toward the virtual stimuli and walked faster toward them. Performance in a short-term retention test showed that bumble bees chose and fixated longer on the correct stimulus in the absence of reinforcement. Body size and weight, although variable across individuals, did not affect cognitive performances and had a mild impact on motor performances. Overall, we show that bumble bees are suitable experimental subjects for experiments on visual learning under VR conditions, which opens important perspectives for invasive studies on the neural and molecular bases of such learning given the robustness of these insects and the accessibility of their brain.

Matched function of the neuropil processing optic flow in flies and crabs: the lobula plate mediates optomotor responses in Neohelice granulata

When an animal rotates (whether it is an arthropod, a fish, a bird or a human) a drift of the visual panorama occurs over its retina, termed optic flow. The image is stabilized by compensatory behaviours (driven by the movement of the eyes, head or the whole body depending on the animal) collectively termed optomotor responses. The dipteran lobula plate has been consistently linked with optic flow processing and the control of optomotor responses. Crabs have a neuropil similarly located and interconnected in the optic lobes, therefore referred to as a lobula plate too. Here we show that the crabs' lobula plate is required for normal optomotor responses since the response was lost or severely impaired in animals whose lobula plate had been lesioned. The effect was behaviour-specific, since avoidance responses to approaching visual stimuli were not affected. Crabs require simpler optic flow processing than flies (because they move slower and in two-dimensional instead of three-dimensional space), consequently their lobula plates are relatively smaller. Nonetheless, they perform the same essential role in the visual control of behaviour. Our findings add a fundamental piece to the current debate on the evolutionary relationship between the lobula plates of insects and crustaceans.

Short-term exposure to heatwave-like temperatures affects learning and memory in bumblebees

Global warming has been identified as a key driver of bee declines around the world. While it is clear that elevated temperatures during the spring and summer months-the principal activity period of many bee species-is a factor in this decline, exactly how temperature affects bee survival is unknown. In vertebrates, there is clear evidence that elevated ambient temperatures impair cognition but whether and how heat affects the cognitive abilities of invertebrates remains unclear. Cognitive skills in bees are essential for their survival as, to supply the hive with nutrition, workers must be able to learn and remember the location of the most rewarding floral resources. Here, we investigate whether temperature-related cognitive impairments could be a driver of bee declines by exploring the effect of short-term increases in ambient temperature on learning and memory. We found that, in comparison to bees that were tested at 25°C (a temperature that they would typically experience in summer), bees that were exposed to 32°C (a temperature that they will becoming increasingly exposed to during heatwave events) were significantly worse at forming an association between a coloured light and a sucrose reward and that their capacity to remember this association after just 1 h was abolished. This study provides novel experimental evidence that even just a few hours of exposure to heatwave-like temperatures can severely impair the cognitive performance of insects. Such temperature-induced cognitive deficits could play an important role in explaining recent and future bee population declines.

The Neural Signature of Visual Learning Under Restrictive Virtual-Reality Conditions

Honey bees are reputed for their remarkable visual learning and navigation capabilities. These capacities can be studied in virtual reality (VR) environments, which allow studying performances of tethered animals in stationary flight or walk under full control of the sensory environment. Here, we used a 2D VR setup in which a tethered bee walking stationary under restrictive closed-loop conditions learned to discriminate vertical rectangles differing in color and reinforcing outcome. Closed-loop conditions restricted stimulus control to lateral displacements. Consistently with prior VR analyses, bees learned to discriminate the trained stimuli. Ex vivo analyses on the brains of learners and non-learners showed that successful learning led to a downregulation of three immediate early genes in the main regions of the visual circuit, the optic lobes (OLs) and the calyces of the mushroom bodies (MBs). While Egr1 was downregulated in the OLs, Hr38 and kakusei were coincidently downregulated in the calyces of the MBs. Our work thus reveals that color discrimination learning induced a neural signature distributed along the sequential pathway of color processing that is consistent with an inhibitory trace. This trace may relate to the motor patterns required to solve the discrimination task, which are different from those underlying pathfinding in 3D VR scenarios allowing for navigation and exploratory learning and which lead to IEG upregulation.

A new innovative real-time tracking method for flying insects applicable under natural conditions

Background

Sixty percent of all species are insects, yet despite global efforts to monitor animal movement patterns, insects are continuously underrepresented. This striking difference between species richness and the number of species monitored is not due to a lack of interest but rather to the lack of technical solutions. Often the accuracy and speed of established tracking methods is not high enough to record behavior and react to it experimentally in real-time, which applies in particular to small flying animals.

Results

Our new method of real-time tracking relates to frequencies of solar radiation which are almost completely absorbed by traveling through the atmosphere. For tracking, photoluminescent tags with a peak emission (1400 nm), which lays in such a region of strong absorption through the atmosphere, were attached to the animals. The photoluminescent properties of passivated lead sulphide quantum dots were responsible for the emission of light by the tags and provide a superb signal-to noise ratio. We developed prototype markers with a weight of 12.5 mg and a diameter of 5 mm. Furthermore, we developed a short wave infrared detection system which can record and determine the position of an animal in a heterogeneous environment with a delay smaller than 10 ms. With this method we were able to track tagged bumblebees as well as hawk moths in a flight arena that was placed outside on a natural meadow.

Conclusion

Our new method eliminates the necessity of a constant or predictable environment for many experimental setups. Furthermore, we postulate that the developed matrix-detector mounted to a multicopter will enable tracking of small flying insects, over medium range distances (>1000m) in the near future because: a) the matrix-detector equipped with an 70 mm interchangeable lens weighs less than 380 g, b) it evaluates the position of an animal in real-time and c) it can directly control and communicate with electronic devices.

Motion cues from the background influence associative color learning of honey bees in a virtual-reality scenario

Honey bees exhibit remarkable visual learning capacities, which can be studied using virtual reality (VR) landscapes in laboratory conditions. Existing VR environments for bees are imperfect as they provide either open-loop conditions or 2D displays. Here we achieved a true 3D environment in which walking bees learned to discriminate a rewarded from a punished virtual stimulus based on color differences. We included ventral or frontal background cues, which were also subjected to 3D updating based on the bee movements. We thus studied if and how the presence of such motion cues affected visual discrimination in our VR landscape. Our results showed that the presence of frontal, and to a lesser extent, of ventral background motion cues impaired the bees' performance. Whenever these cues were suppressed, color discrimination learning became possible. We analyzed the specific contribution of foreground and background cues and discussed the role of attentional interference and differences in stimulus salience in the VR environment to account for these results. Overall, we show how background and target cues may interact at the perceptual level and influence associative learning in bees. In addition, we identify issues that may affect decision-making in VR landscapes, which require specific control by experimenters.

Visual and movement memories steer foraging bumblebees along habitual routes

One persistent question in animal navigation is how animals follow habitual routes between their home and a food source. Our current understanding of insect navigation suggests an interplay between visual memories, collision avoidance and path integration, the continuous integration of distance and direction travelled. However, these behavioural modules have to be continuously updated with instantaneous visual information. In order to alleviate this need, the insect could learn and replicate habitual movements ('movement memories') around objects (e.g. a bent trajectory around an object) to reach its destination. We investigated whether bumblebees, Bombus terrestris, learn and use movement memories en route to their home. Using a novel experimental paradigm, we habituated bumblebees to establish a habitual route in a flight tunnel containing 'invisible' obstacles. We then confronted them with conflicting cues leading to different choice directions depending on whether they rely on movement or visual memories. The results suggest that they use movement memories to navigate, but also rely on visual memories to solve conflicting situations. We investigated whether the observed behaviour was due to other guidance systems, such as path integration or optic flow-based flight control, and found that neither of these systems was sufficient to explain the behaviour.

Looking at lateralization as a dynamic and plastic feature of nervous systems

In the last few decades, research on lateralization has expanded our knowledge about the manifestation, development, and mechanisms of this fascinating feature of nervous systems. This has been possible not only thanks to human studies, but to the use of animal models and the introduction of ground-breaking techniques within this research field. However, recent studies have also demonstrated how complex this phenomenon is and highlighted that we still lack a complete understanding of brain and behavioural asymmetries. Here, I comment on two of the challenges presented by Ocklenburg and colleagues that research on lateralization has to face in the next future. I argue that, in order to improve our understanding of lateralization, we have to consider it as a dynamic and plastic characteristic, which is strongly influenced by both internal factors, such as an animal's motivation and emotional states, and external factors, including the physical environment and the social context.

Analysing Head-Thorax Choreography During Free-Flights in Bumblebees

Animals coordinate their various body parts, sometimes in elaborate manners to swim, walk, climb, fly, and navigate their environment. The coordination of body parts is essential to behaviors such as, chasing, escaping, landing, and the extraction of relevant information. For example, by shaping the movement of the head and body in an active and controlled manner, flying insects structure their flights to facilitate the acquisition of distance information. They condense their turns into a short period of time (the saccade) interspaced by a relatively long translation (the intersaccade). However, due to technological limitations, the precise coordination of the head and thorax during insects' free-flight remains unclear. Here, we propose methods to analyse the orientation of the head and thorax of bumblebees Bombus terrestris, to segregate the trajectories of flying insects into saccades and intersaccades by using supervised machine learning (ML) techniques, and finally to analyse the coordination between head and thorax by using artificial neural networks (ANN). The segregation of flights into saccades and intersaccades by ML, based on the thorax angular velocities, decreased the misclassification by 12% compared to classically used methods. Our results demonstrate how machine learning techniques can be used to improve the analyses of insect flight structures and to learn about the complexity of head-body coordination. We anticipate our assay to be a starting point for more sophisticated experiments and analysis on freely flying insects. For example, the coordination of head and body movements during collision avoidance, chasing behavior, or negotiation of gaps could be investigated by monitoring the head and thorax orientation of freely flying insects within and across behavioral tasks, and in different species.

Characterizing long-range search behavior in Diptera using complex 3D virtual environments

The exemplary search capabilities of flying insects have established them as one of the most diverse taxa on Earth. However, we still lack the fundamental ability to quantify, represent, and predict trajectories under natural contexts to understand search and its applications. For example, flying insects have evolved in complex multimodal three-dimensional (3D) environments, but we do not yet understand which features of the natural world are used to locate distant objects. Here, we independently and dynamically manipulate 3D objects, airflow fields, and odor plumes in virtual reality over large spatial and temporal scales. We demonstrate that flies make use of features such as foreground segmentation, perspective, motion parallax, and integration of multiple modalities to navigate to objects in a complex 3D landscape while in flight. We first show that tethered flying insects of multiple species navigate to virtual 3D objects. Using the apple fly Rhagoletis pomonella, we then measure their reactive distance to objects and show that these flies use perspective and local parallax cues to distinguish and navigate to virtual objects of different sizes and distances. We also show that apple flies can orient in the absence of optic flow by using only directional airflow cues, and require simultaneous odor and directional airflow input for plume following to a host volatile blend. The elucidation of these features unlocks the opportunity to quantify parameters underlying insect behavior such as reactive space, optimal foraging, and dispersal, as well as develop strategies for pest management, pollination, robotics, and search algorithms.