Wind Gates Search States in Free Flight.

For any organism tracking a chemical cue to its source, the motion of its surrounding fluid provides crucial information for success. For both swimming and flying animals engaged in olfaction driven search, turning into the direction of oncoming wind or water current is often a critical first step 1, 2 . However, in nature, wind and water currents may not always provide a reliable directional cue 3, 4, 5 . It is unclear how organisms adjust their search strategies accordingly due to the challenges of separately controlling flow and chemical encounters. Here, we use the genetic toolkit of Drosophila melanogaster , a model organism for olfaction 6 , to develop an optogenetic paradigm to deliver temporally precise "virtual" olfactory experiences in free-flying animals while independently manipulating the wind conditions. We show that in free flight, Drosophila melanogaster adopt distinct search routines that are gated by whether they are flying in laminar wind or in still air. We first confirm that in laminar wind flies turn upwind, and further, we show that they achieve this using a rapid turn. In still air, flies adopt remarkably stereotyped "sink and circle" search state characterized by ∼60°turns at 3-4 Hz, biased in a consistent direction. In both laminar wind and still air, immediately after odor onset, flies decelerate and often perform a rapid turn. Both maneuvers are consistent with predictions from recent control theoretic analyses for how insects may estimate properties of wind while in flight 7, 8 . We suggest that flies may use their deceleration and "anemometric" turn as active sensing maneuvers to rapidly gauge properties of their wind environment before initiating a proximal or upwind search routine.

Sensory neuron population expansion enhances odor tracking without sensitizing projection neurons.

The evolutionary expansion of sensory neuron populations detecting important environmental cues is widespread, but functionally enigmatic. We investigated this phenomenon through comparison of homologous neural pathways of Drosophila melanogaster and its close relative Drosophila sechellia , an extreme specialist for Morinda citrifolia noni fruit. D. sechellia has evolved species-specific expansions in select, noni-detecting olfactory sensory neuron (OSN) populations, through multigenic changes. Activation and inhibition of defined proportions of neurons demonstrate that OSN population increases contribute to stronger, more persistent, noni-odor tracking behavior. These sensory neuron expansions result in increased synaptic connections with their projection neuron (PN) partners, which are conserved in number between species. Surprisingly, having more OSNs does not lead to greater odor-evoked PN sensitivity or reliability. Rather, pathways with increased sensory pooling exhibit reduced PN adaptation, likely through weakened lateral inhibition. Our work reveals an unexpected functional impact of sensory neuron expansions to explain ecologically-relevant, species-specific behavior.

Near-surface wind variability over spatiotemporal scales relevant to plume tracking insects.

Odor plume tracking is important for many organisms, and flying insects have served as popular model systems for studying this behavior both in field and laboratory settings. The shape and statistics of the airborne odor plumes that insects follow are largely governed by the wind that advects them. Prior atmospheric studies have investigated aspects of microscale wind patterns with an emphasis on characterizing pollution dispersion, enhancing weather prediction models, and for assessing wind energy potential. Here, we aim to characterize microscale wind dynamics through the lens of short-term ecological functions by focusing on spatial and temporal scales most relevant to insects actively searching for odor sources. We collected and compared near-surface wind data across three distinct environments (sage steppe, forest, and urban) in Northern Nevada. Our findings show that near-surface wind direction variability decreases with increasing wind speeds and increases in environments with greater surface complexity. Across environments, there is a strong correlation between the variability in the wind speed (i.e., turbulence intensity) and wind direction (i.e., standard deviation in wind direction). In some environments, the standard deviation in the wind direction varied as much as 15°-75° on time scales of 1-10 min. We draw insight between our findings and previous plume tracking experiments to provide a general intuition for future field research and guidance for wind tunnel design. Our analysis suggests a hypothesis that there may be an ideal range of wind speeds and environment complexity in which insects will be most successful when tracking odor plumes over long distances.

Emergent behaviour and neural dynamics in artificial agents tracking odour plumes.

Tracking an odour plume to locate its source under variable wind and plume statistics is a complex task. Flying insects routinely accomplish such tracking, often over long distances, in pursuit of food or mates. Several aspects of this remarkable behaviour and its underlying neural circuitry have been studied experimentally. Here we take a complementary in silico approach to develop an integrated understanding of their behaviour and neural computations. Specifically, we train artificial recurrent neural network agents using deep reinforcement learning to locate the source of simulated odour plumes that mimic features of plumes in a turbulent flow. Interestingly, the agents' emergent behaviours resemble those of flying insects, and the recurrent neural networks learn to compute task-relevant variables with distinct dynamic structures in population activity. Our analyses put forward a testable behavioural hypothesis for tracking plumes in changing wind direction, and we provide key intuitions for memory requirements and neural dynamics in odour plume tracking.

Active anemosensing hypothesis: how flying insects could estimate ambient wind direction through sensory integration and active movement.

Estimating the direction of ambient fluid flow is a crucial step during chemical plume tracking for flying and swimming animals. How animals accomplish this remains an open area of investigation. Recent calcium imaging with tethered flying Drosophila has shown that flies encode the angular direction of multiple sensory modalities in their central complex: orientation, apparent wind (or airspeed) direction and direction of motion. Here, we describe a general framework for how these three sensory modalities can be integrated over time to provide a continuous estimate of ambient wind direction. After validating our framework using a flying drone, we use simulations to show that ambient wind direction can be most accurately estimated with trajectories characterized by frequent, large magnitude turns. Furthermore, sensory measurements and estimates of their derivatives must be integrated over a period of time that incorporates at least one of these turns. Finally, we discuss approaches that insects might use to simplify the required computations, and present a list of testable predictions. Together, our results suggest that ambient flow estimation may be an important driver underlying the zigzagging manoeuvres characteristic of plume tracking animals' trajectories.

Correlated decision making across multiple phases of olfactory-guided search in Drosophila improves search efficiency.

Nearly all motile organisms must search for food, often requiring multiple phases of exploration across heterogeneous environments. The fruit fly, Drosophila, has emerged as an effective model system for studying this behavior; however, little is known about the extent to which experiences at one point in their search might influence decisions in another. To investigate whether prior experiences impact flies' search behavior after landing, I tracked individually labelled fruit flies as they explored three odor-emitting but food-barren objects. I found two features of their behavior that are correlated with the distance they travel on foot. First, flies walked larger distances when they approached the odor source, which they were almost twice as likely to do when landing on the patch farthest downwind. Computational fluid dynamics simulations suggest this patch may have had a stronger baseline odor, but only ∼15% higher than the other two patches. This small increase, together with flies' high olfactory sensitivity, suggests that their flight trajectory used to approach the patches plays a role. Second, flies also walked larger distances when the time elapsed since their last visit was longer. However, the correlation is subtle and subject to a large degree of variability. Using agent-based models, I show that this small correlation can increase search efficiency by 25-50% across many scenarios. Furthermore, my models provide mechanistic hypotheses explaining the variability through either a noisy or stochastic decision-making process. Surprisingly, these stochastic decision-making algorithms enhance search efficiency in challenging but realistic search scenarios compared with deterministic strategies.

Insect inspired vision-based velocity estimation through spatial pooling of optic flow during linear motion.

Insects rely on the perception of image motion, or optic flow, to estimate their velocity relative to nearby objects. This information provides important sensory input for avoiding obstacles. However, certain behaviors, such as estimating the absolute distance to a landing target, accurately measuring absolute distance traveled, and estimating the ambient wind speed require decoupling optic flow into its component parts: absolute ground velocity and distance to nearby objects. Behavioral experiments suggest that insects perform these calculations, but their mechanism for doing so remains unknown. Here we present a novel algorithm that combines the geometry of dynamic forward motion with known features of insect visual processing to provide a hypothesis for how insects mightdirectlyestimate absolute ground velocity from a combination of optic flow and acceleration information. Our robotics-inspired-biology approach reveals three critical requirements. First, absolute ground velocity can only be directly estimated from optic flow during times of active acceleration and deceleration. Second, spatial pooling of optic flow across a receptive field helps to alleviate the effects of noise and/or low resolution visual systems. Third, averaging velocity estimates from multiple receptive fields further helps to reject noise. Our algorithm provides a hypothesis for how insects might estimate absolute velocity from vision during active maneuvers, and also provides a theoretical framework for designing fast analog circuitry for efficient state estimation that can be applied to insect-sized robots.

The long-distance flight behavior of Drosophila supports an agent-based model for wind-assisted dispersal in insects.

Despite the ecological importance of long-distance dispersal in insects, its mechanistic basis is poorly understood in genetic model species, in which advanced molecular tools are readily available. One critical question is how insects interact with the wind to detect attractive odor plumes and increase their travel distance as they disperse. To gain insight into dispersal, we conducted release-and-recapture experiments in the Mojave Desert using the fruit fly, Drosophila melanogaster We deployed chemically baited traps in a 1 km radius ring around the release site, equipped with cameras that captured the arrival times of flies as they landed. In each experiment, we released between 30,000 and 200,000 flies. By repeating the experiments under a variety of conditions, we were able to quantify the influence of wind on flies' dispersal behavior. Our results confirm that even tiny fruit flies could disperse ∼12 km in a single flight in still air and might travel many times that distance in a moderate wind. The dispersal behavior of the flies is well explained by an agent-based model in which animals maintain a fixed body orientation relative to celestial cues, actively regulate groundspeed along their body axis, and allow the wind to advect them sideways. The model accounts for the observation that flies actively fan out in all directions in still air but are increasingly advected downwind as winds intensify. Our results suggest that dispersing insects may strike a balance between the need to cover large distances while still maintaining the chance of intercepting odor plumes from upwind sources.

A Nonlinear Observability Analysis of Ambient Wind Estimation with Uncalibrated Sensors, Inspired by Insect Neural Encoding.

Estimating the direction of ambient fluid flow is key for many flying or swimming animals and robots, but can only be accomplished through indirect measurements and active control. Recent work with tethered flying insects indicates that their sensory representation of orientation, apparent wind, direction of movement, and control is represented by a 2-dimensional angular encoding in the central brain. This representation simplifies sensory integration by projecting the direction (but not scale) of measurements with different units onto a universal polar coordinate frame. To align these angular measurements with one another and the motor system does, however, require a calibration of angular gain and offset for each sensor. This calibration could change with time due to changes in the environment or physical structure. The circumstances under which small robots and animals with angular sensors and changing calibrations could self-calibrate and estimate the direction of ambient fluid flow while moving remains an open question. Here, a methodical nonlinear observability analysis is presented to address this. The analysis shows that it is mathematically feasible to continuously estimate flow direction and perform self-calibrations by adopting frequent changes in course (or active prevention thereof) and orientation, and requires fusion and temporal differentiation of three sensory measurements: apparent flow, orientation (or its derivative), and direction of motion (or its derivative). These conclusions are consistent with the zigzagging trajectories exhibited by many plume tracking organisms, suggesting that perhaps flow estimation is a secondary driver of their trajectory structure.

Numerical differentiation of noisy data: A unifying multi-objective optimization framework.

Computing derivatives of noisy measurement data is ubiquitous in the physical, engineering, and biological sciences, and it is often a critical step in developing dynamic models or designing control. Unfortunately, the mathematical formulation of numerical differentiation is typically ill-posed, and researchers often resort to an ad hoc process for choosing one of many computational methods and its parameters. In this work, we take a principled approach and propose a multi-objective optimization framework for choosing parameters that minimize a loss function to balance the faithfulness and smoothness of the derivative estimate. Our framework has three significant advantages. First, the task of selecting multiple parameters is reduced to choosing a single hyper-parameter. Second, where ground-truth data is unknown, we provide a heuristic for selecting this hyper-parameter based on the power spectrum and temporal resolution of the data. Third, the optimal value of the hyper-parameter is consistent across different differentiation methods, thus our approach unifies vastly different numerical differentiation methods and facilitates unbiased comparison of their results. Finally, we provide an extensive open-source Python library pynumdiff to facilitate easy application to diverse datasets (https://github.com/florisvb/PyNumDiff).

Visual-Olfactory Integration in the Human Disease Vector Mosquito Aedes aegypti.

Mosquitoes rely on the integration of multiple sensory cues, including olfactory, visual, and thermal stimuli, to detect, identify, and locate their hosts [1-4]. Although we increasingly know more about the role of chemosensory behaviors in mediating mosquito-host interactions [1], the role of visual cues is comparatively less studied [3], and how the combination of olfactory and visual information is integrated in the mosquito brain remains unknown. In the present study, we used a tethered-flight light-emitting diode (LED) arena, which allowed for quantitative control over the stimuli, and a control theoretic model to show that CO2 modulates mosquito steering responses toward vertical bars. To gain insight into the neural basis of this olfactory and visual coupling, we conducted two-photon microscopy experiments in a new GCaMP6s-expressing mosquito line. Imaging revealed that neuropil regions within the lobula exhibited strong responses to objects, such as a bar, but showed little response to a large-field motion. Approximately 20% of the lobula neuropil we imaged were modulated when CO2 preceded the presentation of a moving bar. By contrast, responses in the antennal (olfactory) lobe were not modulated by visual stimuli presented before or after an olfactory stimulus. Together, our results suggest that asymmetric coupling between these sensory systems provides enhanced steering responses to discrete objects.

Distinct activity-gated pathways mediate attraction and aversion to CO2 in Drosophila.

Carbon dioxide is produced by many organic processes and is a convenient volatile cue for insects1 that are searching for blood hosts2, flowers3, communal nests4, fruit5 and wildfires6. Although Drosophila melanogaster feed on yeast that produce CO2 and ethanol during fermentation, laboratory experiments7-12 suggest that walking flies avoid CO2. Here we resolve this paradox by showing that both flying and walking Drosophila find CO2 attractive, but only when they are in an active state associated with foraging. Their aversion to CO2 at low-activity levels may be an adaptation to avoid parasites that seek CO2, or to avoid succumbing to respiratory acidosis in the presence of high concentrations of CO2 that exist in nature13,14. In contrast to CO2, flies are attracted to ethanol in all behavioural states, and invest twice the time searching near ethanol compared to CO2. These behavioural differences reflect the fact that ethanol is a unique signature of yeast fermentation, whereas CO2 is generated by many natural processes. Using genetic tools, we determined that the evolutionarily conserved ionotropic co-receptor IR25a is required for CO2 attraction, and that the receptors necessary for CO2 avoidance are not involved in this attraction. Our study lays the foundation for future research to determine the neural circuits that underlie both state- and odorant-dependent decision-making in Drosophila.

History dependence in insect flight decisions during odor tracking.

Natural decision-making often involves extended decision sequences in response to variable stimuli with complex structure. As an example, many animals follow odor plumes to locate food sources or mates, but turbulence breaks up the advected odor signal into intermittent filaments and puffs. This scenario provides an opportunity to ask how animals use sparse, instantaneous, and stochastic signal encounters to generate goal-oriented behavioral sequences. Here we examined the trajectories of flying fruit flies (Drosophila melanogaster) and mosquitoes (Aedes aegypti) navigating in controlled plumes of attractive odorants. While it is known that mean odor-triggered flight responses are dominated by upwind turns, individual responses are highly variable. We asked whether deviations from mean responses depended on specific features of odor encounters, and found that odor-triggered turns were slightly but significantly modulated by two features of odor encounters. First, encounters with higher concentrations triggered stronger upwind turns. Second, encounters occurring later in a sequence triggered weaker upwind turns. To contextualize the latter history dependence theoretically, we examined trajectories simulated from three normative tracking strategies. We found that neither a purely reactive strategy nor a strategy in which the tracker learned the plume centerline over time captured the observed history dependence. In contrast, "infotaxis", in which flight decisions maximized expected information gain about source location, exhibited a history dependence aligned in sign with the data, though much larger in magnitude. These findings suggest that while true plume tracking is dominated by a reactive odor response it might also involve a history-dependent modulation of responses consistent with the accumulation of information about a source over multi-encounter timescales. This suggests that short-term memory processes modulating decision sequences may play a role in natural plume tracking.

Superhydrophobic diving flies (Ephydra hians) and the hypersaline waters of Mono Lake.

The remarkable alkali fly, Ephydra hians, deliberately crawls into the alkaline waters of Mono Lake to feed and lay eggs. These diving flies are protected by an air bubble that forms around their superhydrophobic cuticle upon entering the lake. To study the physical mechanisms underlying this process we measured the work required for flies to enter and leave various aqueous solutions. Our measurements show that it is more difficult for the flies to escape from Mono Lake water than from fresh water, due to the high concentration of Na2CO3 which causes water to penetrate and thus wet their setose cuticle. Other less kosmotropic salts do not have this effect, suggesting that the phenomenon is governed by Hofmeister effects as well as specific interactions between ion pairs. These effects likely create a small negative charge at the air-water interface, generating an electric double layer that facilitates wetting. Compared with six other species of flies, alkali flies are better able to resist wetting in a 0.5 M Na2CO3 solution. This trait arises from a combination of factors including a denser layer of setae on their cuticle and the prevalence of smaller cuticular hydrocarbons compared with other species. Although superbly adapted to resisting wetting, alkali flies are vulnerable to getting stuck in natural and artificial oils, including dimethicone, a common ingredient in sunscreen and other cosmetics. Mono Lake's alkali flies are a compelling example of how the evolution of picoscale physical and chemical changes can allow an animal to occupy an entirely new ecological niche.

Mosquitoes Use Vision to Associate Odor Plumes with Thermal Targets.

All moving animals, including flies, sharks, and humans, experience a dynamic sensory landscape that is a function of both their trajectory through space and the distribution of stimuli in the environment. This is particularly apparent for mosquitoes, which use a combination of olfactory, visual, and thermal cues to locate hosts. Mosquitoes are thought to detect suitable hosts by the presence of a sparse CO2 plume, which they track by surging upwind and casting crosswind. Upon approach, local cues such as heat and skin volatiles help them identify a landing site. Recent evidence suggests that thermal attraction is gated by the presence of CO2, although this conclusion was based experiments in which the actual flight trajectories of the animals were unknown and visual cues were not studied. Using a three-dimensional tracking system, we show that rather than gating heat sensing, the detection of CO2 actually activates a strong attraction to visual features. This visual reflex guides the mosquitoes to potential hosts where they are close enough to detect thermal cues. By experimentally decoupling the olfactory, visual, and thermal cues, we show that the motor reactions to these stimuli are independently controlled. Given that humans become visible to mosquitoes at a distance of 5-15 m, visual cues play a critical intermediate role in host localization by coupling long-range plume tracking to behaviors that require short-range cues. Rather than direct neural coupling, the separate sensory-motor reflexes are linked as a result of the interaction between the animal's reactions and the spatial structure of the stimuli in the environment.

Strategies for the stabilization of longitudinal forward flapping flight revealed using a dynamically-scaled robotic fly.

The ability to regulate forward speed is an essential requirement for flying animals. Here, we use a dynamically-scaled robot to study how flapping insects adjust their wing kinematics to regulate and stabilize forward flight. The results suggest that the steady-state lift and thrust requirements at different speeds may be accomplished with quite subtle changes in hovering kinematics, and that these adjustments act primarily by altering the pitch moment. This finding is consistent with prior hypotheses regarding the relationship between body pitch and flight speed in fruit flies. Adjusting the mean stroke position of the wings is a likely mechanism for trimming the pitch moment at all speeds, whereas changes in the mean angle of attack may be required at higher speeds. To ensure stability, the flapping system requires additional pitch damping that increases in magnitude with flight speed. A compensatory reflex driven by fast feedback of pitch rate from the halteres could provide such damping, and would automatically exhibit gain scheduling with flight speed if pitch torque was regulated via changes in stroke deviation. Such a control scheme would provide an elegant solution for stabilization across a wide range of forward flight speeds.

Monocular distance estimation from optic flow during active landing maneuvers.

Vision is arguably the most widely used sensor for position and velocity estimation in animals, and it is increasingly used in robotic systems as well. Many animals use stereopsis and object recognition in order to make a true estimate of distance. For a tiny insect such as a fruit fly or honeybee, however, these methods fall short. Instead, an insect must rely on calculations of optic flow, which can provide a measure of the ratio of velocity to distance, but not either parameter independently. Nevertheless, flies and other insects are adept at landing on a variety of substrates, a behavior that inherently requires some form of distance estimation in order to trigger distance-appropriate motor actions such as deceleration or leg extension. Previous studies have shown that these behaviors are indeed under visual control, raising the question: how does an insect estimate distance solely using optic flow? In this paper we use a nonlinear control theoretic approach to propose a solution for this problem. Our algorithm takes advantage of visually controlled landing trajectories that have been observed in flies and honeybees. Finally, we implement our algorithm, which we term dynamic peering, using a camera mounted to a linear stage to demonstrate its real-world feasibility.

Octopaminergic modulation of the visual flight speed regulator of Drosophila.

Recent evidence suggests that flies' sensitivity to large-field optic flow is increased by the release of octopamine during flight. This increase in gain presumably enhances visually mediated behaviors such as the active regulation of forward speed, a process that involves the comparison of a vision-based estimate of velocity with an internal set point. To determine where in the neural circuit this comparison is made, we selectively silenced the octopamine neurons in the fruit fly Drosophila, and examined the effect on vision-based velocity regulation in free-flying flies. We found that flies with inactivated octopamine neurons accelerated more slowly in response to visual motion than control flies, but maintained nearly the same baseline flight speed. Our results are parsimonious with a circuit architecture in which the internal control signal is injected into the visual motion pathway upstream of the interneuron network that estimates groundspeed.