Neural mechanisms of insect navigation

Structural changes shaping the Drosophila ellipsoid body ER-neurons during development and aging

The ellipsoid body (EB) of the insect brain performs pivotal functions in controlling navigation. Input and output of the EB is provided by multiple classes of R-neurons (now referred to as ER-neurons) and columnar neurons which interact with each other in a stereotypical and spatially highly ordered manner. The developmental mechanisms that control the connectivity and topography of EB neurons are largely unknown. One indispensable prerequisite to unravel these mechanisms is to document in detail the sequence of events that shape EB neurons during their development. In this study, we analyzed the development of the Drosophila EB. In addition to globally following the ER-neuron and columnar neuron (sub)classes in the spatial context of their changing environment we performed a single cell analysis using the multi-color flip out (MCFO) system to analyze the developmental trajectory of ER-neurons at different pupal stages, young adults (4d) and aged adults (∼60d). We show that the EB develops as a merger of two distinct elements, a posterior and anterior EB primordium (prEBp and prEBa, respectively. ER-neurons belonging to different subclasses form growth cones and filopodia that associate with the prEBp and prEBa in a pattern that, from early pupal stages onward, foreshadows their mature structure. Filopodia of all ER-subclasses are initially much longer than the dendritic and terminal axonal branches they give rise to, and are pruned back during late pupal stages. Interestingly, extraneous branches, particularly significant in the dendritic domain, are a hallmark of ER-neuron structure in aged brains. Aging is also associated with a decline in synaptic connectivity from columnar neurons, as well as upregulation of presynaptic protein (Brp) in ER-neurons. Our findings advance the EB (and ER-neurons) as a favorable system to visualize and quantify the development and age-related decline of a complex neuronal circuitry.

Transcriptomic Signature of Spatial Navigation in Brains of Desert Ants

Navigation is crucial for central-place foragers to locate food and return to the nest. Cataglyphis ants are renowned for their advanced navigation abilities, relying on landmark cues and path integration. This study aims to uncover the transcriptomic basis of exceptional spatial learning in the central nervous system of Cataglyphis niger. Ants navigated a maze with a food reward, and we examined expression changes linked to correct decisions in subsequent runs. Correct decisions correlated with expression changes in the optic lobes, but not the central brain, showing a downregulation of genes associated with sucrose response and Creb3l1. The latter gene is homologous to Drosophila crebA, which is essential for long-term memory formation. To understand how ants use distance information during path integration, we analyzed expression shifts associated with the last distance traveled. We uncovered a transcriptomic footprint in the central brain, but not in the optic lobes, with genes enriched for energy consumption and neurological functions, including neuronal projection development, synaptic target inhibition, and recognition processes. This suggests that transcriptional activity in the central brain is necessary for estimating distance traveled, which is crucial for path integration. Our study supports the distinct roles of different brain parts for navigation in Cataglyphis ants.

Reinforcement learning as a robotics-inspired framework for insect navigation: from spatial representations to neural implementation

Bees are among the master navigators of the insect world. Despite impressive advances in robot navigation research, the performance of these insects is still unrivaled by any artificial system in terms of training efficiency and generalization capabilities, particularly considering the limited computational capacity. On the other hand, computational principles underlying these extraordinary feats are still only partially understood. The theoretical framework of reinforcement learning (RL) provides an ideal focal point to bring the two fields together for mutual benefit. In particular, we analyze and compare representations of space in robot and insect navigation models through the lens of RL, as the efficiency of insect navigation is likely rooted in an efficient and robust internal representation, linking retinotopic (egocentric) visual input with the geometry of the environment. While RL has long been at the core of robot navigation research, current computational theories of insect navigation are not commonly formulated within this framework, but largely as an associative learning process implemented in the insect brain, especially in the mushroom body (MB). Here we propose specific hypothetical components of the MB circuit that would enable the implementation of a certain class of relatively simple RL algorithms, capable of integrating distinct components of a navigation task, reminiscent of hierarchical RL models used in robot navigation. We discuss how current models of insect and robot navigation are exploring representations beyond classical, complete map-like representations, with spatial information being embedded in the respective latent representations to varying degrees.

A neural circuit architecture for rapid learning in goal-directed navigation

Anchoring goals to spatial representations enables flexible navigation but is challenging in novel environments when both representations must be acquired simultaneously. We propose a framework for how Drosophila uses internal representations of head direction (HD) to build goal representations upon selective thermal reinforcement. We show that flies use stochastically generated fixations and directed saccades to express heading preferences in an operant visual learning paradigm and that HD neurons are required to modify these preferences based on reinforcement. We used a symmetric visual setting to expose how flies' HD and goal representations co-evolve and how the reliability of these interacting representations impacts behavior. Finally, we describe how rapid learning of new goal headings may rest on a behavioral policy whose parameters are flexible but whose form is genetically encoded in circuit architecture. Such evolutionarily structured architectures, which enable rapidly adaptive behavior driven by internal representations, may be relevant across species.

Control and recalibration of path integration in place cells using optic flow

Hippocampal place cells are influenced by both self-motion (idiothetic) signals and external sensory landmarks as an animal navigates its environment. To continuously update a position signal on an internal 'cognitive map', the hippocampal system integrates self-motion signals over time, a process that relies on a finely calibrated path integration gain that relates movement in physical space to movement on the cognitive map. It is unclear whether idiothetic cues alone, such as optic flow, exert sufficient influence on the cognitive map to enable recalibration of path integration, or if polarizing position information provided by landmarks is essential for this recalibration. Here, we demonstrate both recalibration of path integration gain and systematic control of place fields by pure optic flow information in freely moving rats. These findings demonstrate that the brain continuously rebalances the influence of conflicting idiothetic cues to fine-tune the neural dynamics of path integration, and that this recalibration process does not require a top-down, unambiguous position signal from landmarks.

The Budding Neuroscience of Ant Social Behavior

Ant physiology has been fashioned by 100 million years of social evolution. Ants perform many sophisticated social and collective behaviors yet possess nervous systems similar in schematic and scale to that of the fruit fly Drosophila melanogaster, a popular solitary model organism. Ants are thus attractive complementary subjects to investigate adaptations pertaining to complex social behaviors that are absent in flies. Despite research interest in ant behavior and the neurobiological foundations of sociality more broadly, our understanding of the ant nervous system is incomplete. Recent technical advances have enabled cutting-edge investigations of the nervous system in a fashion that is less dependent on model choice, opening the door for mechanistic social insect neuroscience. In this review, we revisit important aspects of what is known about the ant nervous system and behavior, and we look forward to how functional circuit neuroscience in ants will help us understand what distinguishes solitary animals from highly social ones.

Acute exposure to caffeine improves foraging in an invasive ant

Argentine ants, Linepithema humile, are a particularly concerning invasive species. Control efforts often fall short likely due to a lack of sustained bait consumption. Using neuroactives, such as caffeine, to improve ant learning and navigation could increase recruitment and consumption of toxic baits. Here, we exposed L. humile to a range of caffeine concentrations and a complex ecologically relevant task: an open landscape foraging experiment. Without caffeine, we found no effect of consecutive foraging visits on the time the ants take to reach a reward, suggesting a failure to learn the reward's location. However, under low to intermediate caffeine concentrations ants were 38% faster with each consecutive visit, implying that caffeine boosts learning. Interestingly, such improvements were lost at high doses. In contrast, caffeine had no impact on the ants' homing behavior. Adding moderate levels of caffeine to baits could improve ant's ability to learn its location, improving bait efficacy.

Investigating visual navigation using spiking neural network models of the insect mushroom bodies

Ants are capable of learning long visually guided foraging routes with limited neural resources. The visual scene memory needed for this behaviour is mediated by the mushroom bodies; an insect brain region important for learning and memory. In a visual navigation context, the mushroom bodies are theorised to act as familiarity detectors, guiding ants to views that are similar to those previously learned when first travelling along a foraging route. Evidence from behavioural experiments, computational studies and brain lesions all support this idea. Here we further investigate the role of mushroom bodies in visual navigation with a spiking neural network model learning complex natural scenes. By implementing these networks in GeNN-a library for building GPU accelerated spiking neural networks-we were able to test these models offline on an image database representing navigation through a complex outdoor natural environment, and also online embodied on a robot. The mushroom body model successfully learnt a large series of visual scenes (400 scenes corresponding to a 27 m route) and used these memories to choose accurate heading directions during route recapitulation in both complex environments. Through analysing our model's Kenyon cell (KC) activity, we were able to demonstrate that KC activity is directly related to the respective novelty of input images. Through conducting a parameter search we found that there is a non-linear dependence between optimal KC to visual projection neuron (VPN) connection sparsity and the length of time the model is presented with an image stimulus. The parameter search also showed training the model on lower proportions of a route generally produced better accuracy when testing on the entire route. We embodied the mushroom body model and comparator visual navigation algorithms on a Quanser Q-car robot with all processing running on an Nvidia Jetson TX2. On a 6.5 m route, the mushroom body model had a mean distance to training route (error) of 0.144 ± 0.088 m over 5 trials, which was performance comparable to standard visual-only navigation algorithms. Thus, we have demonstrated that a biologically plausible model of the ant mushroom body can navigate complex environments both in simulation and the real world. Understanding the neural basis of this behaviour will provide insight into how neural circuits are tuned to rapidly learn behaviourally relevant information from complex environments and provide inspiration for creating bio-mimetic computer/robotic systems that can learn rapidly with low energy requirements.

Evolution of neural circuitry and cognition

Neural circuits govern the interface between the external environment, internal cues and outwardly directed behaviours. To process multiple environmental stimuli and integrate these with internal state requires considerable neural computation. Expansion in neural network size, most readily represented by whole brain size, has historically been linked to behavioural complexity, or the predominance of cognitive behaviours. Yet, it is largely unclear which aspects of circuit variation impact variation in performance. A key question in the field of evolutionary neurobiology is therefore how neural circuits evolve to allow improved behavioural performance or innovation. We discuss this question by first exploring how volumetric changes in brain areas reflect actual neural circuit change. We explore three major axes of neural circuit evolution-replication, restructuring and reconditioning of cells and circuits-and discuss how these could relate to broader phenotypes and behavioural variation. This discussion touches on the relevant uses and limitations of volumetrics, while advocating a more circuit-based view of cognition. We then use this framework to showcase an example from the insect brain, the multi-sensory integration and internal processing that is shared between the mushroom bodies and central complex. We end by identifying future trends in this research area, which promise to advance the field of evolutionary neurobiology.

Landmarks, beacons, or panoramic views: What do pigeons attend to for guidance in familiar environments?

Birds and social insects represent excellent systems for understanding visually guided navigation. Both animal groups use surrounding visual cues for homing and foraging. Ants extract sufficient spatial information from panoramic views, which naturally embed all near and far spatial information, for successful homing. Although egocentric panoramic views allow for parsimonious explanations of navigational behaviors, this potential source of spatial information has been mostly neglected during studies of vertebrates. Here we investigate how distinct landmarks, a beacon, and panoramic views influence the reorientation behavior in pigeons (Columba livia). Pigeons were trained to search for a location characterized by a beacon and several distinct landmarks. Transformation tests manipulated aspects of the landmark configuration, allowing for a dissociation among navigational strategies. Quantitative image and path analyses provided support that the panoramic view was used by the pigeons. Although the results from some individuals support the use of beaconing, overall the pigeons relied predominantly on the panoramic view when spatial cues provided conflicting information regarding the goal location. Reorientation based on vector and bearing information derived from distinct landmarks as well as environmental geometry failed to account fully for the results. Thus, the results of our study support that pigeons can use panoramic views for reorientation in familiar environments. Given that the current model for landmark use by pigeons posits the use of different vectors from an object, a global panorama-matching strategy suggests a fundamental change in the theory of how pigeons use surrounding visual cues for localization.

Endotaxis: A neuromorphic algorithm for mapping, goal-learning, navigation, and patrolling

An animal entering a new environment typically faces three challenges: explore the space for resources, memorize their locations, and navigate towards those targets as needed. Here we propose a neural algorithm that can solve all these problems and operates reliably in diverse and complex environments. At its core, the mechanism makes use of a behavioral module common to all motile animals, namely the ability to follow an odor to its source. We show how the brain can learn to generate internal "virtual odors" that guide the animal to any location of interest. This endotaxis algorithm can be implemented with a simple 3-layer neural circuit using only biologically realistic structures and learning rules. Several neural components of this scheme are found in brains from insects to humans. Nature may have evolved a general mechanism for search and navigation on the ancient backbone of chemotaxis.

Resolving conflict between aversive and appetitive learning of views: how ants shift to a new route during navigation

Ants store and recall views associated with foraging success, facilitating future foraging journeys. Negative views are also learned, but instead prompt avoidance behaviors such as turning away. However, little is known about the aversive view's role in navigation, the effect of cue conflict, or the contextual relationship between learning and recalling. In this study, we tested Myrmecia midas' capacity for aversive learning of views either independently of or in conflict with appetitive events. We either captured and released foragers when reaching a location or let them pass unhindered. After a few journeys, captured foragers exhibited aversive learning by circumventing the capture location and increasing both meandering and scanning. Ants that experienced foraging-appetitive and homing-aversive events on their journeys exhibited lower rates of avoidance behavior and scans than those experiencing aversive events in both outbound and homebound journeys. The foraging-aversive and homing-aversive ants exhibited similar levels of avoidance and scanning as those that experienced the foraging-aversive and homing-appetitive. We found that foragers showed evidence of context specificity in their scanning behavior, but not in other measures of aversive learning. The foragers did not increase their meandering and scans while approaching the views associated with aversive events. In addition to shedding light on the role of aversive views in navigation, our finding has important implications for understanding the learning mechanisms triggered by handling animals.

Emergent spatial goals in an integrative model of the insect central complex

The insect central complex appears to encode and process spatial information through vector manipulation. Here, we draw on recent insights into circuit structure to fuse previous models of sensory-guided navigation, path integration and vector memory. Specifically, we propose that the allocentric encoding of location provided by path integration creates a spatially stable anchor for converging sensory signals that is relevant in multiple behavioural contexts. The allocentric reference frame given by path integration transforms a goal direction into a goal location and we demonstrate through modelling that it can enhance approach of a sensory target in noisy, cluttered environments or with temporally sparse stimuli. We further show the same circuit can improve performance in the more complex navigational task of route following. The model suggests specific functional roles for circuit elements of the central complex that helps explain their high preservation across insect species.

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.

Neural Networks for Navigation: From Connections to Computations

Many animals can navigate toward a goal they cannot see based on an internal representation of that goal in the brain's spatial maps. These maps are organized around networks with stable fixed-point dynamics (attractors), anchored to landmarks, and reciprocally connected to motor control. This review summarizes recent progress in understanding these networks, focusing on studies in arthropods. One factor driving recent progress is the availability of the Drosophila connectome; however, it is increasingly clear that navigation depends on ongoing synaptic plasticity in these networks. Functional synapses appear to be continually reselected from the set of anatomical potential synapses based on the interaction of Hebbian learning rules, sensory feedback, attractor dynamics, and neuromodulation. This can explain how the brain's maps of space are rapidly updated; it may also explain how the brain can initialize goals as stable fixed points for navigation.

Rapid expansion and visual specialisation of learning and memory centres in the brains of Heliconiini butterflies

Changes in the abundance and diversity of neural cell types, and their connectivity, shape brain composition and provide the substrate for behavioral evolution. Although investment in sensory brain regions is understood to be largely driven by the relative ecological importance of particular sensory modalities, how selective pressures impact the elaboration of integrative brain centers has been more difficult to pinpoint. Here, we provide evidence of extensive, mosaic expansion of an integration brain center among closely related species, which is not explained by changes in sites of primary sensory input. By building new datasets of neural traits among a tribe of diverse Neotropical butterflies, the Heliconiini, we detected several major evolutionary expansions of the mushroom bodies, central brain structures pivotal for insect learning and memory. The genus Heliconius, which exhibits a unique dietary innovation, pollen-feeding, and derived foraging behaviors reliant on spatial memory, shows the most extreme enlargement. This expansion is primarily associated with increased visual processing areas and coincides with increased precision of visual processing, and enhanced long term memory. These results demonstrate that selection for behavioral innovation and enhanced cognitive ability occurred through expansion and localized specialization in integrative brain centers.

Scanning behaviour in ants: an interplay between random-rate processes and oscillators

At the start of a journey home or to a foraging site, ants often stop, interrupting their forward movement, turn on the spot a number of times, and fixate in different directions. These scanning bouts are thought to provide visual information for choosing a path to travel. The temporal organization of such scanning bouts has implications about the neural organisation of navigational behaviour. We examined (1) the temporal distribution of the start of such scanning bouts and (2) the dynamics of saccadic body turns and fixations that compose a scanning bout in Australian desert ants, Melophorus bagoti, as they came out of a walled channel onto open field at the start of their homeward journey. Ants were caught when they neared their nest and displaced to different locations to start their journey home again. The observed parameters were mostly similar across familiar and unfamiliar locations. The turning angles of saccadic body turning to the right or left showed some stereotypy, with a peak just under 45°. The direction of such saccades appears to be determined by a slow oscillatory process as described in other insect species. In timing, however, both the distribution of inter-scanning-bout intervals and individual fixation durations showed exponential characteristics, the signature for a random-rate or Poisson process. Neurobiologically, therefore, there must be some process that switches behaviour (starting a scanning bout or ending a fixation) with equal probability at every moment in time. We discuss how chance events in the ant brain that occasionally reach a threshold for triggering such behaviours can generate the results.

Homing tasks performed using variations of crawling gait patterns reveal a role for attention in podokinetic path integration

Self-motion can be perceived via podokinetic information, that is, based upon the movements of the legs during legged locomotion. This information can be integrated in order to perceive a path of travel through the environment (i.e., via podokinetic path integration). Two types of podokinetic information have been distinguished by analyzing the patterns of bias that result from manipulating the gait patterns used in direct-route homing tasks. Each type of podokinetic information has been associated specific groupings of gaits that support equivalent perceptual measurements of self-motion. Specifically, gaits are grouped if they can be varied across the outbound and inbound phases of a homing task (e.g., walking outbound and jogging inbound) and the accuracy of homing task performances does not differ from matched-gait control conditions. Recently, it was theorized that different types of podokinetic information are related to the differences in the kinematic form of limb motions in these groupings of gaits. Here we test an alternative hypothesis, namely that attention plays a role in selecting the type of podokinetic information. In three experiments, we manipulated the crawling gait patterns used in direct-route homing tasks. Consistent with our hypotheses, we observe that self-motion is equivalently measured via crawling movement patterns that (1) have distinct kinematic forms, but that similarly direct participants' attention onto controlling the swing phase trajectories of their arms, and (2) have distinct inter-limb coordination patterns (i.e., pace vs. trot), but do not require attention to be specifically focused upon swing phase arm trajectories.

A monocular wide-field speed sensor inspired by the crabs' visual system for traffic analysis

The development of visual sensors for traffic analysis can benefit from mimicking two fundamental aspects of the visual system of crabs: their panoramic vision and their visual processing strategy adapted to a flat world. First, the use of omnidirectional cameras in urban environments allows for analyzing the simultaneous movement of many objects of interest over broad areas. This would reduce the costs and complications associated with infrastructure: installation, synchronization, maintenance, and operation of traditional vision systems that use multiple cameras with a limited field of view. Second, in urban traffic analysis, the objects of interest (e.g. vehicles and pedestrians) move on the ground surface. This constraint allows the calculation of the 3D trajectory of the vehicles using a single camera without the need to use binocular vision techniques.The main contribution of this work is to show that the strategy used by crabs to visually analyze their habitat (monocular omnidirectional vision with the assumption of a flat world ) is useful for developing a simple and effective method to estimate the speed of vehicles on long trajectories in urban environments. It is shown that the proposed method estimates the speed with a root mean squared error of 2.7 km h^-1.

Comparative biology of spatial navigation in three arachnid orders (Amblypygi, Araneae, and Scorpiones)

From both comparative biology and translational research perspectives, there is escalating interest in understanding how animals navigate their environments. Considerable work is being directed towards understanding the sensory transduction and neural processing of environmental stimuli that guide animals to, for example, food and shelter. While much has been learned about the spatial orientation behavior, sensory cues, and neurophysiology of champion navigators such as bees and ants, many other, often overlooked animal species possess extraordinary sensory and spatial capabilities that can broaden our understanding of the behavioral and neural mechanisms of animal navigation. For example, arachnids are predators that often return to retreats after hunting excursions. Many of these arachnid central-place foragers are large and highly conducive to scientific investigation. In this review we highlight research on three orders within the Class Arachnida: Amblypygi (whip spiders), Araneae (spiders), and Scorpiones (scorpions). For each, we describe (I) their natural history and spatial navigation, (II) how they sense the world, (III) what information they use to navigate, and (IV) how they process information for navigation. We discuss similarities and differences among the groups and highlight potential avenues for future research.

An intrinsic oscillator underlies visual navigation in ants

Many insects display lateral oscillations while moving, but how these oscillations are produced and participate in visual navigation remains unclear. Here, we show that visually navigating ants continuously display regular lateral oscillations coupled with variations of forward speed that strongly optimize the distance covered while simultaneously enabling them to scan left and right directions. This pattern of movement is produced endogenously and conserved across navigational contexts in two phylogenetically distant ant species. Moreover, the oscillations' amplitude can be modulated by both innate or learnt visual cues to adjust the exploration/exploitation balance to the current need. This lower-level motor pattern thus drastically reduces the degree of freedom needed for higher-level strategies to control behavior. The observed dynamical signature readily emerges from a simple neural circuit model of the insect's conserved pre-motor area known as the lateral accessory lobe, offering a surprisingly simple but effective neural control and endorsing oscillation as a core, ancestral way of moving in insects.

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.

Varieties of visual navigation in insects

The behaviours and cognitive mechanisms animals use to orient, navigate, and remember spatial locations exemplify how cognitive abilities have evolved to suit a number of different mobile lifestyles and habitats. While spatial cognition observed in vertebrates has been well characterised in recent decades, of no less interest are the great strides that have also been made in characterizing and understanding the behavioural and cognitive basis of orientation and navigation in invertebrate models and in particular insects. Insects are known to exhibit remarkable spatial cognitive abilities and are able to successfully migrate over long distances or pinpoint known locations relying on multiple navigational strategies similar to those found in vertebrate models-all while operating under the constraint of relatively limited neural architectures. Insect orientation and navigation systems are often tailored to each species' ecology, yet common mechanistic principles can be observed repeatedly. Of these, reliance on visual cues is observed across a wide number of insect groups. In this review, we characterise some of the behavioural strategies used by insects to solve navigational problems, including orientation over short-distances, migratory heading maintenance over long distances, and homing behaviours to known locations. We describe behavioural research using examples from a few well-studied insect species to illustrate how visual cues are used in navigation and how they interact with non-visual cues and strategies.

Behavioral algorithms and neural mechanisms underlying odor-modulated locomotion in insects

Odors released from mates and resources such as a host and food are often the first sensory signals that an animal can detect. Changes in locomotion in response to odors are an important mechanism by which animals access resources important to their survival. Odor-modulated changes in locomotion in insects constitute a whole suite of flexible behaviors that allow insects to close in on these resources from long distances and perform local searches to locate and subsequently assess them. Here, we review changes in odor-mediated locomotion across many insect species. We emphasize that changes in locomotion induced by odors are diverse. In particular, the olfactory stimulus is sporadic at long distances and becomes more continuous at short distances. This distance-dependent change in temporal profile produces a corresponding change in an insect's locomotory strategy. We also discuss the neural circuits underlying odor modulation of locomotion.

The role of learning-walk related multisensory experience in rewiring visual circuits in the desert ant brain

Efficient spatial orientation in the natural environment is crucial for the survival of most animal species. Cataglyphis desert ants possess excellent navigational skills. After far-ranging foraging excursions, the ants return to their inconspicuous nest entrance using celestial and panoramic cues. This review focuses on the question about how naïve ants acquire the necessary spatial information and adjust their visual compass systems. Naïve ants perform structured learning walks during their transition from the dark nest interior to foraging under bright sunlight. During initial learning walks, the ants perform rotational movements with nest-directed views using the earth’s magnetic field as an earthbound compass reference. Experimental manipulations demonstrate that specific sky compass cues trigger structural neuronal plasticity in visual circuits to integration centers in the central complex and mushroom bodies. During learning walks, rotation of the sky-polarization pattern is required for an increase in volume and synaptic complexes in both integration centers. In contrast, passive light exposure triggers light-spectrum (especially UV light) dependent changes in synaptic complexes upstream of the central complex. We discuss a multisensory circuit model in the ant brain for pathways mediating structural neuroplasticity at different levels following passive light exposure and multisensory experience during the performance of learning walks.

Geometry-based navigation in the dark: layout symmetry facilitates spatial learning in the house cricket, Acheta domesticus, in the absence of visual cues

The capacity to navigate by layout geometry has been widely recognized as a robust strategy of place-finding. It has been reported in various species, although most studies were performed with vision-based paradigms. In the presented study, we aimed to investigate layout symmetry-based navigation in the house cricket, Acheta domesticus, in the absence of visual cues. For this purpose, we used a non-visual paradigm modeled on the Tennessee Williams setup. We ensured that the visual cues were indeed inaccessible to insects. In the main experiment, we tested whether crickets are capable of learning to localize the centrally positioned, inconspicuous cool spot in heated arenas of various shapes (i.e., circular, square, triangular, and asymmetric quadrilateral). We found that the symmetry of the arena significantly facilitates crickets’ learning to find the cool spot, indicated by the increased time spent on the cool spot and the decreased latency in locating it in subsequent trials. To investigate mechanisms utilized by crickets, we analyzed their approach paths to the spot. We found that crickets used both heuristic and directed strategies of approaching the target, with the dominance of a semi-directed strategy (i.e., a thigmotactic phase preceding direct navigation to the target). We propose that the poor performance of crickets in the asymmetrical quadrilateral arena may be explained by the difficulty of encoding its layout with cues from a single modality.

Insect navigation: Bumblebees walk the walk

A new study shows that bumblebees can display path integration while walking in a small laboratory arena. This opens a new avenue for studying how insects' brains can encode direction and distance.

Aversive view memories and risk perception in navigating ants

Many ants establish foraging routes through learning views of the visual panorama. Route models have focused primarily on attractive view use, which experienced foragers orient towards to return to known sites. However, aversive views have recently been uncovered as a key component of route learning. Here, Cataglyphis velox rapidly learned aversive views, when associated with a negative outcome, a period of captivity in vegetation, triggering increases in hesitation behavior. These memories were based on the accumulation of experiences over multiple trips with each new experience regulating forager hesitancy. Foragers were also sensitive to captivity time differences, suggesting they possess some mechanism to quantify duration. Finally, we analyzed foragers' perception of risky (i.e. variable) versus stable aversive outcomes by associating two sites along the route with distinct captivity schedules, a fixed or variable duration, with the same mean across training. Foragers exhibited fewer hesitations in response to risky outcomes compared to fixed ones, indicating they perceived risky outcomes as less severe. Results align with a logarithmic relationship between captivity duration and hesitations, suggesting that aversive stimulus perception is a logarithm of its actual value. We discuss how aversive view learning could be executed within the mushroom bodies circuitry following a prediction error rule.

How the insect central complex could coordinate multimodal navigation

The central complex of the insect midbrain is thought to coordinate insect guidance strategies. Computational models can account for specific behaviours, but their applicability across sensory and task domains remains untested. Here, we assess the capacity of our previous model (Sun et al. 2020) of visual navigation to generalise to olfactory navigation and its coordination with other guidance in flies and ants. We show that fundamental to this capacity is the use of a biologically plausible neural copy-and-shift mechanism that ensures sensory information is presented in a format compatible with the insect steering circuit regardless of its source. Moreover, the same mechanism is shown to allow the transfer cues from unstable/egocentric to stable/geocentric frames of reference, providing a first account of the mechanism by which foraging insects robustly recover from environmental disturbances. We propose that these circuits can be flexibly repurposed by different insect navigators to address their unique ecological needs.

Approach Direction Prior to Landing Explains Patterns of Colour Learning in Bees

Gaze direction is closely coupled with body movement in insects and other animals. If movement patterns interfere with the acquisition of visual information, insects can actively adjust them to seek relevant cues. Alternatively, where multiple visual cues are available, an insect's movements may influence how it perceives a scene. We show that the way a foraging bumblebee approaches a floral pattern could determine what it learns about the pattern. When trained to vertical bicoloured patterns, bumblebees consistently approached from below centre in order to land in the centre of the target where the reward was located. In subsequent tests, the bees preferred the colour of the lower half of the pattern that they predominantly faced during the approach and landing sequence. A predicted change of learning outcomes occurred when the contrast line was moved up or down off-centre: learned preferences again reflected relative frontal exposure to each colour during the approach, independent of the overall ratio of colours. This mechanism may underpin learning strategies in both simple and complex visual discriminations, highlighting that morphology and action patterns determines how animals solve sensory learning tasks. The deterministic effect of movement on visual learning may have substantially influenced the evolution of floral signals, particularly where plants depend on fine-scaled movements of pollinators on flowers.

Anesthesia disrupts distance, but not direction, of path integration memory

Solitary foraging insects, such as ants, maintain an estimate of the direction and distance to their starting location as they move away from it, in a process known as path integration. This estimate, commonly known as the "home vector," is updated continuously as the ant moves^1-4 and is reset as soon as it enters its nest,⁵ yet ants prevented from returning to their nest can still use their home vector when released several hours later.^6,7 This conjunction of fast update and long persistence of the home vector memory does not directly map to existing accounts of short-, mid-, and long-term memory;^2,8-12 hence, the substrate of this memory remains unknown. Chill-coma anesthesia^13-15 has previously been shown to affect associative memory retention in fruit flies^14,16 and honeybees.^9,17,18 We investigate the nature of path integration memory by anesthetizing ants after they have accumulated home vector information and testing if the memory persists on recovery. We show that after anesthesia the memory of the distance ants have traveled is degraded, but the memory of the direction is retained. We also show that this is consistent with models of path integration that maintain the memory in a redundant Cartesian coordinate system and with the hypothesis that chill-coma produces a proportional reduction of the memory, rather than a subtractive reduction or increase of noise. The observed effect is not compatible with a memory based on recurrent circuit activity and points toward an activity-dependent molecular process as the basis of path integration memory.

A Flying Platform to Investigate Neuronal Correlates of Navigation in the Honey Bee (Apis mellifera)

Navigating animals combine multiple perceptual faculties, learn during exploration, retrieve multi-facetted memory contents, and exhibit goal-directedness as an expression of their current needs and motivations. Navigation in insects has been linked to a variety of underlying strategies such as path integration, view familiarity, visual beaconing, and goal-directed orientation with respect to previously learned ground structures. Most works, however, study navigation either from a field perspective, analyzing purely behavioral observations, or combine computational models with neurophysiological evidence obtained from lab experiments. The honey bee (Apis mellifera) has long been a popular model in the search for neural correlates of complex behaviors and exhibits extraordinary navigational capabilities. However, the neural basis for bee navigation has not yet been explored under natural conditions. Here, we propose a novel methodology to record from the brain of a copter-mounted honey bee. This way, the animal experiences natural multimodal sensory inputs in a natural environment that is familiar to her. We have developed a miniaturized electrophysiology recording system which is able to record spikes in the presence of time-varying electric noise from the copter's motors and rotors, and devised an experimental procedure to record from mushroom body extrinsic neurons (MBENs). We analyze the resulting electrophysiological data combined with a reconstruction of the animal's visual perception and find that the neural activity of MBENs is linked to sharp turns, possibly related to the relative motion of visual features. This method is a significant technological step toward recording brain activity of navigating honey bees under natural conditions. By providing all system specifications in an online repository, we hope to close a methodological gap and stimulate further research informing future computational models of insect navigation.

The Diversity of Eyes and Vision

Every aspect of vision, from the opsin proteins to the eyes and the ways that they serve animal behavior, is incredibly diverse. It is only with an evolutionary perspective that this diversity can be understood and fully appreciated. In this review, I describe and explain the diversity at each level and try to convey an understanding of how the origin of the first opsin some 800 million years ago could initiate the avalanche that produced the astonishing diversity of eyes and vision that we see today. Despite the diversity, many types of photoreceptors, eyes, and visual roles have evolved multiple times independently in different animals, revealing a pattern of eye evolution strictly guided by functional constraints and driven by the evolution of gradually more demanding behaviors. I conclude the review by introducing a novel distinction between active and passive vision that points to uncharted territories in vision research. Expected final online publication date for the Annual Review of Vision Science, Volume 7 is September 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Movements, embodiment and the emergence of decisions. Insights from insect navigation

We readily infer that animals make decisions, but what this implies is usually not clearly defined. The notion of 'decision-making' ultimately stems from human introspection, and is thus loaded with anthropomorphic assumptions. Notably, the decision is made internally, is based on information, and precedes the goal directed behaviour. Also, making a decision implies that 'something' did it, thus hints at the presence of a cognitive mind, whose existence is independent of the decision itself. This view may convey some truth, but here I take the opposite stance. Using examples from research in insect navigation, this essay highlights how apparent decisions can emerge without a brain, how actions can precede information or how sophisticated goal directed behaviours can be implemented without neural decisions. This perspective requires us to shake off the idea that behaviour is a consequence of the brain; and embrace the concept that movements arise from - as much as participate in - distributed interactions between various computational centres - including the body - that reverberate in closed-loop with the environment. From this perspective we may start to picture how a cognitive mind can be the consequence, rather than the cause, of such neural and body movements.

Assessing the relative contribution of vision to odometry via manipulations of gait in an over-ground homing task

The visual, vestibular, and haptic perceptual systems are each able to detect self-motion. Such information can be integrated during locomotion to perceive traversed distances. The process of distance integration is referred to as odometry. Visual odometry relies on information in optic flow patterns. For haptic odometry, such information is associated with leg movement patterns. Recently, it has been shown that haptic odometry is differently calibrated for different types of gaits. Here, we use this fact to examine the relative contributions of the perceptual systems to odometry. We studied a simple homing task in which participants travelled set distances away from an initial starting location (outbound phase), before turning and attempting to walk back to that location (inbound phase). We manipulated whether outbound gait was a walk or a gallop-walk. We also manipulated the outbound availability of optic flow. Inbound reports were performed via walking with eyes closed. Consistent with previous studies of haptic odometry, inbound reports were shorter when the outbound gait was a gallop-walk. We showed that the availability of optic flow decreased this effect. In contrast, the availability of optic flow did not have an observable effect when the outbound gait was walking. We interpreted this to suggest that visual odometry and haptic odometry via walking are similarly calibrated. By measuring the decrease in shortening in the gallop-walk condition, and scaling it relative to the walk condition, we estimated a relative contribution of optic flow to odometry of 41%. Our results present a proof of concept for a new, potentially more generalizable, method for examining the contributions of different perceptual systems to odometry, and by extension, path integration. We discuss implications for understanding human wayfinding.

Towards a multi-level understanding in insect navigation

To understand the brain is to understand behaviour. However, understanding behaviour itself requires consideration of sensory information, body movements and the animal's ecology. Therefore, understanding the link between neurons and behaviour is a multi-level problem, which can be achieved when considering Marr's three levels of understanding: behaviour, computation, and neural implementation. Rather than establishing direct links between neurons and behaviour, the matter boils down to understanding two transitions: the link between neurons and brain computation on one hand, and the link between brain computations and behaviour on the other hand. The field of insect navigation illustrates well the power of such two-sided endeavour. We provide here examples revealing that each transition requires its own approach with its own intrinsic difficulties, and show how modelling can help us reach the desired multi-level understanding.

The Antarium: A Reconstructed Visual Reality Device for Ant Navigation Research

We constructed a large projection device (the Antarium) with 20,000 UV-Blue-Green LEDs that allows us to present tethered ants with views of their natural foraging environment. The ants walk on an air-cushioned trackball, their movements are registered and can be fed back to the visual panorama. Views are generated in a 3D model of the ants’ environment so that they experience the changing visual world in the same way as they do when foraging naturally. The Antarium is a biscribed pentakis dodecahedron with 55 facets of identical isosceles triangles. The length of the base of the triangles is 368 mm resulting in a device that is roughly 1 m in diameter. Each triangle contains 361 blue/green LEDs and nine UV LEDs. The 55 triangles of the Antarium have 19,855 Green and Blue pixels and 495 UV pixels, covering 360° azimuth and elevation from −50° below the horizon to +90° above the horizon. The angular resolution is 1.5° for Green and Blue LEDs and 6.7° for UV LEDs, offering 65,536 intensity levels at a flicker frequency of more than 9,000 Hz and a framerate of 190 fps. Also, the direction and degree of polarisation of the UV LEDs can be adjusted through polarisers mounted on the axles of rotary actuators. We build 3D models of the natural foraging environment of ants using purely camera-based methods. We reconstruct panoramic scenes at any point within these models, by projecting panoramic images onto six virtual cameras which capture a cube-map of images to be projected by the LEDs of the Antarium. The Antarium is a unique instrument to investigate visual navigation in ants. In an open loop, it allows us to provide ants with familiar and unfamiliar views, with completely featureless visual scenes, or with scenes that are altered in spatial or spectral composition. In closed-loop, we can study the behavior of ants that are virtually displaced within their natural foraging environment. In the future, the Antarium can also be used to investigate the dynamics of navigational guidance and the neurophysiological basis of ant navigation in natural visual environments.

Arthropod spatial cognition

The feats of arthropods, and of the well-studied insects and crustaceans in particular, have fascinated scientists and laymen alike for centuries. Arthropods show a diverse repertoire of cognitive feats, of often unexpected sophistication. Despite their smaller brains and resulting lower neuronal capacity, the cognitive abilities of arthropods are comparable to, or may even exceed, those of vertebrates, depending on the species compared. Miniature brains often provide parsimonious but smart solutions for complex behaviours or ecologically relevant problems. This makes arthropods inspiring subjects for basic research, bionics, and robotics. Investigations of arthropod spatial cognition have originally concentrated on the honeybee, an animal domesticated for several thousand years. Bees are easy to keep and handle, making this species amenable to experimental study. However, there are an estimated 5–10 million arthropod species worldwide, with a broad diversity of lifestyles, ecology, and cognitive abilities. This high diversity provides ample opportunity for comparative analyses. Comparative study, rather than focusing on single model species, is well suited to scrutinise the link between ecological niche, lifestyle, and cognitive competence. It also allows the discovery of general concepts that are transferable between distantly related groups of organisms. With species diversity and a comparative approach in mind, this special issue compiles four review articles and ten original research reports from a spectrum of arthropod species. These contributions range from the well-studied hymenopterans, and ants in particular, to chelicerates and crustaceans. They thus present a broad spectrum of glimpses into current research on arthropod spatial cognition, and together they cogently emphasise the merits of research into arthropod cognitive achievements.

The Neuroscience of Spatial Navigation and the Relationship to Artificial Intelligence

Recent advances in artificial intelligence (AI) and neuroscience are impressive. In AI, this includes the development of computer programs that can beat a grandmaster at GO or outperform human radiologists at cancer detection. A great deal of these technological developments are directly related to progress in artificial neural networks-initially inspired by our knowledge about how the brain carries out computation. In parallel, neuroscience has also experienced significant advances in understanding the brain. For example, in the field of spatial navigation, knowledge about the mechanisms and brain regions involved in neural computations of cognitive maps-an internal representation of space-recently received the Nobel Prize in medicine. Much of the recent progress in neuroscience has partly been due to the development of technology used to record from very large populations of neurons in multiple regions of the brain with exquisite temporal and spatial resolution in behaving animals. With the advent of the vast quantities of data that these techniques allow us to collect there has been an increased interest in the intersection between AI and neuroscience, many of these intersections involve using AI as a novel tool to explore and analyze these large data sets. However, given the common initial motivation point-to understand the brain-these disciplines could be more strongly linked. Currently much of this potential synergy is not being realized. We propose that spatial navigation is an excellent area in which these two disciplines can converge to help advance what we know about the brain. In this review, we first summarize progress in the neuroscience of spatial navigation and reinforcement learning. We then turn our attention to discuss how spatial navigation has been modeled using descriptive, mechanistic, and normative approaches and the use of AI in such models. Next, we discuss how AI can advance neuroscience, how neuroscience can advance AI, and the limitations of these approaches. We finally conclude by highlighting promising lines of research in which spatial navigation can be the point of intersection between neuroscience and AI and how this can contribute to the advancement of the understanding of intelligent behavior.

Route-following ants respond to alterations of the view sequence

Ants can navigate by comparing the currently perceived view with memorised views along a familiar foraging route. Models regarding route-following suggest that the views are stored and recalled independently of the sequence in which they occur. Hence, the ant only needs to evaluate the instantaneous familiarity of the current view to obtain a heading direction. This study investigates whether ant homing behaviour is influenced by alterations in the sequence of views experienced along a familiar route, using the frequency of stop-and-scan behaviour as an indicator of the ant's navigational uncertainty. Ants were trained to forage between their nest and a feeder which they exited through a short channel before proceeding along the homeward route. In tests, ants were collected before entering the nest and released again in the channel, which was placed either in its original location or halfway along the route. Ants exiting the familiar channel in the middle of the route would thus experience familiar views in a novel sequence. Results show that ants exiting the channel scan significantly more when they find themselves in the middle of the route, compared with when emerging at the expected location near the feeder. This behaviour suggests that previously encountered views influence the recognition of current views, even when these views are highly familiar, revealing a sequence component to route memory. How information about view sequences could be implemented in the insect brain, as well as potential alternative explanations to our results, are discussed.

For humans navigating without vision, navigation depends upon the layout of mechanically contacted ground surfaces

Navigation can be haptically guided. In specific, tissue deformations arising from both limb motions during locomotion (i.e., gait patterns) and mechanical interactions between the limbs and the environment can convey information, detected by the haptic perceptual system, about how the body is moving relative to the environment. Here, we test hypotheses concerning the properties of mechanically contacted environments relevant to navigation of this kind. We studied blindfolded participants implicitly learning to perceive their location within environments that were physically encountered via walking on, stepping on, and probing ground surfaces with a cane. Environments were straight-line paths with elevated sections where the path either narrowed or remained the same width. We formed hypotheses concerning how these two environments would affect spatial updating and reorientation processes. In the constant pathwidth environment, homing task accuracy was higher and a manipulation of the elevated surface, to be either unchanged or (unbeknown to participants) shortened, biased the performance. This was consistent with our hypothesis of a metric recalibration scaled to elevated surface extent. In the narrowing pathwidth environment, elevated surface shortening did not bias performance. This supported our hypothesis of positional recalibration resulting from contact with the leading edge of the elevated surface. We discuss why certain environmental properties, such as path-narrowing, have significance for how one becomes implicitly oriented the surrounding environment.

A Robust Visual System for Small Target Motion Detection Against Cluttered Moving Backgrounds

Monitoring small objects against cluttered moving backgrounds is a huge challenge to future robotic vision systems. As a source of inspiration, insects are quite apt at searching for mates and tracking prey, which always appear as small dim speckles in the visual field. The exquisite sensitivity of insects for small target motion, as revealed recently, is coming from a class of specific neurons called small target motion detectors (STMDs). Although a few STMD-based models have been proposed, these existing models only use motion information for small target detection and cannot discriminate small targets from small-target-like background features (named fake features). To address this problem, this paper proposes a novel visual system model (STMD+) for small target motion detection, which is composed of four subsystems-ommatidia, motion pathway, contrast pathway, and mushroom body. Compared with the existing STMD-based models, the additional contrast pathway extracts directional contrast from luminance signals to eliminate false positive background motion. The directional contrast and the extracted motion information by the motion pathway are integrated into the mushroom body for small target discrimination. Extensive experiments showed the significant and consistent improvements of the proposed visual system model over the existing STMD-based models against fake features.

Opponent processes in visual memories: A model of attraction and repulsion in navigating insects' mushroom bodies

Solitary foraging insects display stunning navigational behaviours in visually complex natural environments. Current literature assumes that these insects are mostly driven by attractive visual memories, which are learnt when the insect's gaze is precisely oriented toward the goal direction, typically along its familiar route or towards its nest. That way, an insect could return home by simply moving in the direction that appears most familiar. Here we show using virtual reconstructions of natural environments that this principle suffers from fundamental drawbacks, notably, a given view of the world does not provide information about whether the agent should turn or not to reach its goal. We propose a simple model where the agent continuously compares its current view with both goal and anti-goal visual memories, which are treated as attractive and repulsive respectively. We show that this strategy effectively results in an opponent process, albeit not at the perceptual level-such as those proposed for colour vision or polarisation detection-but at the level of the environmental space. This opponent process results in a signal that strongly correlates with the angular error of the current body orientation so that a single view of the world now suffices to indicate whether the agent should turn or not. By incorporating this principle into a simple agent navigating in reconstructed natural environments, we show that it overcomes the usual shortcomings and produces a step-increase in navigation effectiveness and robustness. Our findings provide a functional explanation to recent behavioural observations in ants and why and how so-called aversive and appetitive memories must be combined. We propose a likely neural implementation based on insects' mushroom bodies' circuitry that produces behavioural and neural predictions contrasting with previous models.

Deltamethrin Impairs Honeybees (Apis mellifera) Dancing Communication

As a commonly used pyrethroid insecticide, deltamethrin is very toxic to honeybees, which seriously threatens the managed and feral honeybee population. Because deltamethrin is a nerve agent, it may interfere with the nervous system of honeybees, such as dance behavior and memory-related characteristics. We found that the waggle dances were less precise in honeybees that consumed syrup containing deltamethrin (pesticide group) than those that consumed normal sucrose syrup (control group). Compared with the control group, honeybees of the pesticide group significantly increased number of circuits per 15 s, the divergence angle, return phases in waggle dances, as well as the crop content of the dance followers. Furthermore, six learning and memory-related genes were significantly interfered with the gene expression levels. Our data suggest that the sublethal dose of deltamethrin impaired the honeybees' learning and memory and resulted in cognitive disorder. The novel results assist in establishing guidelines for the risk assessment of pesticide to honeybee safety and prevention of nontarget biological agriculture pesticide poisoning.