Not seeing the forest for the trees: combination of path integration and landmark cues in human virtual navigation.

Introduction: In order to successfully move from place to place, our brain often combines sensory inputs from various sources by dynamically weighting spatial cues according to their reliability and relevance for a given task. Two of the most important cues in navigation are the spatial arrangement of landmarks in the environment, and the continuous path integration of travelled distances and changes in direction. Several studies have shown that Bayesian integration of cues provides a good explanation for navigation in environments dominated by small numbers of easily identifiable landmarks. However, it remains largely unclear how cues are combined in more complex environments. Methods: To investigate how humans process and combine landmarks and path integration in complex environments, we conducted a series of triangle completion experiments in virtual reality, in which we varied the number of landmarks from an open steppe to a dense forest, thus going beyond the spatially simple environments that have been studied in the past. We analysed spatial behaviour at both the population and individual level with linear regression models and developed a computational model, based on maximum likelihood estimation (MLE), to infer the underlying combination of cues. Results: Overall homing performance was optimal in an environment containing three landmarks arranged around the goal location. With more than three landmarks, individual differences between participants in the use of cues are striking. For some, the addition of landmarks does not worsen their performance, whereas for others it seems to impair their use of landmark information. Discussion: It appears that navigation success in complex environments depends on the ability to identify the correct clearing around the goal location, suggesting that some participants may not be able to see the forest for the trees.

Finding the gap: neuromorphic motion-vision in dense environments.

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

Model organisms and systems in neuroethology: one hundred years of history and a look into the future.

The Journal of Comparative Physiology lived up to its name in the last 100 years by including more than 1500 different taxa in almost 10,000 publications. Seventeen phyla of the animal kingdom were represented. The honeybee (Apis mellifera) is the taxon with most publications, followed by locust (Locusta migratoria), crayfishes (Cambarus spp.), and fruitfly (Drosophila melanogaster). The representation of species in this journal in the past, thus, differs much from the 13 model systems as named by the National Institutes of Health (USA). We mention major accomplishments of research on species with specific adaptations, specialist animals, for example, the quantitative description of the processes underlying the axon potential in squid (Loligo forbesii) and the isolation of the first receptor channel in the electric eel (Electrophorus electricus) and electric ray (Torpedo spp.). Future neuroethological work should make the recent genetic and technological developments available for specialist animals. There are many research questions left that may be answered with high yield in specialists and some questions that can only be answered in specialists. Moreover, the adaptations of animals that occupy specific ecological niches often lend themselves to biomimetic applications. We go into some depth in explaining our thoughts in the research of motion vision in insects, sound localization in barn owls, and electroreception in weakly electric fish.

The Virtual Navigation Toolbox: Providing tools for virtual navigation experiments.

Spatial navigation research in humans increasingly relies on experiments using virtual reality (VR) tools, which allow for the creation of highly flexible, and immersive study environments, that can react to participant interaction in real time. Despite the popularity of VR, tools simplifying the creation and data management of such experiments are rare and often restricted to a specific scope-limiting usability and comparability. To overcome those limitations, we introduce the Virtual Navigation Toolbox (VNT), a collection of interchangeable and independent tools for the development of spatial navigation VR experiments using the popular Unity game engine. The VNT's features are packaged in loosely coupled and reusable modules, facilitating convenient implementation of diverse experimental designs. Here, we depict how the VNT fulfils feature requirements of different VR environments and experiments, guiding through the implementation and execution of a showcase study using the toolbox. The presented showcase study reveals that homing performance in a classic triangle completion task is invariant to translation velocity of the participant's avatar, but highly sensitive to the number of landmarks. The VNT is freely available under a creative commons license, and we invite researchers to contribute, extending and improving tools using the provided repository.

Nest-associated scent marks help bumblebees localizing their nest in visually ambiguous situations.

Social insects such as ants and bees are excellent navigators. To manage their daily routines bumblebees, as an example, must learn multiple locations in their environment, like flower patches and their nest. While navigating from one location to another, they mainly rely on vision. Although the environment in which bumblebees live, be it a meadow or a garden, is visually stable overall, it may be prone to changes such as moving shadows or the displacement of an object in the scenery. Therefore, bees might not solely rely on visual cues, but use additional sources of information, forming a multimodal guidance system to ensure their return home to their nest. Here we show that the home-finding behavior of bumblebees, when confronted with a visually ambiguous scenario, is strongly influenced by natural scent marks they deposit at the inconspicuous nest hole when leaving their nest. Bumblebees search for a longer time and target their search with precision at potential nest locations that are visually familiar, if also marked with their natural scent. This finding sheds light on the crucial role of odor in helping bees find their way back to their inconspicuous nest.

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.

Random attention can explain apparent object choice behavior in free-walking blowflies.

Flies are often observed to approach dark objects. To a naive observer they seem to pay selective attention to one out of several objects although previous research identified as a possible underlying mechanism a reflex-like fixation behavior integrating responses to all objects. In a combination of behavioral experiments and computational modelling, we investigated the choice behavior of flies freely walking towards an arrangement of two objects placed at a variable distance from each other. The walking trajectories were oriented towards one of the objects much earlier than predicted by a simple reactive model. We show that object choice can be explained by a continuous control scheme in combination with a mechanism randomly responding to the position of each object according to a stochastic process. This may be viewed as a special form of an implicit attention-like mechanism, for which the model does not require an explicit decision mechanism or a memory for the drawn decision.

Bumblebees display characteristics of active vision during robust obstacle avoidance flight.

Insects are remarkable flyers and capable of navigating through highly cluttered environments. We tracked the head and thorax of bumblebees freely flying in a tunnel containing vertically oriented obstacles to uncover the sensorimotor strategies used for obstacle detection and collision avoidance. Bumblebees presented all the characteristics of active vision during flight by stabilizing their head relative to the external environment and maintained close alignment between their gaze and flightpath. Head stabilization increased motion contrast of nearby features against the background to enable obstacle detection. As bees approached obstacles, they appeared to modulate avoidance responses based on the relative retinal expansion velocity (RREV) of obstacles and their maximum evasion acceleration was linearly related to RREVmax. Finally, bees prevented collisions through rapid roll manoeuvres implemented by their thorax. Overall, the combination of visuo-motor strategies of bumblebees highlights elegant solutions developed by insects for visually guided flight through cluttered environments.

Disentangling of Local and Wide-Field Motion Adaptation.

Motion adaptation has been attributed in flying insects a pivotal functional role in spatial vision based on optic flow. Ongoing motion enhances in the visual pathway the representation of spatial discontinuities, which manifest themselves as velocity discontinuities in the retinal optic flow pattern during translational locomotion. There is evidence for different spatial scales of motion adaptation at the different visual processing stages. Motion adaptation is supposed to take place, on the one hand, on a retinotopic basis at the level of local motion detecting neurons and, on the other hand, at the level of wide-field neurons pooling the output of many of these local motion detectors. So far, local and wide-field adaptation could not be analyzed separately, since conventional motion stimuli jointly affect both adaptive processes. Therefore, we designed a novel stimulus paradigm based on two types of motion stimuli that had the same overall strength but differed in that one led to local motion adaptation while the other did not. We recorded intracellularly the activity of a particular wide-field motion-sensitive neuron, the horizontal system equatorial cell (HSE) in blowflies. The experimental data were interpreted based on a computational model of the visual motion pathway, which included the spatially pooling HSE-cell. By comparing the difference between the recorded and modeled HSE-cell responses induced by the two types of motion adaptation, the major characteristics of local and wide-field adaptation could be pinpointed. Wide-field adaptation could be shown to strongly depend on the activation level of the cell and, thus, on the direction of motion. In contrast, the response gain is reduced by local motion adaptation to a similar extent independent of the direction of motion. This direction-independent adaptation differs fundamentally from the well-known adaptive adjustment of response gain according to the prevailing overall stimulus level that is considered essential for an efficient signal representation by neurons with a limited operating range. Direction-independent adaptation is discussed to result from the joint activity of local motion-sensitive neurons of different preferred directions and to lead to a representation of the local motion direction that is independent of the overall direction of global motion.

Visual and movement memories steer foraging bumblebees along habitual routes.

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

Bumblebees perceive the spatial layout of their environment in relation to their body size and form to minimize inflight collisions.

Animals that move through complex habitats must frequently contend with obstacles in their path. Humans and other highly cognitive vertebrates avoid collisions by perceiving the relationship between the layout of their surroundings and the properties of their own body profile and action capacity. It is unknown whether insects, which have much smaller brains, possess such abilities. We used bumblebees, which vary widely in body size and regularly forage in dense vegetation, to investigate whether flying insects consider their own size when interacting with their surroundings. Bumblebees trained to fly in a tunnel were sporadically presented with an obstructing wall containing a gap that varied in width. Bees successfully flew through narrow gaps, even those that were much smaller than their wingspans, by first performing lateral scanning (side-to-side flights) to visually assess the aperture. Bees then reoriented their in-flight posture (i.e., yaw or heading angle) while passing through, minimizing their projected frontal width and mitigating collisions; in extreme cases, bees flew entirely sideways through the gap. Both the time that bees spent scanning during their approach and the extent to which they reoriented themselves to pass through the gap were determined not by the absolute size of the gap, but by the size of the gap relative to each bee's own wingspan. Our findings suggest that, similar to humans and other vertebrates, flying bumblebees perceive the affordance of their surroundings relative their body size and form to navigate safely through complex environments.

Visually guided homing of bumblebees in ambiguous situations: A behavioural and modelling study.

Returning home is a crucial task accomplished daily by many animals, including humans. Because of their tiny brains, insects, like bees or ants, are good study models for efficient navigation strategies. Bees and ants are known to rely mainly on learned visual information about the nest surroundings to pinpoint their barely visible nest-entrance. During the return, when the actual sight of the insect matches the learned information, the insect is easily guided home. Occasionally, modifications to the visual environment may take place while the insect is on a foraging trip. Here, we addressed the ecologically relevant question of how bumblebees' homing is affected by such a situation. In an artificial setting, we habituated bees to be guided to their nest by two constellations of visual cues. After habituation, these cues were displaced during foraging trips into a conflict situation. We recorded bumblebees' return flights in such circumstances and investigated where they search for their nest entrance following the degree of displacement between the two visually relevant cues. Bumblebees mostly searched at the fictive nest location as indicated by either cue constellation, but never at a compromise location between them. We compared these experimental results to the predictions of different types of homing models. We found that models guiding an agent by a single holistic view of the nest surroundings could not account for the bumblebees' search behaviour in cue-conflict situations. Instead, homing models relying on multiple views were sufficient. We could further show that homing models required fewer views and got more robust to height changes if optic flow-based spatial information was encoded and learned, rather than just brightness information.

Resource-efficient bio-inspired visual processing on the hexapod walking robot HECTOR.

Emulating the highly resource-efficient processing of visual motion information in the brain of flying insects, a bio-inspired controller for collision avoidance and navigation was implemented on a novel, integrated System-on-Chip-based hardware module. The hardware module is used to control visually-guided navigation behavior of the stick insect-like hexapod robot HECTOR. By leveraging highly parallelized bio-inspired algorithms to extract nearness information from visual motion in dynamically reconfigurable logic, HECTOR is able to navigate to predefined goal positions without colliding with obstacles. The system drastically outperforms CPU- and graphics card-based implementations in terms of speed and resource efficiency, making it suitable to be also placed on fast moving robots, such as flying drones.

The Critical Role of Head Movements for Spatial Representation During Bumblebees Learning Flight.

Bumblebees perform complex flight maneuvers around the barely visible entrance of their nest upon their first departures. During these flights bees learn visual information about the surroundings, possibly including its spatial layout. They rely on this information to return home. Depth information can be derived from the apparent motion of the scenery on the bees' retina. This motion is shaped by the animal's flight and orientation: Bees employ a saccadic flight and gaze strategy, where rapid turns of the head (saccades) alternate with flight segments of apparently constant gaze direction (intersaccades). When during intersaccades the gaze direction is kept relatively constant, the apparent motion contains information about the distance of the animal to environmental objects, and thus, in an egocentric reference frame. Alternatively, when the gaze direction rotates around a fixed point in space, the animal perceives the depth structure relative to this pivot point, i.e., in an allocentric reference frame. If the pivot point is at the nest-hole, the information is nest-centric. Here, we investigate in which reference frames bumblebees perceive depth information during their learning flights. By precisely tracking the head orientation, we found that half of the time, the head appears to pivot actively. However, only few of the corresponding pivot points are close to the nest entrance. Our results indicate that bumblebees perceive visual information in several reference frames when they learn about the surroundings of a behaviorally relevant location.

Image statistics of the environment surrounding freely behaving hoverflies.

Natural scenes are not as random as they might appear, but are constrained in both space and time. The 2-dimensional spatial constraints can be described by quantifying the image statistics of photographs. Human observers perceive images with naturalistic image statistics as more pleasant to view, and both fly and vertebrate peripheral and higher order visual neurons are tuned to naturalistic image statistics. However, for a given animal, what is natural differs depending on the behavior, and even if we have a broad understanding of image statistics, we know less about the scenes relevant for particular behaviors. To mitigate this, we here investigate the image statistics surrounding Episyrphus balteatus hoverflies, where the males hover in sun shafts created by surrounding trees, producing a rich and dense background texture and also intricate shadow patterns on the ground. We quantified the image statistics of photographs of the ground and the surrounding panorama, as the ventral and lateral visual field is particularly important for visual flight control, and found differences in spatial statistics in photos where the hoverflies were hovering compared to where they were flying. Our results can, in the future, be used to create more naturalistic stimuli for experimenter-controlled experiments in the laboratory.

Gap perception in bumblebees.

A number of insects fly over long distances below the natural canopy, where the physical environment is highly cluttered consisting of obstacles of varying shape, size and texture. While navigating within such environments, animals need to perceive and disambiguate environmental features that might obstruct their flight. The most elemental aspect of aerial navigation through such environments is gap identification and 'passability' evaluation. We used bumblebees to seek insights into the mechanisms used for gap identification when confronted with an obstacle in their flight path and behavioral compensations employed to assess gap properties. Initially, bumblebee foragers were trained to fly though an unobstructed flight tunnel that led to a foraging chamber. After the bees were familiar with this situation, we placed a wall containing a gap that unexpectedly obstructed the flight path on a return trip to the hive. The flight trajectories of the bees as they approached the obstacle wall and traversed the gap were analyzed in order to evaluate their behavior as a function of the distance between the gap and a background wall that was placed behind the gap. Bumblebees initially decelerated when confronted with an unexpected obstacle. Deceleration was first noticed when the obstacle subtended around 35 deg on the retina but also depended on the properties of the gap. Subsequently, the bees gradually traded off their longitudinal velocity to lateral velocity and approached the gap with increasing lateral displacement and lateral velocity. Bumblebees shaped their flight trajectory depending on the salience of the gap, indicated in our case by the optic flow contrast between the region within the gap and on the obstacle, which decreased with decreasing distance between the gap and the background wall. As the optic flow contrast decreased, the bees spent an increasing amount of time moving laterally across the obstacles. During these repeated lateral maneuvers, the bees are probably assessing gap geometry and passability.

The problem of home choice in skyline-based homing.

Navigation in cluttered environments is an important challenge for animals and robots alike and has been the subject of many studies trying to explain and mimic animal navigational abilities. However, the question of selecting an appropriate home location has, so far, received only little attention. This is surprising, since the choice of a home location might greatly influence an animal's navigation performance. To address the question of home choice in cluttered environments, a systematic analysis of homing trajectories was performed by computer simulations using a skyline-based local homing method. Our analysis reveals that homing performance strongly depends on the location of the home in the environment. Furthermore, it appears that by assessing homing success in the immediate vicinity of the home, an animal might be able to predict its overall success in returning to it from within a much larger area.

Taking a goal-centred dynamic snapshot as a possibility for local homing in initially naïve bumblebees.

It is essential for central place foragers, such as bumblebees, to return reliably to their nest. Bumblebees, leaving their inconspicuous nest hole for the first time need to gather and learn sufficient information about their surroundings to allow them to return to their nest at the end of their trip, instead of just flying away to forage. Therefore, we assume an intrinsic learning programme that manifests itself in the flight structure immediately after leaving the nest for the first time. In this study, we recorded and analysed the first outbound flight of individually marked naïve bumblebees in an indoor environment. We found characteristic loop-like features in the flight pattern that appear to be necessary for the bees to acquire environmental information and might be relevant for finding the nest hole after a foraging trip. Despite common features in their spatio-temporal organisation, first departure flights from the nest are characterised by a high level of variability in their loop-like flight structure across animals. Changes in turn direction of body orientation, for example, are distributed evenly across the entire area used for the flights without any systematic relationship to the nest location. By considering the common flight motifs and this variability, we came to the hypothesis that a kind of dynamic snapshot is taken during the early phase of departure flights centred at the nest location. The quality of this snapshot is hypothesised to be 'tested' during the later phases of the departure flights concerning its usefulness for local homing.

Local motion adaptation enhances the representation of spatial structure at EMD arrays.

Neuronal representation and extraction of spatial information are essential for behavioral control. For flying insects, a plausible way to gain spatial information is to exploit distance-dependent optic flow that is generated during translational self-motion. Optic flow is computed by arrays of local motion detectors retinotopically arranged in the second neuropile layer of the insect visual system. These motion detectors have adaptive response characteristics, i.e. their responses to motion with a constant or only slowly changing velocity decrease, while their sensitivity to rapid velocity changes is maintained or even increases. We analyzed by a modeling approach how motion adaptation affects signal representation at the output of arrays of motion detectors during simulated flight in artificial and natural 3D environments. We focused on translational flight, because spatial information is only contained in the optic flow induced by translational locomotion. Indeed, flies, bees and other insects segregate their flight into relatively long intersaccadic translational flight sections interspersed with brief and rapid saccadic turns, presumably to maximize periods of translation (80% of the flight). With a novel adaptive model of the insect visual motion pathway we could show that the motion detector responses to background structures of cluttered environments are largely attenuated as a consequence of motion adaptation, while responses to foreground objects stay constant or even increase. This conclusion even holds under the dynamic flight conditions of insects.

Head orientation of walking blowflies is controlled by visual and mechanical cues.

During locomotion, animals employ visual and mechanical cues in order to establish the orientation of their head, which reflects the orientation of the visual coordinate system. However, in certain situations, contradictory cues may suggest different orientations relative to the environment. We recorded blowflies walking on a horizontal or tilted surface surrounded by visual cues suggesting a variety of orientations. We found that the different orientations relative to gravity of visual cues and walking surface were integrated, with the orientation of the surface being the major contributor to head orientation, while visual cues and gravity also play an important role. In contrast, visual cues did not affect body orientation much. Cue integration was modeled as the weighted sum of orientations suggested by the different cues. Our model suggests that in the case of lacking visual cues, more weight is given to gravity.