Ground-nesting insects could use visual tracking for monitoring nest position during learning flights

Ants, bees and wasps are central place foragers. They leave their nests to forage and routinely return to their home-base. Most are guided by memories of the visual panorama and the visual appearance of the local nest environment when pinpointing their nest. These memories are acquired during highly structured learning walks or flights that are performed when leaving the nest for the first time or whenever the insects had difficulties finding the nest during their previous return. Ground-nesting bees and wasps perform such learning flights daily when they depart for the first time. During these flights, the insects turn back to face the nest entrance and subsequently back away from the nest while flying along ever increasing arcs that are centred on the nest. Flying along these arcs, the insects counter-turn in such a way that the nest entrance is always seen in the frontal visual field at slightly lateral positions. Here we asked how the insects may achieve keeping track of the nest entrance location given that it is a small, inconspicuous hole in the ground, surrounded by complex natural structures that undergo unpredictable perspective transformations as the insect pivots around the area and gains distance from it. We reconstructed the natural visual scene experienced by wasps and bees during their learning flights and applied a number of template-based tracking methods to these image sequences. We find that tracking with a fixed template fails very quickly in the course of a learning flight, but that continuously updating the template allowed us to reliably estimate nest direction in reconstructed image sequences. This is true even for later sections of learning flights when the insects are so far away from the nest that they cannot resolve the nest entrance as a visual feature. We discuss why visual goal-anchoring is likely to be important during the acquisition of visual-spatial memories and describe experiments to test whether insects indeed update nest-related templates during their learning flights. © 2014 Springer International Publishing Switzerland

Monkey Steering Responses Reveal Rapid Visual-Motor Feedback

The neural mechanisms underlying primate locomotion are largely unknown. While behavioral and theoretical work has provided a number of ideas of how navigation is controlled, progress will require direct physiolgical tests of the underlying mechanisms. In turn, this will require development of appropriate animal models. We trained three monkeys to track a moving visual target in a simple virtual environment, using a joystick to control their direction. The monkeys learned to quickly and accurately turn to the target, and their steering behavior was quite stereotyped and reliable. Monkeys typically responded to abrupt steps of target direction with a biphasic steering movement, exhibiting modest but transient overshoot. Response latencies averaged approximately 300 ms, and monkeys were typically back on target after about 1 s. We also exploited the variability of responses about the mean to explore the time-course of correlation between target direction and steering response. This analysis revealed a broad peak of correlation spanning approximately 400 ms in the recent past, during which steering errors provoke a compensatory response. This suggests a continuous, visual-motor loop controls steering behavior, even during the epoch surrounding transient inputs. Many results from the human literature also suggest that steering is controlled by such a closed loop. The similarity of our results to those in humans suggests the monkey is a very good animal model for human visually guided steering

Honeybees' Speed Depends on Dorsal as Well as Lateral, Ventral and Frontal Optic Flows

Flying insects use the optic flow to navigate safely in unfamiliar environments, especially by adjusting their speed and their clearance from surrounding objects. It has not yet been established, however, which specific parts of the optical flow field insects use to control their speed. With a view to answering this question, freely flying honeybees were trained to fly along a specially designed tunnel including two successive tapering parts: the first part was tapered in the vertical plane and the second one, in the horizontal plane. The honeybees were found to adjust their speed on the basis of the optic flow they perceived not only in the lateral and ventral parts of their visual field, but also in the dorsal part. More specifically, the honeybees' speed varied monotonically, depending on the minimum cross-section of the tunnel, regardless of whether the narrowing occurred in the horizontal or vertical plane. The honeybees' speed decreased or increased whenever the minimum cross-section decreased or increased. In other words, the larger sum of the two opposite optic flows in the horizontal and vertical planes was kept practically constant thanks to the speed control performed by the honeybees upon encountering a narrowing of the tunnel. The previously described ALIS (“AutopiLot using an Insect-based vision System”) model nicely matches the present behavioral findings. The ALIS model is based on a feedback control scheme that explains how honeybees may keep their speed proportional to the minimum local cross-section of a tunnel, based solely on optic flow processing, without any need for speedometers or rangefinders. The present behavioral findings suggest how flying insects may succeed in adjusting their speed in their complex foraging environments, while at the same time adjusting their distance not only from lateral and ventral objects but also from those located in their dorsal visual field

Signatures of a globally optimal searching strategy in the three-dimensional foraging flights of bumblebees

Simulated annealing is a powerful stochastic search algorithm for locating a global maximum that is hidden among many poorer local maxima in a search space. It is frequently implemented in computers working on complex optimization problems but until now has not been directly observed in nature as a searching strategy adopted by foraging animals. We analysed high-speed video recordings of the three-dimensional searching flights of bumblebees (Bombus terrestris) made in the presence of large or small artificial flowers within a 0.5 m3 enclosed arena. Analyses of the three-dimensional flight patterns in both conditions reveal signatures of simulated annealing searches. After leaving a flower, bees tend to scan back-and forth past that flower before making prospecting flights (loops), whose length increases over time. The search pattern becomes gradually more expansive and culminates when another rewarding flower is found. Bees then scan back and forth in the vicinity of the newly discovered flower and the process repeats. This looping search pattern, in which flight step lengths are typically power-law distributed, provides a relatively simple yet highly efficient strategy for pollinators such as bees to find best quality resources in complex environments made of multiple ephemeral feeding sites with nutritionally variable rewards

Order in Spontaneous Behavior

Brains are usually described as input/output systems: they transform sensory input into motor output. However, the motor output of brains (behavior) is notoriously variable, even under identical sensory conditions. The question of whether this behavioral variability merely reflects residual deviations due to extrinsic random noise in such otherwise deterministic systems or an intrinsic, adaptive indeterminacy trait is central for the basic understanding of brain function. Instead of random noise, we find a fractal order (resembling Lévy flights) in the temporal structure of spontaneous flight maneuvers in tethered Drosophila fruit flies. Lévy-like probabilistic behavior patterns are evolutionarily conserved, suggesting a general neural mechanism underlying spontaneous behavior. Drosophila can produce these patterns endogenously, without any external cues. The fly's behavior is controlled by brain circuits which operate as a nonlinear system with unstable dynamics far from equilibrium. These findings suggest that both general models of brain function and autonomous agents ought to include biologically relevant nonlinear, endogenous behavior-initiating mechanisms if they strive to realistically simulate biological brains or out-compete other agents

Visually Guided Avoidance in the Chameleon (Chamaeleo chameleon): Response Patterns and Lateralization

The common chameleon, Chamaeleo chameleon, is an arboreal lizard with highly independent, large-amplitude eye movements. In response to a moving threat, a chameleon on a perch responds with distinct avoidance movements that are expressed in its continuous positioning on the side of the perch distal to the threat. We analyzed body-exposure patterns during threat avoidance for evidence of lateralization, that is, asymmetry at the functional/behavioral levels. Chameleons were exposed to a threat approaching horizontally from the left or right, as they held onto a vertical pole that was either wider or narrower than the width of their head, providing, respectively, monocular or binocular viewing of the threat. We found two equal-sized sub-groups, each displaying lateralization of motor responses to a given direction of stimulus approach. Such an anti-symmetrical distribution of lateralization in a population may be indicative of situations in which organisms are regularly exposed to crucial stimuli from all spatial directions. This is because a bimodal distribution of responses to threat in a natural population will reduce the spatial advantage of predators

Strategies of the honeybee Apis mellifera during visual search for vertical targets presented at various heights: a role for spatial attention?

When honeybees are presented with a colour discrimination task, they tend to choose swiftly and accurately when objects are presented in the ventral part of their frontal visual field. In contrast, poor performance is observed when objects appear in the dorsal part. Here we investigate if this asymmetry is caused by fixed search patterns or if bees can use alternative search mechanisms such as spatial attention, which allows flexible focusing on different areas of the visual field. We asked individual honeybees to choose an orange rewarded target among blue distractors. Target and distractors were presented in the ventral visual field, the dorsal field or both. Bees presented with targets in the ventral visual field consistently had the highest search efficiency, with rapid decisions, high accuracy and direct flight paths. In contrast, search performance for dorsally located targets was inaccurate and slow at the beginning of the test phase, but bees increased their search performance significantly after a few learning trials: they found the target faster, made fewer errors and flew in a straight line towards the target. However, bees needed thrice as long to improve the search for a dorsally located target when the target's position changed randomly between the ventral and the dorsal visual field. We propose that honeybees form expectations of the location of the target's appearance and adapt their search strategy accordingly. Different possible mechanisms of this behavioural adaptation are discussed.L.M. was recipient of a DOC-fFORTE fellowship of the Austrian Academy of Science at the Department of Integrative Zoology, University of Vienna. L.C. is supported by an ERC Advanced Grant and a Royal Society Wolfson Research Merit Award

Insect Bio-inspired Neural Network Provides New Evidence on How Simple Feature Detectors Can Enable Complex Visual Generalization and Stimulus Location Invariance in the Miniature Brain of Honeybees

This work was supported by a Human Frontier Science Program Grant RGP0022/2014 to LC and Queen Mary University of London Scholarship to MR. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Homing with audio landmarks and path integration

Boeddeker N, Moscatelli A, Ernst M. Homing with audio landmarks and path integration. Journal of Vision. 2014;14(10):2-2