An Anatomically Constrained Model for Path Integration in the Bee Brain

Inhibitory control explains locomotor statistics in walking Drosophila

In order to forage for food, many animals regulate not only specific limb movements but the statistics of locomotor behavior, switching between long-range dispersal and local search depending on resource availability. How premotor circuits regulate locomotor statistics is not clear. Here, we analyze and model locomotor statistics and their modulation by attractive food odor in walking Drosophila. Food odor evokes three motor regimes in flies: baseline walking, upwind running during odor, and search behavior following odor loss. During search, we find that flies adopt higher angular velocities and slower ground speeds and turn for longer periods in the same direction. We further find that flies adopt periods of different mean ground speed and that these state changes influence the length of odor-evoked runs. We next developed a simple model of neural locomotor control that suggests that contralateral inhibition plays a key role in regulating the statistical features of locomotion. As the fly connectome predicts decussating inhibitory neurons in the premotor lateral accessory lobe (LAL), we gained genetic access to a subset of these neurons and tested their effects on behavior. We identified one population whose activation induces all three signature of local search and that regulates angular velocity at odor offset. We identified a second population, including a single LAL neuron pair, that bidirectionally regulates ground speed. Together, our work develops a biologically plausible computational architecture that captures the statistical features of fly locomotion across behavioral states and identifies neural substrates of these computations.

Spatiotemporal computations in the insect celestial compass

Obtaining a geocentric directional reference from a celestial compass requires compensation for the sun's movement during the day (relative to the observer), which depends on the earth's rotation, time of year and the observer's latitude. We examine how insects could solve this problem, assuming they have clock neurons that represent time as a sinusoidal oscillation, and taking into account the neuroanatomy of their celestial compass pathway. We show how this circuit could exploit trigonometric identities to perform the required spatiotemporal calculations. Our basic model assumes a constant change in sun azimuth (the 'hour angle'), which is recentred on solar noon for changing day lengths. In a more complete model, the time of year is represented by an oscillation with an annual period, and the latitude is estimated from the inclination of the geomagnetic field. Evaluating these models in simulated migration and foraging behaviours shows the hour angle may be sufficient.

Vector-based navigation in desert ants: the significance of path-integration vectors

In the longstanding discussion of whether insects, especially central place foragers such as bees and ants, use metric representations of their landmark surroundings (so-called "cognitive maps"), the ability to find novel shortcuts between familiar locations has been considered one of the most decisive proofs for the use of such maps. Here we show by channel-based field experiments that desert ants Cataglyphis can travel such shortcuts between locations (defined by memorized goal vectors) just on the basis of path integration. When trained to visit two spatially separated feeders A and B they later travel the hitherto novel route A→B. This behavior may originate from the interaction of goal vectors retrieved from long-term memory and the current vector computed by the continuously running path integrator. Based on former experiments, we further argue that path integration is a necessary requirement also for acquiring landmark information (in form of learned goal-directed views). This emphasizes the paramount importance of path integration in these central place foragers. Finally we hypothesize that the ant's overall system of navigation consists in the optimal combination of path-integration vectors and view-based vectors, and thus handles and uses vectorial information without the need of constructing a "vector map", in which vectors are linked to known places in the environment others than to the origin of all journeys, the nest.

A vector-based strategy for olfactory navigation in Drosophila

Odors serve as essential cues for navigation. Although tracking an odor plume has been modeled as a reflexive process, it remains unclear whether animals can use memories of their past odor encounters to infer the spatial structure of their chemical environment or their location within it. Here we developed a virtual-reality olfactory paradigm that allows head-fixed Drosophila to navigate structured chemical landscapes, offering insight into how memory mechanisms shape their navigational strategies. We found that flies track an appetitive odor corridor by following its boundary, alternating between rapid counterturns to exit the plume and directed returns to its edge. Using a combination of behavioral modeling, functional calcium imaging, and neural perturbations, we demonstrate that this 'edge-tracking' strategy relies on vector-based computations within the Drosophila central complex in which flies store and dynamically update memories of the direction to return them to the plume's boundary. Consistent with this, we find that FC2 neurons within the fan-shaped body, which encode a fly's navigational goal, signal the direction back to the odor boundary when flies are outside the plume. Together, our studies suggest that flies leverage the plume's boundary as a dynamic landmark to guide their navigation, analogous to the memory-based strategies other insects use for long-distance migration or homing to their nests. Plume tracking thus uses components of a conserved navigational toolkit, enabling flies to use memory mechanisms to navigate through a complex shifting chemical landscape.

The neuroethology of ant navigation

Unlike any other group of animals, all ant species are social: individual ants share the food they gather with their nestmates and as a consequence they must repeatedly leave their nest to find food and then return home with it. These back-and-forth foraging trips have been studied for about a century and much of our growing understanding of the strategies underlying animal navigation has come from these studies. One important strategy that ants use to keep track of where they are on a foraging trip is 'path integration', in which they continuously update a 'home vector' that gives their estimated distance and direction from the nest. As path integration accumulates errors, it cannot be relied on to bring ants precisely home: such precision is accomplished by using views of the nest acquired before they start foraging. Further learning is scaffolded by home vectors or remembered food vectors, which guide a route and help in learning useful views experienced on the way. Many species rely on olfaction as well as vision for route guidance and the full details of their foraging paths have revealed how ants use a mix of innate and learnt multisensory cues. Wood ants, a species on which we focus in this review, take an oscillating path along a pheromone trail to sample odours, but acquire visual information only at the peaks and troughs of the oscillations. To provide a working model of the neural basis of the multimodal navigational strategies of ants, we outline the anatomy and functioning of major central brain areas and neural circuits - the central complex, mushroom bodies and lateral accessory lobes - that are involved in the coordination of navigational behaviour and the learning of visual and olfactory patterns. Because ant brains have not yet been well-studied, we rely on the work that has been done with other species - notably, Drosophila, silkworm moths and bees - to derive plausible neural circuitry that can deliver the ants' navigational strategies.

I2Bot: an open-source tool for multi-modal and embodied simulation of insect navigation

Achieving a comprehensive understanding of animal intelligence demands an integrative approach that acknowledges the interplay between an organism's brain, body and environment. Insects, despite their limited computational resources, demonstrate remarkable abilities in navigation. Existing computational models often fall short in faithfully replicating the morphology of real insects and their interactions with the environment, hindering validation and practical application in robotics. To address these gaps, we present I2Bot, a novel simulation tool based on the morphological characteristics of real insects. This tool empowers robotic models with dynamic sensory capabilities, realistic modelling of insect morphology, physical dynamics and sensory capacity. By integrating gait controllers and computational models into I2Bot, we have implemented classical embodied navigation behaviours and revealed some fundamental navigation principles. By open-sourcing I2Bot, we aim to accelerate the understanding of insect intelligence and foster advances in the development of autonomous robotic systems.

Inhibitory control of locomotor statistics in walking Drosophila

In order to forage for food, many animals regulate not only specific limb movements but the statistics of locomotor behavior over time, switching between long-range dispersal and localized search depending on resource availability. How pre-motor circuits regulate such locomotor statistics is not clear. Here we analyze and model locomotor statistics in walking Drosophila, and their modulation by attractive food odor. Odor evokes three motor regimes in flies: baseline walking, upwind running during odor, and search behavior following odor loss. During search behavior, we find that flies adopt higher angular velocities and slower ground speeds, and tend to turn for longer periods of time in one direction. We further find that flies spontaneously adopt periods of different mean ground speed, and that these changes in state influence the length of odor-evoked runs. We next developed a simple model of neural locomotor control that suggests that contralateral inhibition plays a key role in regulating the statistical features of locomotion. As the fly connectome predicts decussating inhibitory neurons in the lateral accessory lobe (LAL), a pre-motor structure, we gained genetic access to a subset of these neurons and tested their effects on behavior. We identified one population of neurons whose activation induces all three signature of search and that bi-directionally regulates angular velocity at odor offset. We identified a second group of neurons, including a single LAL neuron pair, that bi-directionally regulate ground speed. Together, our work develops a biologically plausible computational architecture that captures the statistical features of fly locomotion across behavioral states and identifies potential neural substrates of these computations.

Neuroarchitecture of the Central Complex in the Madeira Cockroach Rhyparobia maderae: Tangential Neurons

Navigating in diverse environments to find food, shelter, or mating partners is an important ability for nearly all animals. Insects have evolved diverse navigational strategies to survive in challenging and unknown environments. In the insect brain, the central complex (CX) plays an important role in spatial orientation and directed locomotion. It consists of the protocerebral bridge (PB), the central body with upper (CBU) and lower division (CBL), and the paired noduli (NO). As shown in various insect species, the CX integrates multisensory cues, including sky compass signals, wind direction, and ego-motion to provide goal-directed vector output used for steering locomotion and flight. While most of these data originate from studies on day-active insects, less is known about night-active species such as cockroaches. Following our analysis of columnar and pontine neurons, the present study complements our investigation of the cellular architecture of the CX of the Madeira cockroach by analyzing tangential neurons. Based on single-cell tracer injections, we provide further details on the internal organization of the CX and distinguished 27 types of tangential neuron, including three types of neuron innervating the PB, six types of the CBL, and 18 types of the CBU. The anterior lip, a brain area unknown in flies and highly reduced in bees, and the crepine are strongly connected to the cockroach CBU in contrast to other insect species. One tangential neuron of the CBU revealed a direct connection between the mushroom bodies and the CBU.

A computational model for angular velocity integration in a locust heading circuit

Accurate navigation often requires the maintenance of a robust internal estimate of heading relative to external surroundings. We present a model for angular velocity integration in a desert locust heading circuit, applying concepts from early theoretical work on heading circuits in mammals to a novel biological context in insects. In contrast to similar models proposed for the fruit fly, this circuit model uses a single 360° heading direction representation and is updated by neuromodulatory angular velocity inputs. Our computational model was implemented using steady-state firing rate neurons with dynamical synapses. The circuit connectivity was constrained by biological data, and remaining degrees of freedom were optimised with a machine learning approach to yield physiologically plausible neuron activities. We demonstrate that the integration of heading and angular velocity in this circuit is robust to noise. The heading signal can be effectively used as input to an existing insect goal-directed steering circuit, adapted for outbound locomotion in a steady direction that resembles locust migration. Our study supports the possibility that similar computations for orientation may be implemented differently in the neural hardware of the fruit fly and the locust.

Velocity coding in the central brain of bumblebees

Moving animals experience wide-field optic flow due to the displacement of the retinal image during motion. These cues provide information about self-motion and are important for flight control and stabilization, and for more complex tasks like path integration. Although in honeybees and bumblebees the use of wide-field optic flow in behavioral tasks is well investigated, little is known about the underlying neuronal processing of these cues. Furthermore, there is a discrepancy between the temporal frequency tuning observed in most motion-sensitive neurons described so far from the optic lobe of insects and the velocity tuning that has been shown for many behaviors. Here, we investigated response properties of motion-sensitive neurons in the central brain of bumblebees. Extracellular recordings allowed us to present a large number of stimuli to probe the spatiotemporal tuning of these neurons. We presented moving gratings that simulated either front-to-back or back-to-front optic flow and found three response types. Direction-selective responses of one of the groups matched those of TN-neurons, which provide optic flow information to the central complex, whereas the other groups contained neurons with purely excitatory responses that were either selective or nonselective for stimulus direction. Most recorded units showed velocity-coding properties at lower angular velocities, but showed spatial frequency-dependent responses at higher velocities. Based on behavioral data, neuronal modeling work has previously predicted the existence of nondirection-selective neurons with such properties. Our data now provide physiological evidence for these neurons and show that neurons with TN-like properties exhibit a similar velocity-dependent coding.NEW & NOTEWORTHY Using extracellular recordings, we show that neurons in the central brain and central complex of bumblebees show velocity coding properties.

Neural circuits for goal-directed navigation across species

Across species, navigation is crucial for finding both resources and shelter. In vertebrates, the hippocampus supports memory-guided goal-directed navigation, whereas in arthropods the central complex supports similar functions. A growing literature is revealing similarities and differences in the organization and function of these brain regions. We review current knowledge about how each structure supports goal-directed navigation by building internal representations of the position or orientation of an animal in space, and of the location or direction of potential goals. We describe input pathways to each structure - medial and lateral entorhinal cortex in vertebrates, and columnar and tangential neurons in insects - that primarily encode spatial and non-spatial information, respectively. Finally, we highlight similarities and differences in spatial encoding across clades and suggest experimental approaches to compare coding principles and behavioral capabilities across species. Such a comparative approach can provide new insights into the neural basis of spatial navigation and neural computation.

Fine-grained descending control of steering in walking Drosophila

Locomotion involves rhythmic limb movement patterns that originate in circuits outside the brain. Purposeful locomotion requires descending commands from the brain, but we do not understand how these commands are structured. Here, we investigate this issue, focusing on the control of steering in walking Drosophila. First, we describe different limb "gestures" associated with different steering maneuvers. Next, we identify a set of descending neurons whose activity predicts steering. Focusing on two descending cell types downstream of distinct brain networks, we show that they evoke specific limb gestures: one lengthens strides on the outside of a turn, while the other attenuates strides on the inside of a turn. Our results suggest that a single descending neuron can have opposite effects during different locomotor rhythm phases, and we identify networks positioned to implement this phase-specific gating. Together, our results show how purposeful locomotion emerges from specific, coordinated modulations of low-level patterns.

Drosophila require both green and UV wavelengths for sun orientation but lack a time-compensated sun compass

Celestial orientation and navigation are performed by many organisms in contexts as diverse as migration, nest finding and straight-line orientation. The vinegar fly, Drosophila melanogaster, performs menotaxis in response to celestial cues during tethered flight and can disperse more than 10 km under field conditions. However, we still do not understand how spectral components of celestial cues and pauses in flight impact heading direction in flies. To assess individual heading, we began by testing flies in a rotating tether arena using a single green LED as a stimulus. We found that flies robustly perform menotaxis and fly straight for at least 20 min. Flies maintain their preferred heading directions after experiencing a period of darkness or stopping flight, even up to 2 h, but reset their heading when the LED changes position, suggesting that flies do not treat this stimulus as the sun. Next, we assessed the flies' responses to a UV spot alone or a paired UV-green stimulus - two dots situated 180 deg apart to simulate the solar and antisolar hemispheres. We found that flies respond to UV much as they do to green light; however, when the stimuli are paired, flies adjust for sudden 90 deg movements, performing sun orientation. Lastly, we found no evidence of a time-compensated sun compass when we moved the paired stimuli at 15 deg h-1 for 6 h. This study demonstrates that wavelength influences how flies respond to visual cues during flight, shaping the interpretation of visual information to execute an appropriate behavioral response.

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.

Formalising the role of behaviour in neuroscience

We develop a mathematical approach to formally proving that certain neural computations and representations exist based on patterns observed in an organism's behaviour. To illustrate, we provide a simple set of conditions under which an ant's ability to determine how far it is from its nest would logically imply neural structures isomorphic to the natural numbers ℕ . We generalise these results to arbitrary behaviours and representations and show what mathematical characterisation of neural computation and representation is simplest while being maximally predictive of behaviour. We develop this framework in detail using a path integration example, where an organism's ability to search for its nest in the correct location implies representational structures isomorphic to two-dimensional coordinates under addition. We also study a system for processinga n b n strings common in comparative work. Our approach provides an objective way to determine what theory of a physical system is best, addressing a fundamental challenge in neuroscientific inference. These results motivate considering which neurobiological structures have the requisite formal structure and are otherwise physically plausible given relevant physical considerations such as generalisability, information density, thermodynamic stability and energetic cost.

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.

Non-symbolic estimation of big and small ratios with accurate and noisy feedback

The ratio of two magnitudes can take one of two values depending on the order they are operated on: a 'big' ratio of the larger to smaller magnitude, or a 'small' ratio of the smaller to larger. Although big and small ratio scales have different metric properties and carry divergent predictions for perceptual comparison tasks, no psychophysical studies have directly compared them. Two experiments are reported in which subjects implicitly learned to compare pairs of brightnesses and line lengths by non-symbolic feedback based on the scaled big ratio, small ratio or difference of the magnitudes presented. Results of Experiment 1 showed all three operations were learned quickly and estimated with a high degree of accuracy that did not significantly differ across groups or between intensive and extensive modalities, though regressions on individual data suggested an overall predisposition towards differences. Experiment 2 tested whether subjects learned to estimate the operation trained or to associate stimulus pairs with correct responses. For each operation, Gaussian noise was added to the feedback that was constant for repetitions of each pair. For all subjects, coefficients for the added noise component were negative when entered in a regression model alongside the trained differences or ratios, and were statistically significant in 80% of individual cases. Thus, subjects learned to estimate the comparative operations and effectively ignored or suppressed the added noise. These results suggest the perceptual system is highly flexible in its capacity for non-symbolic computation, which may reflect a deeper connection between perceptual structure and mathematics.

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.

Estimating orientation in natural scenes: A spiking neural network model of the insect central complex

The central complex of insects contains cells, organised as a ring attractor, that encode head direction. The 'bump' of activity in the ring can be updated by idiothetic cues and external sensory information. Plasticity at the synapses between these cells and the ring neurons, that are responsible for bringing sensory information into the central complex, has been proposed to form a mapping between visual cues and the heading estimate which allows for more accurate tracking of the current heading, than if only idiothetic information were used. In Drosophila, ring neurons have well characterised non-linear receptive fields. In this work we produce synthetic versions of these visual receptive fields using a combination of excitatory inputs and mutual inhibition between ring neurons. We use these receptive fields to bring visual information into a spiking neural network model of the insect central complex based on the recently published Drosophila connectome. Previous modelling work has focused on how this circuit functions as a ring attractor using the same type of simple visual cues commonly used experimentally. While we initially test the model on these simple stimuli, we then go on to apply the model to complex natural scenes containing multiple conflicting cues. We show that this simple visual filtering provided by the ring neurons is sufficient to form a mapping between heading and visual features and maintain the heading estimate in the absence of angular velocity input. The network is successful at tracking heading even when presented with videos of natural scenes containing conflicting information from environmental changes and translation of the camera.

Parallel vector memories in the brain of a bee as foundation for flexible navigation

Insects rely on path integration (vector-based navigation) and landmark guidance to perform sophisticated navigational feats, rivaling those seen in mammals. Bees in particular exhibit complex navigation behaviors including creating optimal routes and novel shortcuts between locations, an ability historically indicative of the presence of a cognitive map. A mammalian cognitive map has been widely accepted. However, in insects, the existence of a centralized cognitive map is highly contentious. Using a controlled laboratory assay that condenses foraging behaviors to short distances in walking bumblebees, we reveal that vectors learned during path integration can be transferred to long-term memory, that multiple such vectors can be stored in parallel, and that these vectors can be recalled at a familiar location and used for homeward navigation. These findings demonstrate that bees meet the two fundamental requirements of a vector-based analog of a decentralized cognitive map: Home vectors need to be stored in long-term memory and need to be recalled from remembered locations. Thus, our data demonstrate that bees possess the foundational elements for a vector-based map. By utilizing this relatively simple strategy for spatial organization, insects may achieve high-level navigation behaviors seen in vertebrates with the limited number of neurons in their brains, circumventing the computational requirements associated with the cognitive maps of mammals.

Visual route following for tiny autonomous robots

Navigation is an essential capability for autonomous robots. In particular, visual navigation has been a major research topic in robotics because cameras are lightweight, power-efficient sensors that provide rich information on the environment. However, the main challenge of visual navigation is that it requires substantial computational power and memory for visual processing and storage of the results. As of yet, this has precluded its use on small, extremely resource-constrained robots such as lightweight drones. Inspired by the parsimony of natural intelligence, we propose an insect-inspired approach toward visual navigation that is specifically aimed at extremely resource-restricted robots. It is a route-following approach in which a robot's outbound trajectory is stored as a collection of highly compressed panoramic images together with their spatial relationships as measured with odometry. During the inbound journey, the robot uses a combination of odometry and visual homing to return to the stored locations, with visual homing preventing the buildup of odometric drift. A main advancement of the proposed strategy is that the number of stored compressed images is minimized by spacing them apart as far as the accuracy of odometry allows. To demonstrate the suitability for small systems, we implemented the strategy on a tiny 56-gram drone. The drone could successfully follow routes up to 100 meters with a trajectory representation that consumed less than 20 bytes per meter. The presented method forms a substantial step toward the autonomous visual navigation of tiny robots, facilitating their more widespread application.

Insect Flight: State of the Field and Future Directions

The evolution of flight in an early winged insect ancestral lineage is recognized as a key adaptation explaining the unparalleled success and diversification of insects. Subsequent transitions and modifications to flight machinery, including secondary reductions and losses, also play a central role in shaping the impacts of insects on broadscale geographic and ecological processes and patterns in the present and future. Given the importance of insect flight, there has been a centuries-long history of research and debate on the evolutionary origins and biological mechanisms of flight. Here, we revisit this history from an interdisciplinary perspective, discussing recent discoveries regarding the developmental origins, physiology, biomechanics, and neurobiology and sensory control of flight in a diverse set of insect models. We also identify major outstanding questions yet to be addressed and provide recommendations for overcoming current methodological challenges faced when studying insect flight, which will allow the field to continue to move forward in new and exciting directions. By integrating mechanistic work into ecological and evolutionary contexts, we hope that this synthesis promotes and stimulates new interdisciplinary research efforts necessary to close the many existing gaps about the causes and consequences of insect flight evolution.

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.

Theoretical principles explain the structure of the insect head direction circuit

To navigate their environment, insects need to keep track of their orientation. Previous work has shown that insects encode their head direction as a sinusoidal activity pattern around a ring of neurons arranged in an eight-column structure. However, it is unclear whether this sinusoidal encoding of head direction is just an evolutionary coincidence or if it offers a particular functional advantage. To address this question, we establish the basic mathematical requirements for direction encoding and show that it can be performed by many circuits, all with different activity patterns. Among these activity patterns, we prove that the sinusoidal one is the most noise-resilient, but only when coupled with a sinusoidal connectivity pattern between the encoding neurons. We compare this predicted optimal connectivity pattern with anatomical data from the head direction circuits of the locust and the fruit fly, finding that our theory agrees with experimental evidence. Furthermore, we demonstrate that our predicted circuit can emerge using Hebbian plasticity, implying that the neural connectivity does not need to be explicitly encoded in the genetic program of the insect but rather can emerge during development. Finally, we illustrate that in our theory, the consistent presence of the eight-column organisation of head direction circuits across multiple insect species is not a chance artefact but instead can be explained by basic evolutionary principles.

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.

Neuroethology: Decoding the waggle dance

A new study combining high-speed video recordings and computational modeling has revealed an overlooked feature of the famous honeybee waggle dance, yielding the first biologically plausible neural circuit model of how the information transmitted via the waggle dance could be assimilated by the follower bees.

Dynamic antennal positioning allows honeybee followers to decode the dance

The honeybee waggle dance has been widely studied as a communication system, yet we know little about how nestmates assimilate the information needed to navigate toward the signaled resource. They are required to detect the dancer's orientation relative to gravity and duration of the waggle phase and translate this into a flight vector with a direction relative to the sun1 and distance from the hive.2,3 Moreover, they appear capable of doing so from varied, dynamically changing positions around the dancer. Using high-speed, high-resolution video, we have uncovered a previously unremarked correlation between antennal position and the relative body axes of dancer and follower bees. Combined with new information about antennal inputs4,5 and spatial encoding in the insect central complex,6,7 we show how a neural circuit first proposed to underlie path integration could be adapted to decoding the dance and acquiring the signaled information as a flight vector that can be followed to the resource. This provides the first plausible account of how the bee brain could support the interpretation of its dance language.

Effect of repetition of vertical and horizontal routes on navigation performance in Australian bull ants

Solitarily foraging ant species differ in their reliance on their two primary navigational systems- path integration and visual learning. Despite many species of Australian bull ants spending most of their foraging time on their foraging tree, little is known about the use of these systems while climbing. "Rewinding" displacements are commonly used to understand navigational system usage, and work by introducing a mismatch between these navigational systems, by displacing foragers after they have run-down their path integration vector. We used rewinding to test the role of path integration on the arboreal and terrestrial navigation of M. midas. We rewound foragers along either the vertical portion, the ground surface portion, or across both portions of their homing trip. Since rewinding involves repeatedly capturing and releasing foragers, we included a nondisplacement, capture-and-release control, in which the path integration vector is unchanged. We found that rewound foragers do not seem to accumulate path integration vector, although a limited effect of vertical rewinding was found, suggesting a potential higher sensitivity while descending the foraging tree. However, the decrease in navigational efficiency due to capture was larger than the vertical rewinding effect, which along with the negative impact of the vertical surface, and an interaction between capture and rewinding, may suggest aversion rather than path integration caused the vertical rewinding response. Together these results add to the evidence that M. midas makes minimal use of path integration while foraging, and the growing evidence that they are capable of quickly learning from aversive stimulus.

A perspective on neuroethology: what the past teaches us about the future of neuroethology

For 100 years, the Journal of Comparative Physiology-A has significantly supported research in the field of neuroethology. The celebration of the journal's centennial is a great time point to appreciate the recent progress in neuroethology and to discuss possible avenues of the field. Animal behavior is the main source of inspiration for neuroethologists. This is illustrated by the huge diversity of investigated behaviors and species. To explain behavior at a mechanistic level, neuroethologists combine neuroscientific approaches with sophisticated behavioral analysis. The rapid technological progress in neuroscience makes neuroethology a highly dynamic and exciting field of research. To summarize the recent scientific progress in neuroethology, I went through all abstracts of the last six International Congresses for Neuroethology (ICNs 2010-2022) and categorized them based on the sensory modalities, experimental model species, and research topics. This highlights the diversity of neuroethology and gives us a perspective on the field's scientific future. At the end, I highlight three research topics that may, among others, influence the future of neuroethology. I hope that sharing my roots may inspire other scientists to follow neuroethological approaches.

Anatomical organization of the cerebrum of the praying mantis Hierodula membranacea

Many predatory animals, such as the praying mantis, use vision for prey detection and capture. Mantises are known in particular for their capability to estimate distances to prey by stereoscopic vision. While the initial visual processing centers have been extensively documented, we lack knowledge on the architecture of central brain regions, pivotal for sensory motor transformation and higher brain functions. To close this gap, we provide a three-dimensional (3D) reconstruction of the central brain of the Asian mantis, Hierodula membranacea. The atlas facilitates in-depth analysis of neuron ramification regions and aides in elucidating potential neuronal pathways. We integrated seven 3D-reconstructed visual interneurons into the atlas. In total, 42 distinct neuropils of the cerebrum were reconstructed based on synapsin-immunolabeled whole-mount brains. Backfills from the antenna and maxillary palps, as well as immunolabeling of γ-aminobutyric acid (GABA) and tyrosine hydroxylase (TH), further substantiate the identification and boundaries of brain areas. The composition and internal organization of the neuropils were compared to the anatomical organization of the brain of the fruit fly (Drosophila melanogaster) and the two available brain atlases of Polyneoptera-the desert locust (Schistocerca gregaria) and the Madeira cockroach (Rhyparobia maderae). This study paves the way for detailed analyses of neuronal circuitry and promotes cross-species brain comparisons. We discuss differences in brain organization between holometabolous and polyneopteran insects. Identification of ramification sites of the visual neurons integrated into the atlas supports previous claims about homologous structures in the optic lobes of flies and mantises.

Importance of magnetic information for neuronal plasticity in desert ants

Many animal species rely on the Earth's magnetic field during navigation, but where in the brain magnetic information is processed is still unknown. To unravel this, we manipulated the natural magnetic field at the nest entrance of Cataglyphis desert ants and investigated how this affects relevant brain regions during early compass calibration. We found that manipulating the Earth's magnetic field has profound effects on neuronal plasticity in two sensory integration centers. Magnetic field manipulations interfere with a typical look-back behavior during learning walks of naive ants. Most importantly, structural analyses in the ants' neuronal compass (central complex) and memory centers (mushroom bodies) demonstrate that magnetic information affects neuronal plasticity during early visual learning. This suggests that magnetic information does not only serve as a compass cue for navigation but also as a global reference system crucial for spatial memory formation. We propose a neural circuit for integration of magnetic information into visual guidance networks in the ant brain. Taken together, our results provide an insight into the neural substrate for magnetic navigation in insects.

The conserved RNA-binding protein Imp is required for the specification and function of olfactory navigation circuitry in Drosophila

Complex behaviors depend on the precise developmental specification of neuronal circuits, but the relationship between genetic programs for neural development, circuit structure, and behavioral output is often unclear. The central complex (CX) is a conserved sensory-motor integration center in insects, which governs many higher-order behaviors and largely derives from a small number of type II neural stem cells (NSCs). Here, we show that Imp, a conserved IGF-II mRNA-binding protein expressed in type II NSCs, plays a role in specifying essential components of CX olfactory navigation circuitry. We show the following: (1) that multiple components of olfactory navigation circuitry arise from type II NSCs. (2) Manipulating Imp expression in type II NSCs alters the number and morphology of many of these circuit elements, with the most potent effects on neurons targeting the ventral layers of the fan-shaped body (FB). (3) Imp regulates the specification of Tachykinin-expressing ventral FB input neurons. (4) Imp is required in type II NSCs for establishing proper morphology of the CX neuropil structures. (5) Loss of Imp in type II NSCs abolishes upwind orientation to attractive odor while leaving locomotion and odor-evoked regulation of movement intact. Taken together, our findings establish that a temporally expressed gene can regulate the expression of a complex behavior by developmentally regulating the specification of multiple circuit components and provides a first step toward a developmental dissection of the CX and its roles in behavior.

Transforming a head direction signal into a goal-oriented steering command

To navigate, we must continuously estimate the direction we are headed in, and we must correct deviations from our goal1. Direction estimation is accomplished by ring attractor networks in the head direction system2,3. However, we do not fully understand how the sense of direction is used to guide action. Drosophila connectome analyses4,5 reveal three cell populations (PFL3R, PFL3L and PFL2) that connect the head direction system to the locomotor system. Here we use imaging, electrophysiology and chemogenetic stimulation during navigation to show how these populations function. Each population receives a shifted copy of the head direction vector, such that their three reference frames are shifted approximately 120° relative to each other. Each cell type then compares its own head direction vector with a common goal vector; specifically, it evaluates the congruence of these vectors via a nonlinear transformation. The output of all three cell populations is then combined to generate locomotor commands. PFL3R cells are recruited when the fly is oriented to the left of its goal, and their activity drives rightward turning; the reverse is true for PFL3L. Meanwhile, PFL2 cells increase steering speed, and are recruited when the fly is oriented far from its goal. PFL2 cells adaptively increase the strength of steering as directional error increases, effectively managing the tradeoff between speed and accuracy. Together, our results show how a map of space in the brain can be combined with an internal goal to generate action commands, via a transformation from world-centric coordinates to body-centric coordinates.

Converting an allocentric goal into an egocentric steering signal

Neuronal signals that are relevant for spatial navigation have been described in many species1-10. However, a circuit-level understanding of how such signals interact to guide navigational behaviour is lacking. Here we characterize a neuronal circuit in the Drosophila central complex that compares internally generated estimates of the heading and goal angles of the fly-both of which are encoded in world-centred (allocentric) coordinates-to generate a body-centred (egocentric) steering signal. Past work has suggested that the activity of EPG neurons represents the fly's moment-to-moment angular orientation, or heading angle, during navigation2,11. An animal's moment-to-moment heading angle, however, is not always aligned with its goal angle-that is, the allocentric direction in which it wishes to progress forward. We describe FC2 cells12, a second set of neurons in the Drosophila brain with activity that correlates with the fly's goal angle. Focal optogenetic activation of FC2 neurons induces flies to orient along experimenter-defined directions as they walk forward. EPG and FC2 neurons connect monosynaptically to a third neuronal class, PFL3 cells12,13. We found that individual PFL3 cells show conjunctive, spike-rate tuning to both the heading angle and the goal angle during goal-directed navigation. Informed by the anatomy and physiology of these three cell classes, we develop a model that explains how this circuit compares allocentric heading and goal angles to build an egocentric steering signal in the PFL3 output terminals. Quantitative analyses and optogenetic manipulations of PFL3 activity support the model. Finally, using a new navigational memory task, we show that flies expressing disruptors of synaptic transmission in subsets of PFL3 cells have a reduced ability to orient along arbitrary goal directions, with an effect size in quantitative accordance with the prediction of our model. The biological circuit described here reveals how two population-level allocentric signals are compared in the brain to produce an egocentric output signal that is appropriate for motor control.

Route retracing: way pointing and multiple vector memories in trail-following ants

Maintaining positional estimates of goal locations is a fundamental task for navigating animals. Diverse animal groups, including both vertebrates and invertebrates, can accomplish this through path integration. During path integration, navigators integrate movement changes, tracking both distance and direction, to generate a spatial estimate of their start location, or global vector, allowing efficient direct return travel without retracing the outbound route. In ants, path integration is accomplished through the coupling of pedometer and celestial compass estimates. Within path integration, it has been theorized navigators may use multiple vector memories for way pointing. However, in many instances, these navigators may instead be homing via view alignment. Here, we present evidence that trail-following ants can attend to segments of their global vector to retrace their non-straight pheromone trails, without the confound of familiar views. Veromessor pergandei foragers navigate to directionally distinct intermediate sites via path integration by orienting along separate legs of their inbound route at unfamiliar locations, indicating these changes are not triggered by familiar external cues, but by vector state. These findings contrast with path integration as a singular memory estimate in ants and underscore the system's ability to way point to intermediate goals along the inbound route via multiple vector memories, akin to trapline foraging in bees visiting multiple flower patches. We discuss how reliance on non-straight pheromone-marked trails may support attending to separate vectors to remain on the pheromone rather than attempting straight-line shortcuts back to the nest.

The neurobiology of the Monarch butterfly compass

Monarch butterflies (Danaus plexippus) have become a superb model system to unravel how the tiny insect brain controls an impressive navigation behavior, such as long-distance migration. Moreover, the ability to compare the neural substrate between migratory and nonmigratory Monarch butterflies provides us with an attractive model to specifically study how the insect brain is adapted for migration. We here review our current progress on the neural substrate of spatial orientation in Monarch butterflies and how their spectacular annual migration might be controlled by their brain. We also discuss open research questions, the answers to which will provide important missing pieces to obtain a full picture of insect migration - from the perception of orientation cues to the neural control of migration.

On the 'cognitive map debate' in insect navigation

In a historical account recently published in this journal Dhein argues that the current debate whether insects like bees and ants use cognitive maps (centralized map hypothesis) or other means of navigation (decentralized network hypothesis) largely reflects the classical debate between American experimental psychologists à la Tolman and German ethologists à la Lorenz, respectively. In this dichotomy we, i.e., the proponents of the network hypothesis, are inappropriately placed on the Lorenzian line. In particular, we argue that in contrast to Dhein's claim our concepts are not based on merely instinctive or peripheral modes of information processing. In general, on the one side our approaches have largely been motivated by the early biocybernetics way of thinking. On the other side they are deeply rooted in studies on the insect's behavioral ecology, i.e., in the ecological setting within which the navigational strategies have evolved and within which the animal now operates. Following such a bottom-up approach we are not "anti-cognitive map researchers" but argue that the results we have obtained in ants, and also the results of some decisive experiments in bees, can be explained and simulated without the need of invoking metric maps.

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.

Connectomic reconstruction predicts the functional organization of visual inputs to the navigation center of the Drosophila brain

Many animals, including humans, navigate their surroundings by visual input, yet we understand little about how visual information is transformed and integrated by the navigation system. In Drosophila melanogaster, compass neurons in the donut-shaped ellipsoid body of the central complex generate a sense of direction by integrating visual input from ring neurons, a part of the anterior visual pathway (AVP). Here, we densely reconstruct all neurons in the AVP using FlyWire, an AI-assisted tool for analyzing electron-microscopy data. The AVP comprises four neuropils, sequentially linked by three major classes of neurons: MeTu neurons, which connect the medulla in the optic lobe to the small unit of anterior optic tubercle (AOTUsu) in the central brain; TuBu neurons, which connect the anterior optic tubercle to the bulb neuropil; and ring neurons, which connect the bulb to the ellipsoid body. Based on neuronal morphologies, connectivity between different neural classes, and the locations of synapses, we identified non-overlapping channels originating from four types of MeTu neurons, which we further divided into ten subtypes based on the presynaptic connections in medulla and postsynaptic connections in AOTUsu. To gain an objective measure of the natural variation within the pathway, we quantified the differences between anterior visual pathways from both hemispheres and between two electron-microscopy datasets. Furthermore, we infer potential visual features and the visual area from which any given ring neuron receives input by combining the connectivity of the entire AVP, the MeTu neurons' dendritic fields, and presynaptic connectivity in the optic lobes. These results provide a strong foundation for understanding how distinct visual features are extracted and transformed across multiple processing stages to provide critical information for computing the fly's sense of direction.

Neuronal calcium spikes enable vector inversion in the Drosophila brain

A typical neuron signals to downstream cells when it is depolarized and firing sodium spikes. Some neurons, however, also fire calcium spikes when hyperpolarized. The function of such bidirectional signaling remains unclear in most circuits. Here we show how a neuron class that participates in vector computation in the fly central complex employs hyperpolarization-elicited calcium spikes to invert two-dimensional mathematical vectors. When cells switch from firing sodium to calcium spikes, this leads to a ~180° realignment between the vector encoded in the neuronal population and the fly's internal heading signal, thus inverting the vector. We show that the calcium spikes rely on the T-type calcium channel Ca-α1T, and argue, via analytical and experimental approaches, that these spikes enable vector computations in portions of angular space that would otherwise be inaccessible. These results reveal a seamless interaction between molecular, cellular and circuit properties for implementing vector math in the brain.

Neuroarchitecture of the central complex in the Madeira cockroach Rhyparobia maderae: Pontine and columnar neuronal cell types

Insects have evolved remarkable abilities to navigate over short distances and during long-range seasonal migrations. The central complex (CX) is a navigation center in the insect brain that controls spatial orientation and directed locomotion. It is composed of the protocerebral bridge (PB), the upper (CBU) and lower (CBL) division of the central body, and a pair of noduli. While most of its functional organization and involvement in head-direction coding has been obtained from work on flies, bees, and locusts that largely rely on vision for navigation, little contribution has been provided by work on nocturnal species. To close this gap, we have investigated the columnar organization of the CX in the cockroach Rhyparobia maderae. Rhyparobia maderae is a highly agile nocturnal insect that relies largely but not exclusively on antennal information for navigation. A particular feature of the cockroach CX is an organization of the CBU and CBL into interleaved series of eight and nine columns. Single-cell tracer injections combined with imaging and 3D analysis revealed five systems of pontine neurons connecting columns along the vertical and horizontal axis and 18 systems of columnar neurons with topographically organized projection patterns. Among these are six types of neurons with no correspondence in other species. Many neurons send processes into the anterior lip, a brain area highly reduced in bees and unknown in flies. While sharing many features with the CX in other species, the cockroach CX shows some unique attributes that may be related to the ecological niche of this insect.

Non-celestial polarization vision in arthropods

Most insects can detect the pattern of polarized light in the sky with the dorsal rim area in their compound eyes and use this visual information to navigate in their environment by means of 'celestial' polarization vision. 'Non-celestial polarization vision', in contrast, refers to the ability of arthropods to analyze polarized light by means of the 'main' retina, excluding the dorsal rim area. The ability of using the main retina for polarization vision has been attracting sporadic, but steady attention during the last decade. This special issue of the Journal of Comparative Physiology A presents recent developments with a collection of seven original research articles, addressing different aspects of non-celestial polarization vision in crustaceans and insects. The contributions cover different sources of linearly polarized light in nature, the underlying retinal and neural mechanisms of object detection using polarization vision and the behavioral responses of arthropods to polarized reflections from water.

Fine-grained descending control of steering in walking Drosophila

Locomotion involves rhythmic limb movement patterns that originate in circuits outside the brain. Purposeful locomotion requires descending commands from the brain, but we do not understand how these commands are structured. Here we investigate this issue, focusing on the control of steering in walking Drosophila. First, we describe different limb "gestures" associated with different steering maneuvers. Next, we identify a set of descending neurons whose activity predicts steering. Focusing on two descending cell types downstream from distinct brain networks, we show that they evoke specific limb gestures: one lengthens strides on the outside of a turn, while the other attenuates strides on the inside of a turn. Notably, a single descending neuron can have opposite effects during different locomotor rhythm phases, and we identify networks positioned to implement this phase-specific gating. Together, our results show how purposeful locomotion emerges from brain cells that drive specific, coordinated modulations of low-level patterns.

Multilevel visuomotor control of locomotion in Drosophila

Vision is critical for the control of locomotion, but the underlying neural mechanisms by which visuomotor circuits contribute to the movement of the body through space are yet not well understood. Locomotion engages multiple control systems, forming distinct interacting "control levels" driven by the activity of distributed and overlapping circuits. Therefore, a comprehensive understanding of the mechanisms underlying locomotion control requires the consideration of all control levels and their necessary coordination. Due to their small size and the wide availability of experimental tools, Drosophila has become an important model system to study this coordination. Traditionally, insect locomotion has been divided into studying either the biomechanics and local control of limbs, or navigation and course control. However, recent developments in tracking techniques, and physiological and genetic tools in Drosophila have prompted researchers to examine multilevel control coordination in flight and walking.

Honeybee as a food nutrition analysis model of neural development and gut microbiota

Research on the relationships between the gut microbiota and the neurophysiology and behavior of animals has grown exponentially in just a few years. Insect behavior may be controlled by molecular mechanisms that are partially homologous to those in mammals, and swarming insects may be suitable as experiment models in these types of investigations. All core gut bacteria in honeybees can be cultivated in vitro. Certain gut microflora of bees can be genetically engineered or sterilized and colonized. The bee gut bacteria model is established more rapidly and has a higher flux than other sterile animal models. It may help elucidate the pathogenesis of intestinal diseases and identify effective molecular therapeutic targets against them. In the present review, we focused on the contributions of the honeybee model in learning cognition and microbiome research. We explored the relationship between honeybee behavior and neurodevelopment and the factors determining the mechanisms by which the gut microbiota affects the host. In particular, we concentrated on the correlation between gut microbiota and brain development. Finally, we examined strategies for the effective use of simple animal models in animal cognition and microbiome research.

Neural representation of goal direction in the monarch butterfly brain

Neural processing of a desired moving direction requires the continuous comparison between the current heading and the goal direction. While the neural basis underlying the current heading is well-studied, the coding of the goal direction remains unclear in insects. Here, we used tetrode recordings in tethered flying monarch butterflies to unravel how a goal direction is represented in the insect brain. While recording, the butterflies maintained robust goal directions relative to a virtual sun. By resetting their goal directions, we found neurons whose spatial tuning was tightly linked to the goal directions. Importantly, their tuning was unaffected when the butterflies changed their heading after compass perturbations, showing that these neurons specifically encode the goal direction. Overall, we here discovered invertebrate goal-direction neurons that share functional similarities to goal-direction cells reported in mammals. Our results give insights into the evolutionarily conserved principles of goal-directed spatial orientation in animals.

Circuits for self-motion estimation and walking control in Drosophila

The brain's evolution and operation are inextricably linked to animal movement, and critical functions, such as motor control, spatial perception, and navigation, rely on precise knowledge of body movement. Such internal estimates of self-motion emerge from the integration of mechanosensory and visual feedback with motor-related signals. Thus, this internal representation likely depends on the activity of circuits distributed across the central nervous system. However, the circuits responsible for self-motion estimation, and the exact mechanisms by which motor-sensory coordination occurs within these circuits remain poorly understood. Recent technological advances have positioned Drosophila melanogaster as an advantageous model for investigating the emergence, maintenance, and utilization of self-motion representations during naturalistic walking behaviors. In this review, I will illustrate how the adult fly is providing insights into the fundamental problems of self-motion computations and walking control, which have relevance for all animals.

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.