Flies evade looming targets by executing rapid visually directed banked turns

A Bio-Inspired Model for Bee Simulations

As eusocial creatures, bees display unique macro collective behavior and local body dynamics that hold potential applications in various fields, such as computer animation, robotics, and social behavior. Unlike birds and fish, bees fly in a low-aligned zigzag pattern. Additionally, bees rely on visual signals for foraging and predator avoidance, exhibiting distinctive local body oscillations, such as body lifting, thrusting, and swaying. These inherent features pose significant challenges to realistic bee simulations in practical animation applications. In this article, we present a bio-inspired model for bee simulations capable of replicating both macro collective behavior and local body dynamics of bees. Our approach utilizes a visually-driven system to simulate a bee's local body dynamics, incorporating obstacle perception and body rolling control for effective collision avoidance. Moreover, we develop an oscillation rule that captures the dynamics of the bee's local bodies, drawing on insights from biological research. Our model extends beyond simulating individual bees' dynamics; it can also represent bee swarms by integrating a fluid-based field with the bees' innate noise and zigzag motions. To fine-tune our model, we utilize pre-collected honeybee flight data. Through extensive simulations and comparative experiments, we demonstrate that our model can efficiently generate realistic low-aligned and inherently noisy bee swarms.

Untethered subcentimeter flying robots

The miniaturization of insect-scale flying robots with untethered flights is extremely challenging as the tradeoff between mass and power becomes problematic. Here, a subcentimeter rotating-wing robot of 21 mg in weight and 9.4 mm in wingspan driven by a single-axis alternating magnetic field has accomplished navigable flights. This artificial flying robot is the lightest and smallest to realize untethered and controllable aerial travels including hovering, collision recovery, and route adjustments. Experimentally, it has achieved a high aerodynamic efficacy with a measured lift-to-drag ratio of 0.7 and lift-to-flying power ratio of 7.2 × 10-2 N/W at a Reynolds number of ~2500. The wireless driving mechanism, system operation principle, and flight characteristics can be further optimized for the advancement and miniaturization of subcentimeter scale flying robots.

NSF Workshop Report: Exploring Measurements and Interpretations of Intelligent Behaviors Across Animal Model Systems

Defining intelligence is a challenging and fraught task, but one that neuroscientists are repeatedly confronted with. A central goal of neuroscience is to understand how phenomena like intelligent behaviors emerge from nervous systems. This requires some determination of what defines intelligence and how to measure it. The challenge is multifaceted. For instance, as we begin to describe and understand the brain in increasingly specific physical terms (e.g., anatomy, cell types, activity patterns), we amplify an ever-growing divide in how we connect measurable properties of the brain to less tangible concepts like intelligence. As our appreciation for evolutionary diversity in neuroscience grows, we are further confronted with whether there can be a unifying theory of intelligence. The National Science Foundation (NSF) NeuroNex consortium recently gathered experts from multiple animal model systems to discuss intelligence across species. We summarize here the different perspectives offered by the consortium, with the goal of promoting thought and debate of this ancient question from a modern perspective, and asking whether defining intelligence is a useful exercise in neuroscience or an ill-posed and distracting question. We present data from the vantage points of humans, macaques, ferrets, crows, octopuses, bees, and flies, highlighting some of the noteworthy capabilities of each species within the context of each species' ecological niche and how these may be challenged by climate change. We also include a remarkable example of convergent evolution between primates and crows in the circuit and molecular basis for working memory in these highly divergent animal species.

Reynolds rules in swarm fly behavior based on KAN transformer tracking method

The analysis of complex flight patterns and collective behaviors in swarming insects has emerged as a significant focus across biological and computational fields. Tracking these insects, like fruit fly, presents persistent challenges due to their rapid motion patterns and frequent occlusions in densely populated environments. To address these challenges, we propose a tracking method using particle filter framework combined with a Kolmogorov-Arnold Network (KAN)-Transformer model to extract the global features and fine-grained features of the trajectory. Additionally, manually annotated ground truth datasets are established to enable thorough assessment of tracking methods. Experimental results demonstrate the effectiveness and robustness of our proposed tracking method. Analysis of tracked trajectories revealed the Reynolds rules of flocking behavior.

Single-cell type analysis of wing premotor circuits in the ventral nerve cord of Drosophila melanogaster

To perform most behaviors, animals must send commands from higher-order processing centers in the brain to premotor circuits that reside in ganglia distinct from the brain, such as the mammalian spinal cord or insect ventral nerve cord. How these circuits are functionally organized to generate the great diversity of animal behavior remains unclear. An important first step in unraveling the organization of premotor circuits is to identify their constituent cell types and create tools to monitor and manipulate these with high specificity to assess their functions. This is possible in the tractable ventral nerve cord of the fly. To generate such a toolkit, we used a combinatorial genetic technique (split-GAL4) to create 195 sparse transgenic driver lines targeting 196 individual cell types in the ventral nerve cord. These included wing and haltere motoneurons, modulatory neurons, and interneurons. Using a combination of behavioral, developmental, and anatomical analyses, we systematically characterized the cell types targeted in our collection. In addition, we identified correspondences between the cells in this collection and a recent connectomic data set of the ventral nerve cord. Taken together, the resources and results presented here form a powerful toolkit for future investigations of neuronal circuits and connectivity of premotor circuits while linking them to behavioral outputs.

Activity of a descending neuron associated with visually elicited flight saccades in Drosophila

Approaching threats are perceived through visual looming, a rapid expansion of an image on the retina. Visual looming triggers defensive responses such as freezing, flight, turning, or take-off in a wide variety of organisms, from mice to fish to insects.1,2,3,4 In response to looming, flies perform rapid evasive turns known as saccades.5 Saccades can also be initiated spontaneously to change direction during flight.6,7,8,9 Two types of descending neurons (DNs), DNaX and DNb01, were previously shown to exhibit activity correlated with both spontaneous and looming-elicited saccades in Drosophila.10,11 As they do not receive direct input from the visual system, it has remained unclear how visually elicited flight turns are controlled by the nervous system. DNp03 receives input from looming-sensitive visual projection neurons and provides output to wing motor neurons12,13 and is therefore a promising candidate for controlling flight saccades. Using whole-cell patch-clamp recordings from DNp03 in head-fixed flying Drosophila, we showed that DNp03 responds to ipsilateral visual looming in a behavioral-state-dependent manner. We further explored how DNp03 activity relates to the variable behavioral output. Sustained DNp03 activity, persisting after the visual stimulus, was the strongest predictor of saccade execution. However, DNp03 activity alone cannot fully explain the variability in behavioral responses. Combined with optogenetic activation experiments during free flight, these results suggest an important but not exclusive role for DNp03 in controlling saccades, advancing our understanding of how visual information is transformed into motor commands for rapid evasive maneuvers in flying insects.

Acrobatics at the insect scale: A durable, precise, and agile micro-aerial robot

Aerial insects are exceptionally agile and precise owing to their small size and fast neuromotor control. They perform impressive acrobatic maneuvers when evading predators, recovering from wind gust, or landing on moving objects. Flapping-wing propulsion is advantageous for flight agility because it can generate large changes in instantaneous forces and torques. During flapping-wing flight, wings, hinges, and tendons of pterygote insects endure large deformation and high stress hundreds of times each second, highlighting the outstanding flexibility and fatigue resistance of biological structures and materials. In comparison, engineered materials and microscale structures in subgram micro-aerial vehicles (MAVs) exhibit substantially shorter lifespans. Consequently, most subgram MAVs are limited to hovering for less than 10 seconds or following simple trajectories at slow speeds. Here, we developed a 750-milligram flapping-wing MAV that demonstrated substantially improved lifespan, speed, accuracy, and agility. With transmission and hinge designs that reduced off-axis torsional stress and deformation, the robot achieved a 1000-second hovering flight, two orders of magnitude longer than existing subgram MAVs. This robot also performed complex flight trajectories with under 1-centimeter root mean square error and more than 30 centimeters per second average speed. With a lift-to-weight ratio of 2.2 and a maximum ascending speed of 100 centimeters per second, this robot demonstrated double body flips at a rotational rate exceeding that of the fastest aerial insects and larger MAVs. These results highlight insect-like flight endurance, precision, and agility in an at-scale MAV, opening opportunities for future research on sensing and power autonomy.

Kinematics and Flow Field Analysis of Allomyrina dichotoma Flight

In recent years, bioinspired insect flight has become a prominent research area, with a particular focus on beetle-inspired aerial vehicles. Studying the unique flight mechanisms and structural characteristics of beetles has significant implications for the optimization of biomimetic flying devices. Among beetles, Allomyrina dichotoma (rhinoceros beetle) exhibits a distinct wing deployment-flight-retraction sequence, whereby the interaction between the hindwings and protective elytra contributes to lift generation and maintenance. This study investigates A. dichotoma's wing deployment, flight, and retraction behaviors through motion analysis, uncovering the critical role of the elytra in wing folding. We capture the kinematic parameters throughout the entire flight process and develop an accurate kinematic model of A. dichotoma flight. Using smoke visualization, we analyze the flow field generated during flight, revealing the formation of enhanced leading-edge vortices and attached vortices during both upstroke and downstroke phases. These findings uncover the high-lift mechanism underlying A. dichotoma's flight dynamics, offering valuable insights for optimizing beetle-inspired micro aerial vehicles.

NeuroMechFly v2: simulating embodied sensorimotor control in adult Drosophila

Discovering principles underlying the control of animal behavior requires a tight dialogue between experiments and neuromechanical models. Such models have primarily been used to investigate motor control with less emphasis on how the brain and motor systems work together during hierarchical sensorimotor control. NeuroMechFly v2 expands Drosophila neuromechanical modeling by enabling vision, olfaction, ascending motor feedback and complex terrains that can be navigated using leg adhesion. We illustrate its capabilities by constructing biologically inspired controllers that use ascending feedback to perform path integration and head stabilization. After adding vision and olfaction, we train a controller using reinforcement learning to perform a multimodal navigation task. Finally, we illustrate more bio-realistic modeling involving complex odor plume navigation, and fly-fly following using a connectome-constrained visual network. NeuroMechFly can be used to accelerate the discovery of explanatory models of the nervous system and to develop machine learning-based controllers for autonomous artificial agents and robots.

Spatial and temporal characteristics of laboratory-induced Anopheles coluzzii swarms: Shape, structure, and flight kinematics

Malaria mosquitoes mate in swarms, but how these swarms are formed and maintained remains poorly understood. We characterized three-dimensional spatiotemporal flight kinematics of Anopheles coluzzii males swarming at sunset above a ground marker. The location, shape, and volume of swarms were highly stereotypic, consistent over the complete swarming duration. Swarms have an elliptical cone shape; mean flight kinematics varies spatially within the swarm, but remain rather consistent throughout swarming duration. Using a sensory system-informed model, we show that swarming mosquitoes use visual perception of both the ground marker and sunset horizon to display the swarming behavior. To control their height, swarming individuals maintain an optical angle of the marker ranging from 24° to 55°. Limiting the viewing angle deviation to 4.5% of the maximum value results in the observed elliptical cone swarm shape. We discuss the implications of these finding on malaria mosquito mating success, speciation and for vector control.

Mosquitoes integrate visual and acoustic cues to mediate conspecific interactions in swarms

Male mosquitoes form aerial aggregations, known as swarms, to attract females and maximize their chances of finding a mate. Within these swarms, individuals must be able to recognize potential mates and navigate the social environment to successfully intercept a mating partner. Prior research has almost exclusively focused on the role of acoustic cues in mediating the male mosquito's ability to recognize and pursue females. However, the role of other sensory modalities in this behavior has not been explored. Moreover, how males avoid collisions with one another in the swarm while pursuing females remains poorly understood. In this study, we combined free-flight and tethered-flight simulator experiments to demonstrate that swarming Anopheles coluzzii mosquitoes integrate visual and acoustic information to track conspecifics and avoid collisions. Our tethered experiments revealed that acoustic stimuli gated mosquito steering responses to visual objects simulating nearby mosquitoes, especially in males that exhibited a strong response toward visual objects in the presence of female flight tones. Additionally, we observed that visual cues alone could trigger changes in mosquitoes' wingbeat amplitude and frequency. These findings were corroborated by our free-flight experiments, which revealed that Anopheles coluzzii modulate their thrust-based flight responses to nearby conspecifics in a similar manner to tethered animals, potentially allowing for collision avoidance within swarms. Together, these results demonstrate that both males and females integrate multiple sensory inputs to mediate swarming behavior, and for males, the change in flight kinematics in response to multimodal cues might allow them to simultaneously track females while avoiding collisions.

Flies tune the activity of their multifunctional gyroscope

Members of the order Diptera, the true flies, are among the most maneuverable flying animals. These aerial capabilities are partially attributed to flies' possession of halteres, tiny club-shaped structures that evolved from the hindwings and play a crucial role in flight control. Halteres are renowned for acting as biological gyroscopes that rapidly detect rotational perturbations and help flies maintain a stable flight posture. Additionally, halteres provide rhythmic input to the wing steering system that can be indirectly modulated by the visual system. The multifunctional capacity of the haltere is thought to depend on arrays of embedded mechanosensors called campaniform sensilla that are arranged in distinct groups on the haltere's dorsal and ventral surfaces. Although longstanding hypotheses suggest that each array provides different information relevant to the flight control circuitry, we know little about how the haltere campaniforms are functionally organized. Here, we use in vivo calcium imaging during tethered flight to obtain population-level recordings of the haltere sensory afferents in specific fields of sensilla. We find that haltere feedback from both dorsal fields is continuously active, modulated under closed-loop flight conditions, and recruited during saccades to help flies actively maneuver. We also find that the haltere's multifaceted role may arise from the steering muscles of the haltere itself, regulating haltere stroke amplitude to modulate campaniform activity. Taken together, our results underscore the crucial role of efferent control in regulating sensor activity and provide insight into how the sensory and motor systems of flies coevolved.

Network Topology of Wing Veins in Hawaiian Flies Mitigates Allometric Dilemma

Specific Hawaiian fruit flies have an extra crossvein (ECV) in the wing vein network which connects contiguously with another crossvein and forms a unique cruciform topology. These flies are distinguished by their large wings and their allometrically small vein diameters compared to those of typical fruit flies. Small vein diameters may increase frictional energy loss during internal blood transport, although they lead to an improvement in the wing's moment of inertia. Our hypothesis was that the ECV's presence would reduce the hydraulic resistance of the entire vein network. To investigate the hemodynamic effects of its presence, the flow rate of blood and frictional pressure loss within the vein networks was simulated by modeling them as hydraulic circuits. The results showed a 3.1% reduction in pressure loss owing to the network topology created by the presence of the ECV. This vein and its contiguous crossvein diverted part of the blood from the wing veins topologically parallel to them, reducing the pressure loss in these bypassed veins. The contiguity of the ECV to the other crossvein provided the shortest blood transfer route and lowest pressure drop between these crossveins. The results suggest that the presence of the ECV may counterbalance the heightened resistance caused by constricted veins.

Wing deformation improves aerodynamic performance of forward flight of bluebottle flies flying in a flight mill

Insect wings are flexible structures that exhibit deformations of complex spatiotemporal patterns. Existing studies on wing deformation underscore the indispensable role of wing deformation in enhancing aerodynamic performance. Here, we investigated forward flight in bluebottle flies, flying semi-freely in a magnetic flight mill; we quantified wing surface deformation using high-speed videography and marker-less surface reconstruction and studied the effects on aerodynamic forces, power and efficiency using computational fluid dynamics. The results showed that flies' wings exhibited substantial camber near the wing root and twisted along the wingspan, as they were coupled effects of deflection primarily about the claval flexion line. Such deflection was more substantial for supination during the upstroke when most thrust was produced. Compared with deformed wings, the undeformed wings generated 59-98% of thrust and 54-87% of thrust efficiency (i.e. ratio of thrust and power). Wing twist moved the aerodynamic centre of pressure proximally and posteriorly, likely improving aerodynamic efficiency.

Flight behaviour diverges more between seasonal forms than between species in Pieris butterflies

In flying animals, wing morphology is typically assumed to influence flight behaviours. Whether seasonal polymorphism in butterfly morphology is linked to adaptive flight behaviour remains unresolved. Here, we compare the flight behaviours and wing morphologies of the spring and summer forms of two closely related butterfly species, Pieris napi and P. rapae. We first quantify three-dimensional flight behaviour by reconstructing individual flight trajectories using stereoscopic high-speed videography in an experimental outdoor cage. We then measure wing size and shape, which are characteristics assumed to influence flight behaviours in butterflies. We show that seasonal, but not interspecific, differences in flight behaviour might be associated with divergent forewing shapes. During spring, Pieris individuals are small and have elongated forewings, and generally fly at low speed and acceleration, while having a high flight curvature. On the contrary, summer individuals are larger and exhibit rounded forewings. They fly at high speed and acceleration, while having high turning acceleration and advance ratio. Our study provides one of the first quantitative pieces of evidence of different flight behaviours between seasonal forms of two Pieris butterfly species. We discuss the possibility that this co-divergence in flight behaviour and morphology is an adaptation to distinct seasonal environments. Properly identifying the mechanisms underpinning such divergence, nonetheless, requires further investigations to disentangle the interacting effects of microhabitats, predator community, parasitoid pressure and behavioural differences between sexes.

A dynamic scale-mixture model of motion in natural scenes

Some of the most important tasks of visual and motor systems involve estimating the motion of objects and tracking them over time. Such systems evolved to meet the behavioral needs of the organism in its natural environment, and may therefore be adapted to the statistics of motion it is likely to encounter. By tracking the movement of individual points in movies of natural scenes, we begin to identify common properties of natural motion across scenes. As expected, objects in natural scenes move in a persistent fashion, with velocity correlations lasting hundreds of milliseconds. More subtly, but crucially, we find that the observed velocity distributions are heavy-tailed and can be modeled as a Gaussian scale-mixture. Extending this model to the time domain leads to a dynamic scale-mixture model, consisting of a Gaussian process multiplied by a positive scalar quantity with its own independent dynamics. Dynamic scaling of velocity arises naturally as a consequence of changes in object distance from the observer, and may approximate the effects of changes in other parameters governing the motion in a given scene. This modeling and estimation framework has implications for the neurobiology of sensory and motor systems, which need to cope with these fluctuations in scale in order to represent motion efficiently and drive fast and accurate tracking behavior.

Investigating the Mechanical Performance of Bionic Wings Based on the Flapping Kinematics of Beetle Hindwings

The beetle, of the order Coleoptera, possesses outstanding flight capabilities. After completing flight, they can fold their hindwings under the elytra and swiftly unfold them again when they take off. This sophisticated hindwing structure is a result of biological evolution, showcasing the strong environmental adaptability of this species. The beetle's hindwings can provide biomimetic inspiration for the design of flapping-wing micro air vehicles (FWMAVs). In this study, the Asian ladybird (Harmonia axyridis Pallas) was chosen as the bionic research object. Various kinematic parameters of its flapping flight were analyzed, including the flight characteristics of the hindwings, wing tip motion trajectories, and aerodynamic characteristics. Based on these results, a flapping kinematic model of the Asian ladybird was established. Then, three bionic deployable wing models were designed and their structural mechanical properties were analyzed. The results show that the structure of wing vein bars determined the mechanical properties of the bionic wing. This study can provide a theoretical basis and technical reference for further bionic wing design.

Descending networks transform command signals into population motor control

To convert intentions into actions, movement instructions must pass from the brain to downstream motor circuits through descending neurons (DNs). These include small sets of command-like neurons that are sufficient to drive behaviours1-the circuit mechanisms for which remain unclear. Here we show that command-like DNs in Drosophila directly recruit networks of additional DNs to orchestrate behaviours that require the active control of numerous body parts. Specifically, we found that command-like DNs previously thought to drive behaviours alone2-4 in fact co-activate larger populations of DNs. Connectome analyses and experimental manipulations revealed that this functional recruitment can be explained by direct excitatory connections between command-like DNs and networks of interconnected DNs in the brain. Descending population recruitment is necessary for behavioural control: DNs with many downstream descending partners require network co-activation to drive complete behaviours and drive only simple stereotyped movements in their absence. These DN networks reside within behaviour-specific clusters that inhibit one another. These results support a mechanism for command-like descending control in which behaviours are generated through the recruitment of increasingly large DN networks that compose behaviours by combining multiple motor subroutines.

Fully neuromorphic vision and control for autonomous drone flight

Biological sensing and processing is asynchronous and sparse, leading to low-latency and energy-efficient perception and action. In robotics, neuromorphic hardware for event-based vision and spiking neural networks promises to exhibit similar characteristics. However, robotic implementations have been limited to basic tasks with low-dimensional sensory inputs and motor actions because of the restricted network size in current embedded neuromorphic processors and the difficulties of training spiking neural networks. Here, we present a fully neuromorphic vision-to-control pipeline for controlling a flying drone. Specifically, we trained a spiking neural network that accepts raw event-based camera data and outputs low-level control actions for performing autonomous vision-based flight. The vision part of the network, consisting of five layers and 28,800 neurons, maps incoming raw events to ego-motion estimates and was trained with self-supervised learning on real event data. The control part consists of a single decoding layer and was learned with an evolutionary algorithm in a drone simulator. Robotic experiments show a successful sim-to-real transfer of the fully learned neuromorphic pipeline. The drone could accurately control its ego-motion, allowing for hovering, landing, and maneuvering sideways-even while yawing at the same time. The neuromorphic pipeline runs on board on Intel's Loihi neuromorphic processor with an execution frequency of 200 hertz, consuming 0.94 watt of idle power and a mere additional 7 to 12 milliwatts when running the network. These results illustrate the potential of neuromorphic sensing and processing for enabling insect-sized intelligent robots.

Multisensory integration in Anopheles mosquito swarms: The role of visual and acoustic information in mate tracking and collision avoidance

Male mosquitoes form aerial aggregations, known as swarms, to attract females and maximize their chances of finding a mate. Within these swarms, individuals must be able to recognize potential mates and navigate the dynamic social environment to successfully intercept a mating partner. Prior research has almost exclusively focused on the role of acoustic cues in mediating the male mosquito's ability to recognize and pursue flying females. However, the role of other sensory modalities in this behavior has not been explored. Moreover, how males avoid collisions with one another in the dense swarm while pursuing females remains poorly understood. In this study, we combined free-flight and tethered flight simulator experiments to demonstrate that swarming Anopheles coluzzii mosquitoes integrate visual and acoustic information to track conspecifics and avoid collisions. Our tethered experiments revealed that acoustic stimuli gated mosquito steering responses to visual objects simulating nearby mosquitoes, especially in males that exhibited attraction to visual objects in the presence of female flight tones. Additionally, we observed that visual cues alone could trigger changes in mosquitoes' wingbeat amplitude and frequency. These findings were corroborated by our free-flight experiments, which revealed that mosquitoes modulate their flight responses to nearby conspecifics in a similar manner to tethered animals, allowing for collision avoidance within swarms. Together, these results demonstrate that both males and females integrate multiple sensory inputs to mediate swarming behavior, and for males, the change in flight kinematics in response to multimodal cues allows them to simultaneously track females while avoiding collisions.

Synaptic architecture of leg and wing premotor control networks in Drosophila

Animal movement is controlled by motor neurons (MNs), which project out of the central nervous system to activate muscles. MN activity is coordinated by complex premotor networks that allow individual muscles to contribute to many different behaviors. Here, we use connectomics to analyze the wiring logic of premotor circuits controlling the Drosophila leg and wing. We find that both premotor networks cluster into modules that link MNs innervating muscles with related functions. Within most leg motor modules, the synaptic weights of each premotor neuron are proportional to the size of their target MNs, establishing a circuit basis for hierarchical MN recruitment. In contrast, wing premotor networks lack proportional synaptic connectivity, which may allow wing steering muscles to be recruited with different relative timing. By comparing the architecture of distinct limb motor control systems within the same animal, we identify common principles of premotor network organization and specializations that reflect the unique biomechanical constraints and evolutionary origins of leg and wing motor control.

Machine learning reveals the control mechanics of an insect wing hinge

Insects constitute the most species-rich radiation of metazoa, a success that is due to the evolution of active flight. Unlike pterosaurs, birds and bats, the wings of insects did not evolve from legs1, but are novel structures that are attached to the body via a biomechanically complex hinge that transforms tiny, high-frequency oscillations of specialized power muscles into the sweeping back-and-forth motion of the wings2. The hinge consists of a system of tiny, hardened structures called sclerites that are interconnected to one another via flexible joints and regulated by the activity of specialized control muscles. Here we imaged the activity of these muscles in a fly using a genetically encoded calcium indicator, while simultaneously tracking the three-dimensional motion of the wings with high-speed cameras. Using machine learning, we created a convolutional neural network3 that accurately predicts wing motion from the activity of the steering muscles, and an encoder-decoder4 that predicts the role of the individual sclerites on wing motion. By replaying patterns of wing motion on a dynamically scaled robotic fly, we quantified the effects of steering muscle activity on aerodynamic forces. A physics-based simulation incorporating our hinge model generates flight manoeuvres that are remarkably similar to those of free-flying flies. This integrative, multi-disciplinary approach reveals the mechanical control logic of the insect wing hinge, arguably among the most sophisticated and evolutionarily important skeletal structures in the natural world.

Mosquitoes escape looming threats by actively flying with the bow wave induced by the attacker

To detect and escape looming threats, night-flying insects must rely on other senses than vision alone. Nocturnal mosquitoes can evade looming objects in the dark, but how they achieve this is still unknown. Here, we show how night-active female malaria mosquitoes escape from rapidly looming objects that simulate defensive actions of blood-hosts. First, we quantified the escape performance of flying mosquitoes from an event-triggered mechanical swatter, showing that mosquitoes use swatter-induced airflow to increase their escape success. Secondly, we used high-speed videography and deep-learning-based tracking to analyze escape flights in detail, showing that mosquitoes use banked turns to evade the threat. By combining escape kinematics data with numerical simulations of attacker-induced airflow and a mechanistic movement model, we unraveled how mosquitoes control these banked evasive maneuvers: they actively steer away from the danger, and then passively travel with the bow wave produced by the attacker. Our results demonstrate that night-flying mosquitoes can detect looming objects when visual cues are minimal, suggesting that they use attacker-induced airflow both to detect the danger and as a fluid medium to move with away from the threat. This shows that escape strategies of flying insects are more complex than previous visually induced escape flight studies suggest. As most insects are of similar or smaller sizes than mosquitoes, comparable escape strategies are expected among millions of flying insect species. The here-observed escape maneuvers are distinct from those of mosquitoes escaping from odor-baited traps, thus providing new insights for the development of novel trapping techniques for integrative vector management.

Fantastic beasts and how to study them: rethinking experimental animal behavior

Humans have been trying to understand animal behavior at least since recorded history. Recent rapid development of new technologies has allowed us to make significant progress in understanding the physiological and molecular mechanisms underlying behavior, a key goal of neuroethology. However, there is a tradeoff when studying animal behavior and its underlying biological mechanisms: common behavior protocols in the laboratory are designed to be replicable and controlled, but they often fail to encompass the variability and breadth of natural behavior. This Commentary proposes a framework of 10 key questions that aim to guide researchers in incorporating a rich natural context into their experimental design or in choosing a new animal study system. The 10 questions cover overarching experimental considerations that can provide a template for interspecies comparisons, enable us to develop studies in new model organisms and unlock new experiments in our quest to understand behavior.

Descending control and regulation of spontaneous flight turns in Drosophila

The clumped distribution of resources in the world has influenced the pattern of foraging behavior since the origins of locomotion, selecting for a common search motif in which straight movements through resource-poor regions alternate with zig-zag exploration in resource-rich domains. For example, during local search, flying flies spontaneously execute rapid flight turns, called body saccades, but suppress these maneuvers during long-distance dispersal or when surging upstream toward an attractive odor. Here, we describe the key cellular components of a neural network in flies that generate spontaneous turns as well as a specialized pair of neurons that inhibits the network and suppresses turning. Using 2-photon imaging, optogenetic activation, and genetic ablation, we show that only four descending neurons appear sufficient to generate the descending commands to execute flight saccades. The network is organized into two functional units-one for right turns and one for left-with each unit consisting of an excitatory (DNae014) and an inhibitory (DNb01) neuron that project to the flight motor neuropil within the ventral nerve cord. Using resources from recently published connectomes of the fly, we identified a pair of large, distinct interneurons (VES041) that form inhibitory connections to all four saccade command neurons and created specific genetic driver lines for this cell. As predicted by its connectivity, activation of VES041 strongly suppresses saccades, suggesting that it promotes straight flight to regulate the transition between local search and long-distance dispersal. These results thus identify the key elements of a network that may play a crucial role in foraging ecology.

Machine learning reveals the control mechanics of an insect wing hinge

Insects constitute the most species-rich radiation of metazoa, a success due to the evolution of active flight. Unlike pterosaurs, birds, and bats, the wings of insects did not evolve from legs 1 , but are novel structures attached to the body via a biomechanically complex hinge that transforms tiny, high-frequency oscillations of specialized power muscles into the sweeping back-and-forth motion of the wings 2 . The hinge consists of a system of tiny, hardened structures called sclerites that are interconnected to one another via flexible joints and regulated by the activity of specialized control muscles. Here, we imaged the activity of these muscles in a fly using a genetically encoded calcium indicator, while simultaneously tracking the 3D motion of the wings with high-speed cameras. Using machine learning approaches, we created a convolutional neural network 3 that accurately predicts wing motion from the activity of the steering muscles, and an encoder-decoder 4 that predicts the role of the individual sclerites on wing motion. By replaying patterns of wing motion on a dynamically scaled robotic fly, we quantified the effects of steering muscle activity on aerodynamic forces. A physics-based simulation that incorporates our model of the hinge generates flight maneuvers that are remarkably similar to those of free flying flies. This integrative, multi-disciplinary approach reveals the mechanical control logic of the insect wing hinge, arguably among the most sophisticated and evolutionarily important skeletal structures in the natural world.

A hull reconstruction-reprojection method for pose estimation of free-flying fruit flies

Understanding the mechanisms of insect flight requires high-quality data of free-flight kinematics, e.g. for comparative studies or genetic screens. Although recent improvements in high-speed videography allow us to acquire large amounts of free-flight data, a significant bottleneck is automatically extracting accurate body and wing kinematics. Here, we present an experimental system and a hull reconstruction-reprojection algorithm for measuring the flight kinematics of fruit flies. The experimental system can automatically record hundreds of flight events per day. Our algorithm resolves a significant portion of the occlusions in this system by a reconstruction-reprojection scheme that integrates information from all cameras. Wing and body kinematics, including wing deformation, are then extracted from the hulls of the wing boundaries and body. This model-free method is fully automatic, accurate and open source, and can be readily adjusted for different camera configurations or insect species.

The self-oscillation paradox in the flight motor of Drosophila melanogaster

Tiny flying insects, such as Drosophila melanogaster, fly by flapping their wings at frequencies faster than their brains are able to process. To do so, they rely on self-oscillation: dynamic instability, leading to emergent oscillation, arising from muscle stretch-activation. Many questions concerning this vital natural instability remain open. Does flight motor self-oscillation necessarily lead to resonance-a state optimal in efficiency and/or performance? If so, what state? And is self-oscillation even guaranteed in a motor driven by stretch-activated muscle, or are there limiting conditions? In this work, we use data-driven models of wingbeat and muscle behaviour to answer these questions. Developing and leveraging novel analysis techniques, including symbolic computation, we establish a fundamental condition for motor self-oscillation common to a wide range of motor models. Remarkably, D. melanogaster flight apparently defies this condition: a paradox of motor operation. We explore potential resolutions to this paradox, and, within its confines, establish that the D. melanogaster flight motor is probably not resonant with respect to exoskeletal elasticity: instead, the muscular elasticity plays a dominant role. Contrary to common supposition, the stiffness of stretch-activated muscle is an obstacle to, rather than an enabler of, the operation of the D. melanogaster flight motor.

Direct comparison reveals algorithmic similarities in fly and mouse visual motion detection

Evolution has equipped vertebrates and invertebrates with neural circuits that selectively encode visual motion. While similarities in the computations performed by these circuits in mouse and fruit fly have been noted, direct experimental comparisons have been lacking. Because molecular mechanisms and neuronal morphology in the two species are distinct, we directly compared motion encoding in these two species at the algorithmic level, using matched stimuli and focusing on a pair of analogous neurons, the mouse ON starburst amacrine cell (ON SAC) and Drosophila T4 neurons. We find that the cells share similar spatiotemporal receptive field structures, sensitivity to spatiotemporal correlations, and tuning to sinusoidal drifting gratings, but differ in their responses to apparent motion stimuli. Both neuron types showed a response to summed sinusoids that deviates from models for motion processing in these cells, underscoring the similarities in their processing and identifying response features that remain to be explained.

Descending control and regulation of spontaneous flight turns in Drosophila

The clumped distribution of resources in the world has influenced the pattern of foraging behavior since the origins of life, selecting for a common locomotor search motif in which straight movements through resource-poor regions alternate with zig -zag exploration in resource-rich domains. For example, flies execute rapid changes in flight heading called body saccades during local search, but suppress these turns during long-distance dispersal or when surging upwind after encountering an attractive odor plume. Here, we describe the key cellular components of a neural network in flies that generates spontaneous turns as well as a specialized neuron that inhibits the network to promote straight flight. Using 2-photon imaging, optogenetic activation, and genetic ablation, we show that only four descending neurons appear sufficient to generate the descending commands to execute flight saccades. The network is organized into two functional couplets-one for right turns and one for left-with each couplet consisting of an excitatory (DNae014) and inhibitory (DNb01) neuron that project to the flight motor neuropil within the ventral nerve cord. Using resources from recently published connectomes of the fly brain, we identified a large, unique interneuron (VES041) that forms inhibitory connections to all four saccade command neurons and created specific genetic driver lines for this cell. As suggested by its connectivity, activation of VES041 strongly suppresses saccades, suggesting that it regulates the transition between local search and long-distance dispersal. These results thus identify the critical elements of a network that not only structures the locomotor behavior of flies, but may also play a crucial role in their natural foraging ecology.

Postural analysis reveals persistent changes in paper wasp foundress behavioral state after conspecific challenge

Vigilant animals detect and respond to threats in the environment, often changing posture and movement patterns. Vigilance is modulated not only by predators but also by conspecific threats. In social animals, precisely how conspecific threats alter vigilance behavior over time is relevant to long-standing hypotheses about social plasticity. We report persistent effects of a simulated conspecific challenge on behavior of wild northern paper wasp foundresses, Polistes fuscatus. During the founding phase of the colony cycle, conspecific wasps can usurp nests from the resident foundress, representing a severe threat. We used automated tracking to monitor the movement and posture of P. fuscatus foundresses in response to simulated intrusions. Wasps displayed increased movement, greater bilateral wing extension, and reduced antennal separation after the threat was removed. These changes were not observed after presentation with a wooden dowel. By rapidly adjusting individual behavior after fending off an intruder, paper wasp foundresses might invest in surveillance of potential threats, even when such threats are no longer immediately present. The prolonged vigilance-like behavioral state observed here is relevant to plasticity of social recognition processes in paper wasps.

Visual guidance of honeybees approaching a vertical landing surface

Landing is a critical phase for flying animals, whereby many rely on visual cues to perform controlled touchdown. Foraging honeybees rely on regular landings on flowers to collect food crucial for colony survival and reproduction. Here, we explored how honeybees utilize optical expansion cues to regulate approach flight speed when landing on vertical surfaces. Three sensory-motor control models have been proposed for landings of natural flyers. Landing honeybees maintain a constant optical expansion rate set-point, resulting in a gradual decrease in approach velocity and gentile touchdown. Bumblebees exhibit a similar strategy, but they regularly switch to a new constant optical expansion rate set-point. In contrast, landing birds fly at a constant time to contact to achieve faster landings. Here, we re-examined the landing strategy of honeybees by fitting the three models to individual approach flights of honeybees landing on platforms with varying optical expansion cues. Surprisingly, the landing model identified in bumblebees proved to be the most suitable for these honeybees. This reveals that honeybees adjust their optical expansion rate in a stepwise manner. Bees flying at low optical expansion rates tend to increase their set-point stepwise, while those flying at high optical expansion rates tend to decrease it stepwise. This modular landing control system enables honeybees to land rapidly and reliably under a wide range of initial flight conditions and visual landing platform patterns. The remarkable similarity between the landing strategies of honeybees and bumblebees suggests that this may also be prevalent among other flying insects. Furthermore, these findings hold promising potential for bioinspired guidance systems in flying robots.

An insect-inspired model facilitating autonomous navigation by incorporating goal approaching and collision avoidance

Being one of the most fundamental and crucial capacity of robots and animals, autonomous navigation that consists of goal approaching and collision avoidance enables completion of various tasks while traversing different environments. In light of the impressive navigational abilities of insects despite their tiny brains compared to mammals, the idea of seeking solutions from insects for the two key problems of navigation, i.e., goal approaching and collision avoidance, has fascinated researchers and engineers for many years. However, previous bio-inspired studies have focused on merely one of these two problems at one time. Insect-inspired navigation algorithms that synthetically incorporate both goal approaching and collision avoidance, and studies that investigate the interactions of these two mechanisms in the context of sensory-motor closed-loop autonomous navigation are lacking. To fill this gap, we propose an insect-inspired autonomous navigation algorithm to integrate the goal approaching mechanism as the global working memory inspired by the sweat bee's path integration (PI) mechanism, and the collision avoidance model as the local immediate cue built upon the locust's lobula giant movement detector (LGMD) model. The presented algorithm is utilized to drive agents to complete navigation task in a sensory-motor closed-loop manner within a bounded static or dynamic environment. Simulation results demonstrate that the synthetic algorithm is capable of guiding the agent to complete challenging navigation tasks in a robust and efficient way. This study takes the first tentative step to integrate the insect-like navigation mechanisms with different functionalities (i.e., global goal and local interrupt) into a coordinated control system that future research avenues could build upon.

Visual processing in the fly, from photoreceptors to behavior

Originally a genetic model organism, the experimental use of Drosophila melanogaster has grown to include quantitative behavioral analyses, sophisticated perturbations of neuronal function, and detailed sensory physiology. A highlight of these developments can be seen in the context of vision, where pioneering studies have uncovered fundamental and generalizable principles of sensory processing. Here we begin with an overview of vision-guided behaviors and common methods for probing visual circuits. We then outline the anatomy and physiology of brain regions involved in visual processing, beginning at the sensory periphery and ending with descending motor control. Areas of focus include contrast and motion detection in the optic lobe, circuits for visual feature selectivity, computations in support of spatial navigation, and contextual associative learning. Finally, we look to the future of fly visual neuroscience and discuss promising topics for further study.

Visual antipredator effects of web flexing in an orb web spider, with special reference to web decorations

Some visual antipredator strategies involve the rapid movement of highly contrasting body patterns to frighten or confuse the predator. Bright body colouration, however, can also be detected by potential predators and used as a cue. Among spiders, Argiope spp. are usually brightly coloured but they are not a common item in the diet of araneophagic wasps. When disturbed, Argiope executes a web-flexing behaviour in which they move rapidly and may be perceived as if they move backwards and towards an observer in front of the web. We studied the mechanisms underlying web-flexing behaviour as a defensive strategy. Using multispectral images and high-speed videos with deep-learning-based tracking techniques, we evaluated body colouration, body pattern, and spider kinematics from the perspective of a potential wasp predator. We show that the spider's abdomen is conspicuous, with a disruptive colouration pattern. We found that the body outline of spiders with web decorations was harder to detect when compared to spiders without decorations. The abdomen was also the body part that moved fastest, and its motion was composed mainly of translational (vertical) vectors in the potential predator's optical flow. In addition, with high contrast colouration, the spider's movement might be perceived as a sudden change in body size (looming effect) as perceived by the predator. These effects alongside the other visual cues may confuse potential wasp predators by breaking the spider body outline and affecting the wasp's flight manoeuvre, thereby deterring the wasp from executing the final attack.

A deep learning analysis of Drosophila body kinematics during magnetically tethered flight

Flying Drosophila rely on their vision to detect visual objects and adjust their flight course. Despite their robust fixation on a dark, vertical bar, our understanding of the underlying visuomotor neural circuits remains limited, in part due to difficulties in analyzing detailed body kinematics in a sensitive behavioral assay. In this study, we observed the body kinematics of flying Drosophila using a magnetically tethered flight assay, in which flies are free to rotate around their yaw axis, enabling naturalistic visual and proprioceptive feedback. Additionally, we used deep learning-based video analyses to characterize the kinematics of multiple body parts in flying animals. By applying this pipeline of behavioral experiments and analyses, we characterized the detailed body kinematics during rapid flight turns (or saccades) in two different visual conditions: spontaneous flight saccades under static screen and bar-fixating saccades while tracking a rotating bar. We found that both types of saccades involved movements of multiple body parts and that the overall dynamics were comparable. Our study highlights the importance of sensitive behavioral assays and analysis tools for characterizing complex visual behaviors.

An Aerial-Wall Robotic Insect That Can Land, Climb, and Take Off from Vertical Surfaces

Insects that can perform flapping-wing flight, climb on a wall, and switch smoothly between the 2 locomotion regimes provide us with excellent biomimetic models. However, very few biomimetic robots can perform complex locomotion tasks that combine the 2 abilities of climbing and flying. Here, we describe an aerial-wall amphibious robot that is self-contained for flying and climbing, and that can seamlessly move between the air and wall. It adopts a flapping/rotor hybrid power layout, which realizes not only efficient and controllable flight in the air but also attachment to, and climbing on, the vertical wall through a synergistic combination of the aerodynamic negative pressure adsorption of the rotor power and a climbing mechanism with bionic adhesion performance. On the basis of the attachment mechanism of insect foot pads, the prepared biomimetic adhesive materials of the robot can be applied to various types of wall surfaces to achieve stable climbing. The longitudinal axis layout design of the rotor dynamics and control strategy realize a unique cross-domain movement during the flying-climbing transition, which has important implications in understanding the takeoff and landing of insects. Moreover, it enables the robot to cross the air-wall boundary in 0.4 s (landing), and cross the wall-air boundary in 0.7 s (taking off). The aerial-wall amphibious robot expands the working space of traditional flying and climbing robots, which can pave the way for future robots that can perform autonomous visual monitoring, human search and rescue, and tracking tasks in complex air-wall environments.

Wing kinematics measurement and aerodynamics of hovering droneflies with wing damage

In this study, we performed successive unilateral and bilateral wing shearing to simulate wing damage in droneflies (Eristalis tenax) and measured the wing kinematics using high-speed photography technology. Two different shearing types were considered in the artificial wing damage. The aerodynamic force and power consumption were obtained by numerical method. Our major findings are the following. Different shearing methods have little influence on the kinematics, forces and energy consumption of insects. Following wing damage, among the potential strategies to adjust the three Euler angles of the wing, adjusting stroke angle (φ) in isolation, or combing the adjustment of stroke angle (φ) with pitch angle (ψ), contributed most to the change in vertical force. The balance of horizontal thrust can be restored by the adjustment of deviation angle (θ) under the condition of unilateral wing damage. Considering zero elastic energy storage, the mass-specific power (P₁) increases significantly following wing damage. However, the increase in mass-specific power with 100% elastic energy storage (P₂) is very small. The extra cost of the unilateral wing damage is that the energy consumption of the damaged wing and intact wing is highly asymmetrical when zero elastic energy storage is considered. The insects may alleviate the problems of increasing power consumption and asymmetric power distribution by storage and reuse of the negative inertial work of the wing.

Using Videography to Study the Biomechanics and Behavior of Freely Moving Mosquitoes

Female mosquitoes of most species require a blood meal for egg development. When biting a human host to collect this blood meal, they can spread dangerous diseases such as malaria, yellow fever, or dengue. Researchers use videography to study many aspects of mosquito behavior, including in-flight host-seeking, takeoff, and landing behaviors, as well as probing and blood feeding, and more. Here, we introduce protocols on how to use videography to capture and analyze mosquito movements at high spatial and temporal resolution, in two and three dimensions.

A visuomotor circuit for evasive flight turns in Drosophila

Visual systems extract multiple features from a scene using parallel neural circuits. Ultimately, the separate neural signals must come together to coherently influence action. Here, we characterize a circuit in Drosophila that integrates multiple visual features related to imminent threats to drive evasive locomotor turns. We identified, using genetic perturbation methods, a pair of visual projection neurons (LPLC2) and descending neurons (DNp06) that underlie evasive flight turns in response to laterally moving or approaching visual objects. Using two-photon calcium imaging or whole-cell patch clamping, we show that these cells indeed respond to both translating and approaching visual patterns. Furthermore, by measuring visual responses of LPLC2 neurons after genetically silencing presynaptic motion-sensing neurons, we show that their visual properties emerge by integrating multiple visual features across two early visual structures: the lobula and the lobula plate. This study highlights a clear example of how distinct visual signals converge on a single class of visual neurons and then activate premotor neurons to drive action, revealing a concise visuomotor pathway for evasive flight maneuvers in Drosophila.

Optic flow based spatial vision in insects

The optic flow, i.e., the displacement of retinal images of objects in the environment induced by self-motion, is an important source of spatial information, especially for fast-flying insects. Spatial information over a wide range of distances, from the animal's immediate surroundings over several hundred metres to kilometres, is necessary for mediating behaviours, such as landing manoeuvres, collision avoidance in spatially complex environments, learning environmental object constellations and path integration in spatial navigation. To facilitate the processing of spatial information, the complexity of the optic flow is often reduced by active vision strategies. These result in translations and rotations being largely separated by a saccadic flight and gaze mode. Only the translational components of the optic flow contain spatial information. In the first step of optic flow processing, an array of local motion detectors provides a retinotopic spatial proximity map of the environment. This local motion information is then processed in parallel neural pathways in a task-specific manner and used to control the different components of spatial behaviour. A particular challenge here is that the distance information extracted from the optic flow does not represent the distances unambiguously, but these are scaled by the animal's speed of locomotion. Possible ways of coping with this ambiguity are discussed.

Synaptic gradients transform object location to action

To survive, animals must convert sensory information into appropriate behaviours^1,2. Vision is a common sense for locating ethologically relevant stimuli and guiding motor responses^3-5. How circuitry converts object location in retinal coordinates to movement direction in body coordinates remains largely unknown. Here we show through behaviour, physiology, anatomy and connectomics in Drosophila that visuomotor transformation occurs by conversion of topographic maps formed by the dendrites of feature-detecting visual projection neurons (VPNs)^6,7 into synaptic weight gradients of VPN outputs onto central brain neurons. We demonstrate how this gradient motif transforms the anteroposterior location of a visual looming stimulus into the fly's directional escape. Specifically, we discover that two neurons postsynaptic to a looming-responsive VPN type promote opposite takeoff directions. Opposite synaptic weight gradients onto these neurons from looming VPNs in different visual field regions convert localized looming threats into correctly oriented escapes. For a second looming-responsive VPN type, we demonstrate graded responses along the dorsoventral axis. We show that this synaptic gradient motif generalizes across all 20 primary VPN cell types and most often arises without VPN axon topography. Synaptic gradients may thus be a general mechanism for conveying spatial features of sensory information into directed motor outputs.

Kinematic and hydrodynamic analyses of turning manoeuvres in penguins: body banking and wing upstroke generate centripetal force

Penguins perform lift-based swimming by flapping their wings. Previous kinematic and hydrodynamic studies have revealed the basics of wing motion and force generation in penguins. Although these studies have focused on steady forward swimming, the mechanism of turning manoeuvres is not well understood. In this study, we examined the horizontal turning of penguins via 3D motion analysis and quasi-steady hydrodynamic analysis. Free swimming of gentoo penguins (Pygoscelis papua) at an aquarium was recorded, and body and wing kinematics were analysed. In addition, quasi-steady calculations of the forces generated by the wings were performed. Among the selected horizontal swimming manoeuvres, turning was distinguished from straight swimming by the body trajectory for each wingbeat. During the turns, the penguins maintained outward banking through a wingbeat cycle and utilized a ventral force during the upstroke as a centripetal force to turn. Within a single wingbeat during the turns, changes in the body heading and bearing also mainly occurred during the upstroke, while the subsequent downstroke accelerated the body forward. We also found contralateral differences in the wing motion, i.e. the inside wing of the turn became more elevated and pronated. Quasi-steady calculations of the wing force confirmed that the asymmetry of the wing motion contributes to the generation of the centripetal force during the upstroke and the forward force during the downstroke. The results of this study demonstrate that the hydrodynamic force of flapping wings, in conjunction with body banking, is actively involved in the mechanism of turning manoeuvres in penguins.

Neuromuscular embodiment of feedback control elements in Drosophila flight

While insects such as Drosophila are flying, aerodynamic instabilities require that they make millisecond time scale adjustments to their wing motion to stay aloft and on course. These stabilization reflexes can be modeled as a proportional-integral (PI) controller; however, it is unclear how such control might be instantiated in insects at the level of muscles and neurons. Here, we show that the b1 and b2 motor units-prominent components of the fly's steering muscle system-modulate specific elements of the PI controller: the angular displacement (integral) and angular velocity (proportional), respectively. Moreover, these effects are observed only during the stabilization of pitch. Our results provide evidence for an organizational principle in which each muscle contributes to a specific functional role in flight control, a finding that highlights the power of using top-down behavioral modeling to guide bottom-up cellular manipulation studies.

Visual threats reduce blood-feeding and trigger escape responses in Aedes aegypti mosquitoes

The diurnal mosquitoes Aedes aegypti are vectors of several arboviruses, including dengue, yellow fever, and Zika viruses. To find a host to feed on, they rely on the sophisticated integration of olfactory, visual, thermal, and gustatory cues emitted by the hosts. If detected by their target, this latter may display defensive behaviors that mosquitoes need to be able to detect and escape in order to survive. In humans, a typical response is a swat of the hand, which generates both mechanical and visual perturbations aimed at a mosquito. Here, we used programmable visual displays to generate expanding objects sharing characteristics with the visual component of an approaching hand and quantified the behavioral response of female mosquitoes. Results show that Ae. aegypti is capable of using visual information to decide whether to feed on an artificial host mimic. Stimulations delivered in a LED flight arena further reveal that landed Ae. aegypti females display a stereotypical escape strategy by taking off at an angle that is a function of the direction of stimulus introduction. Altogether, this study demonstrates that mosquitoes landed on a host mimic can use isolated visual cues to detect and avoid a potential threat.

Numerical Simulations of the Effect of the Asymmetrical Bending of the Hindwings of a Hovering C. buqueti Bamboo Weevil with Respect to the Aerodynamic Characteristics

The airfoil structure and folding pattern of the hindwings of a beetle provide new transformation paths for improvements in the aerodynamic performance and structural optimization of flapping-wing flying robots. However, the explanation for the aerodynamic mechanism of the asymmetrical bending of a real beetle's hindwings under aerodynamic loads originating from the ventral and dorsal sides is unclear. To address this gap in our understanding, a computational investigation into the aerodynamic characteristics of the flight ability of C. buqueti and the large folding ratio of their hindwings when hovering is carried out in this article. A three-dimensional (3D) pressure-based SST k-ω turbulence model with a biomimetic structure was used for the detailed analysis, and a refined polyhedral mesh was used for the simulations. The results show that the fluid around the hindwings forms a vortex ring consisting of a leading-edge vortex (LEV), wing-tip vortex (TV) and trailing-edge vortex (TEV). Approximately 61% of the total lift is generated during the downstroke, which may be closely related to the asymmetric bending of the hindwings when they are subjected to pressure load.

Model-Based Tracking of Fruit Flies in Free Flight

Insect flight is a complex interdisciplinary phenomenon. Understanding its multiple aspects, such as flight control, sensory integration, physiology and genetics, often requires the analysis of large amounts of free flight kinematic data. Yet, one of the main bottlenecks in this field is automatically and accurately extracting such data from multi-view videos. Here, we present a model-based method for the pose estimation of free-flying fruit flies from multi-view high-speed videos. To obtain a faithful representation of the fly with minimum free parameters, our method uses a 3D model that includes two new aspects of wing deformation: A non-fixed wing hinge and a twisting wing surface. The method is demonstrated for free and perturbed flight. Our method does not use prior assumptions on the kinematics apart from the continuity of the wing pitch angle. Hence, this method can be readily adjusted for other insect species.

Accommodating unobservability to control flight attitude with optic flow

Attitude control is an essential flight capability. Whereas flying robots commonly rely on accelerometers¹ for estimating attitude, flying insects lack an unambiguous sense of gravity^2,3. Despite the established role of several sense organs in attitude stabilization^3-5, the dependence of flying insects on an internal gravity direction estimate remains unclear. Here we show how attitude can be extracted from optic flow when combined with a motion model that relates attitude to acceleration direction. Although there are conditions such as hover in which the attitude is unobservable, we prove that the ensuing control system is still stable, continuously moving into and out of these conditions. Flying robot experiments confirm that accommodating unobservability in this manner leads to stable, but slightly oscillatory, attitude control. Moreover, experiments with a bio-inspired flapping-wing robot show that residual, high-frequency attitude oscillations from flapping motion improve observability. The presented approach holds a promise for robotics, with accelerometer-less autopilots paving the road for insect-scale autonomous flying robots⁶. Finally, it forms a hypothesis on insect attitude estimation and control, with the potential to provide further insight into known biological phenomena^5,7,8 and to generate new predictions such as reduced head and body attitude variance at higher flight speeds⁹.

The Understanding of ON-Edge Motion Detection Through the Simulation Based on the Connectome of ’s Optic Lobe

The optic lobe of the fly is one of the prominent model systems for the neural mechanism of the motion detection. How a fly who lives under various visual situations of the nature processes the information from at most a few thousands of ommatidia in their neural circuit for the detection of moving objects is not exactly clear though many computational models of the fly optic lobe as a moving objects detector were suggested. Here we attempted to elucidate the mechanisms of ON-edge motion detection by a simulation approach based on the TEM connectome of Drosophila. Our simulation model of the optic lobe with the NEURON simulator that covers the full scale of ommatidia, reproduced the characteristics of the receptor neurons, lamina monopolar neurons, and T4 cells in the lobula. The contribution of each neuron can be estimated by changing synaptic connection strengths in the simulation and measuring the response to the motion stimulus. Those show the paradelle pathway provide motion detection in the fly optic lobe has more robustness and is more sophisticated than a simple combination of HR and BL systems.