The head direction circuit of two insect species

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.

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.

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.

Differential Neuroanatomical, Neurochemical, and Behavioral Impacts of Early-Age Isolation in a Eusocial Insect

Social experience early in life appears to be necessary for the development of species-typical behavior. Although isolation during critical periods of maturation has been shown to impact behavior by altering gene expression and brain development in invertebrates and vertebrates, workers of some ant species appear resilient to social deprivation and other neurobiological challenges that occur during senescence or due to loss of sensory input. It is unclear if and to what degree neuroanatomy, neurochemistry, and behavior will show deficiencies if social experience in the early adult life of worker ants is compromised. We reared newly-eclosed adult workers of Camponotus floridanus under conditions of social isolation for 2 to 53 days, quantified brain compartment volumes, recorded biogenic amine levels in individual brains, and evaluated movement and behavioral performance to compare the neuroanatomy, neurochemistry, brood-care behavior, and foraging (predatory behavior) of isolated workers with that of workers experiencing natural social contact after adult eclosion. We found that the volume of the antennal lobe, which processes olfactory inputs, was significantly reduced in workers isolated for an average of 40 days, whereas the size of the mushroom bodies, centers of higher-order sensory processing, increased after eclosion and was not significantly different from controls. Titers of the neuromodulators serotonin, dopamine, and octopamine remained stable and were not significantly different in isolation treatments and controls. Brood care, predation, and overall movement were reduced in workers lacking social contact early in life. These results suggest that the behavioral development of isolated workers of C. floridanus is specifically impacted by a reduction in the size of the antennal lobe. Task performance and locomotor ability therefore appear to be sensitive to a loss of social contact through a reduction of olfactory processing ability rather than change in the size of the mushroom bodies, which serve important functions in learning and memory, or the central complex, which controls movement.

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.

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.

Ring Attractors as the Basis of a Biomimetic Navigation System

The ability to navigate effectively in a rich and complex world is crucial for the survival of all animals. Specialist neural structures have evolved that are implicated in facilitating this ability, one such structure being the ring attractor network. In this study, we model a trio of Spiking Neural Network (SNN) ring attractors as part of a bio-inspired navigation system to maintain an internal estimate of planar translation of an artificial agent. This estimate is dynamically calibrated using a memory recall system of landmark-free allotheic multisensory experiences. We demonstrate that the SNN-based ring attractor system can accurately model motion through 2D space by integrating ideothetic velocity information and use recalled allothetic experiences as a positive corrective mechanism. This SNN based navigation system has potential for use in mobile robotics applications where power supply is limited and external sensory information is intermittent or unreliable.

A model of cue integration as vector summation in the insect brain

Ball-rolling dung beetles are known to integrate multiple cues in order to facilitate their straight-line orientation behaviour. Recent work has suggested that orientation cues are integrated according to a vector sum, that is, compass cues are represented by vectors and summed to give a combined orientation estimate. Further, cue weight (vector magnitude) appears to be set according to cue reliability. This is consistent with the popular Bayesian view of cue integration: cues are integrated to reduce or minimize an agent's uncertainty about the external world. Integration of orientation cues is believed to occur at the input to the insect central complex. Here, we demonstrate that a model of the head direction circuit of the central complex, including plasticity in input synapses, can act as a substrate for cue integration as vector summation. Further, we show that cue influence is not necessarily driven by cue reliability. Finally, we present a dung beetle behavioural experiment which, in combination with simulation, strongly suggests that these beetles do not weight cues according to reliability. We suggest an alternative strategy whereby cues are weighted according to relative contrast, which can also explain previous results.

Neuroscience: Fish and fly headed in the same direction

Using functional imaging and neural circuit reconstructions, a recent study reveals head direction neurons in the anterior hindbrain of zebrafish that resemble insect head-direction cells to a surprising degree.

A leaky integrate-and-fire computational model based on the connectome of the entire adult Drosophila brain reveals insights into sensorimotor processing

The forthcoming assembly of the adult Drosophila melanogaster central brain connectome, containing over 125,000 neurons and 50 million synaptic connections, provides a template for examining sensory processing throughout the brain. Here, we create a leaky integrate-and-fire computational model of the entire Drosophila brain, based on neural connectivity and neurotransmitter identity, to study circuit properties of feeding and grooming behaviors. We show that activation of sugar-sensing or water-sensing gustatory neurons in the computational model accurately predicts neurons that respond to tastes and are required for feeding initiation. Computational activation of neurons in the feeding region of the Drosophila brain predicts those that elicit motor neuron firing, a testable hypothesis that we validate by optogenetic activation and behavioral studies. Moreover, computational activation of different classes of gustatory neurons makes accurate predictions of how multiple taste modalities interact, providing circuit-level insight into aversive and appetitive taste processing. Our computational model predicts that the sugar and water pathways form a partially shared appetitive feeding initiation pathway, which our calcium imaging and behavioral experiments confirm. Additionally, we applied this model to mechanosensory circuits and found that computational activation of mechanosensory neurons predicts activation of a small set of neurons comprising the antennal grooming circuit that do not overlap with gustatory circuits, and accurately describes the circuit response upon activation of different mechanosensory subtypes. Our results demonstrate that modeling brain circuits purely from connectivity and predicted neurotransmitter identity generates experimentally testable hypotheses and can accurately describe complete sensorimotor transformations.

The influence of stimulus history on directional coding in the monarch butterfly brain

The central complex is a brain region in the insect brain that houses a neural network specialized to encode directional information. Directional coding has traditionally been investigated with compass cues that revolve in full rotations and at constant angular velocities around the insect's head. However, these stimulus conditions do not fully simulate an insect's sensory perception of compass cues during navigation. In nature, an insect flight is characterized by abrupt changes in moving direction as well as constant changes in velocity. The influence of such varying cue dynamics on compass coding remains unclear. We performed long-term tetrode recordings from the brain of monarch butterflies to study how central complex neurons respond to different stimulus velocities and directions. As these butterflies derive directional information from the sun during migration, we measured the neural response to a virtual sun. The virtual sun was either presented as a spot that appeared at random angular positions or was rotated around the butterfly at different angular velocities and directions. By specifically manipulating the stimulus velocity and trajectory, we dissociated the influence of angular velocity and direction on compass coding. While the angular velocity substantially affected the tuning directedness, the stimulus trajectory influenced the shape of the angular tuning curve. Taken together, our results suggest that the central complex flexibly adjusts its directional coding to the current stimulus dynamics ensuring a precise compass even under highly demanding conditions such as during rapid flight maneuvers.

Lineages to circuits: the developmental and evolutionary architecture of information channels into the central complex

The representation and integration of internal and external cues is crucial for any organism to execute appropriate behaviors. In insects, a highly conserved region of the brain, the central complex (CX), functions in the representation of spatial information and behavioral states, as well as the transformation of this information into desired navigational commands. How does this relatively invariant structure enable the incorporation of information from the diversity of anatomical, behavioral, and ecological niches occupied by insects? Here, we examine the input channels to the CX in the context of their development and evolution. Insect brains develop from ~ 100 neuroblasts per hemisphere that divide systematically to form "lineages" of sister neurons, that project to their target neuropils along anatomically characteristic tracts. Overlaying this developmental tract information onto the recently generated Drosophila "hemibrain" connectome and integrating this information with the anatomical and physiological recording of neurons in other species, we observe neuropil and lineage-specific innervation, connectivity, and activity profiles in CX input channels. We posit that the proliferative potential of neuroblasts and the lineage-based architecture of information channels enable the modification of neural networks across existing, novel, and deprecated modalities in a species-specific manner, thus forming the substrate for the evolution and diversification of insect navigational circuits.

Global inhibition in head-direction neural circuits: a systematic comparison between connectome-based spiking neural circuit models

The recent discovery of the head-direction (HD) system in fruit flies has provided unprecedented insights into the neural mechanisms of spatial orientation. Despite the progress, the neural substance of global inhibition, an essential component of the HD circuits, remains controversial. Some studies suggested that the ring neurons provide global inhibition, while others suggested the Δ7 neurons. In the present study, we provide evaluations from the theoretical perspective by performing systematic analyses on the computational models based on the ring-neuron (R models) and Δ7-neurons (Delta models) hypotheses with modifications according to the latest connectomic data. We conducted four tests: robustness, persistency, speed, and dynamical characteristics. We discovered that the two models led to a comparable performance in general, but each excelled in different tests. The R Models were more robust, while the Delta models were better in the persistency test. We also tested a hybrid model that combines both inhibitory mechanisms. While the performances of the R and Delta models in each test are highly parameter-dependent, the Hybrid model performed well in all tests with the same set of parameters. Our results suggest the possibility of combined inhibitory mechanisms in the HD circuits of fruit flies.

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.

A cortico-collicular circuit for orienting to shelter during escape

When faced with predatory threats, escape towards shelter is an adaptive action that offers long-term protection against the attacker. Animals rely on knowledge of safe locations in the environment to instinctively execute rapid shelter-directed escape actions1,2. Although previous work has identified neural mechanisms of escape initiation3,4, it is not known how the escape circuit incorporates spatial information to execute rapid flights along the most efficient route to shelter. Here we show that the mouse retrosplenial cortex (RSP) and superior colliculus (SC) form a circuit that encodes the shelter-direction vector and is specifically required for accurately orienting to shelter during escape. Shelter direction is encoded in RSP and SC neurons in egocentric coordinates and SC shelter-direction tuning depends on RSP activity. Inactivation of the RSP-SC pathway disrupts the orientation to shelter and causes escapes away from the optimal shelter-directed route, but does not lead to generic deficits in orientation or spatial navigation. We find that the RSP and SC are monosynaptically connected and form a feedforward lateral inhibition microcircuit that strongly drives the inhibitory collicular network because of higher RSP input convergence and synaptic integration efficiency in inhibitory SC neurons. This results in broad shelter-direction tuning in inhibitory SC neurons and sharply tuned excitatory SC neurons. These findings are recapitulated by a biologically constrained spiking network model in which RSP input to the local SC recurrent ring architecture generates a circular shelter-direction map. We propose that this RSP-SC circuit might be specialized for generating collicular representations of memorized spatial goals that are readily accessible to the motor system during escape, or more broadly, during navigation when the goal must be reached as fast as possible.

Integration of optic flow into the sky compass network in the brain of the desert locust

Flexible orientation through any environment requires a sense of current relative heading that is updated based on self-motion. Global external cues originating from the sky or the earth's magnetic field and local cues provide a reference frame for the sense of direction. Locally, optic flow may inform about turning maneuvers, travel speed and covered distance. The central complex in the insect brain is associated with orientation behavior and largely acts as a navigation center. Visual information from global celestial cues and local landmarks are integrated in the central complex to form an internal representation of current heading. However, it is less clear how optic flow is integrated into the central-complex network. We recorded intracellularly from neurons in the locust central complex while presenting lateral grating patterns that simulated translational and rotational motion to identify these sites of integration. Certain types of central-complex neurons were sensitive to optic-flow stimulation independent of the type and direction of simulated motion. Columnar neurons innervating the noduli, paired central-complex substructures, were tuned to the direction of simulated horizontal turns. Modeling the connectivity of these neurons with a system of proposed compass neurons can account for rotation-direction specific shifts in the activity profile in the central complex corresponding to turn direction. Our model is similar but not identical to the mechanisms proposed for angular velocity integration in the navigation compass of the fly Drosophila.

The sky compass network in the brain of the desert locust

Many arthropods and vertebrates use celestial signals such as the position of the sun during the day or stars at night as compass cues for spatial orientation. The neural network underlying sky compass coding in the brain has been studied in great detail in the desert locust Schistocerca gregaria. These insects perform long-range migrations in Northern Africa and the Middle East following seasonal changes in rainfall. Highly specialized photoreceptors in a dorsal rim area of their compound eyes are sensitive to the polarization of the sky, generated by scattered sunlight. These signals are combined with direct information on the sun position in the optic lobe and anterior optic tubercle and converge from both eyes in a midline crossing brain structure, the central complex. Here, head direction coding is achieved by a compass-like arrangement of columns signaling solar azimuth through a 360° range of space by combining direct brightness cues from the sun with polarization cues matching the polarization pattern of the sky. Other directional cues derived from wind direction and internal self-rotation input are likely integrated. Signals are transmitted as coherent steering commands to descending neurons for directional control of locomotion and flight.

3D-atlas of the brain of the cockroach Rhyparobia maderae

The Madeira cockroach Rhyparobia maderae is a nocturnal insect and a prominent model organism for the study of circadian rhythms. Its master circadian clock, controlling circadian locomotor activity and sleep-wake cycles, is located in the accessory medulla of the optic lobe. For a better understanding of brain regions controlled by the circadian clock and brain organization of this insect in general, we created a three-dimensional (3D) reconstruction of all neuropils of the cerebral ganglia based on anti-synapsin and anti-γ-aminobutyric acid immunolabeling of whole mount brains. Forty-nine major neuropils were identified and three-dimensionally reconstructed. Single-cell dye fills complement the data and provide evidence for distinct subdivisions of certain brain areas. Most neuropils defined in the fruit fly Drosophila melanogaster could be distinguished in the cockroach as well. However, some neuropils identified in the fruit fly do not exist as distinct entities in the cockroach while others are lacking in the fruit fly. In addition to neuropils, major fiber systems, tracts, and commissures were reconstructed and served as important landmarks separating brain areas. Being a nocturnal insect, R. maderae is an important new species to the growing collection of 3D insect brain atlases and only the second hemimetabolous insect, for which a detailed 3D brain atlas is available. This atlas will be highly valuable for an evolutionary comparison of insect brain organization and will greatly facilitate addressing brain areas that are supervised by the circadian clock.

Myoinhibitory peptides in the central complex of the locust Schistocerca gregaria and colocalization with locustatachykinin-related peptides

The central complex in the brain of insects provides a neural network for sensorimotor processing that is essential for spatial navigation and locomotion and plays a role in sleep control. Studies on the neurochemical architecture of the central complex have been performed especially in the fruit fly Drosophila melangoaster and the desert locust, Schistocerca gregaria. In several insect species, myoinhibitory peptides (MIPs) are involved in circadian control and sleep-wake regulation. To identify neurons that might underlie these functions, we investigated the distribution of MIPs in the central complex of the locust. In silico transcript analysis suggests the presence of eight different MIPs in the desert locust. Through immunolabeling, we identified five systems of central-complex neurons that express MIP-like peptides. Two systems constitute columnar neurons of the protocerebral bridge and the lower division of the central body, while the other three systems are columnar neurons (two systems) and tangential neurons (one system) of the upper division of the central body. The innervation pattern and cell count of two systems of columnar neurons revealed the existence of 18 instead of 16 columns of the protocerebral bridge. Immunostaining of preparations containing intracellularly stained single cells allowed us to further specify subtypes of labeled columnar neurons. Double-label experiments showed that three systems of MIP-immunostained columnar neurons are also locustatachykinin-immunoreactive. No colocalization was found with serotonin immunostaining. The data provide novel insights into the architecture of the locust central complex and suggest that MIPs play a prominent role within the central-complex network.

Online learning for orientation estimation during translation in an insect ring attractor network

Insect neural systems are a promising source of inspiration for new navigation algorithms, especially on low size, weight, and power platforms. There have been unprecedented recent neuroscience breakthroughs with Drosophila in behavioral and neural imaging experiments as well as the mapping of detailed connectivity of neural structures. General mechanisms for learning orientation in the central complex (CX) of Drosophila have been investigated previously; however, it is unclear how these underlying mechanisms extend to cases where there is translation through an environment (beyond only rotation), which is critical for navigation in robotic systems. Here, we develop a CX neural connectivity-constrained model that performs sensor fusion, as well as unsupervised learning of visual features for path integration; we demonstrate the viability of this circuit for use in robotic systems in simulated and physical environments. Furthermore, we propose a theoretical understanding of how distributed online unsupervised network weight modification can be leveraged for learning in a trajectory through an environment by minimizing orientation estimation error. Overall, our results may enable a new class of CX-derived low power robotic navigation algorithms and lead to testable predictions to inform future neuroscience experiments.

Performance of polarization-sensitive neurons of the locust central complex at different degrees of polarization

The polarization pattern of the sky is exploited by many insects for spatial orientation and navigation. It derives from Rayleigh scattering in the atmosphere and depends directly on the position of the sun. In the insect brain, the central complex (CX) houses neurons tuned to the angle of polarization (AoP), that together constitute an internal compass for celestial navigation. Polarized light is not only characterized by the AoP, but also by the degree of polarization (DoP), which can be highly variable, depending on sky conditions. Under a clear sky, the DoP of polarized sky light may reach up to 0.75 but is usually much lower especially when light is scattered by clouds or haze. To investigate how the polarization-processing network of the CX copes with low DoPs, we recorded intracellularly from neurons of the locust CX at different stages of processing, while stimulating with light of different DoPs. Significant responses to polarized light occurred down to DoPs of 0.05 indicating reliable coding of the AoP even at unfavorable sky conditions. Moreover, we found that the activity of neurons at the CX input stage may be strongly influenced by nearly unpolarized light, while the activity of downstream neurons appears less affected.

Behavioral performance and division of labor influence brain mosaicism in the leafcutter ant Atta cephalotes

Brain evolution is hypothesized to be driven by behavioral selection on neuroarchitecture. We developed a novel metric of relative neuroanatomical investments involved in performing tasks varying in sensorimotor and processing demands across polymorphic task-specialized workers of the leafcutter ant Atta cephalotes and quantified brain size and structure to examine their correlation with our computational approximations. Investment in multisensory and motor integration for task performance was estimated to be greatest for media workers, whose highly diverse repertoire includes leaf-quality discrimination and leaf-harvesting tasks that likely involve demanding sensory and motor processes. Confocal imaging revealed that absolute brain volume increased with worker size and functionally specialized compartmental scaling differed among workers. The mushroom bodies, centers of sensory integration and learning and memory, and the antennal lobes, olfactory input sites, were larger in medias than in minims (gardeners) and significantly larger than in majors ("soldiers"), both of which had lower scores for involvement of olfactory processing in the performance of their characteristic tasks. Minims had a proportionally larger central complex compared to other workers. These results support the hypothesis that variation in task performance influences selection for mosaic brain structure, the independent evolution of proportions of the brain composed of different neuropils.

Flight-induced compass representation in the monarch butterfly heading network

For navigation, animals use a robust internal compass. Compass navigation is crucial for long-distance migrating animals like monarch butterflies, which use the sun to navigate over 4,000 km to their overwintering sites every fall. Sun-compass neurons of the central complex have only been recorded in immobile butterflies, and experimental evidence for encoding the animal's heading in these neurons is still missing. Although the activity of central-complex neurons exhibits a locomotor-dependent modulation in many insects, the function of such modulations remains unexplored. Here, we developed tetrode recordings from tethered flying monarch butterflies to reveal how flight modulates heading representation. We found that, during flight, heading-direction neurons change their tuning, transforming the central-complex network to function as a global compass. This compass is characterized by the dominance of processing steering feedback and allows for robust heading representation even under unreliable visual scenarios, an ideal strategy for maintaining a migratory heading over enormous distances.

A connectome of the Drosophila central complex reveals network motifs suitable for flexible navigation and context-dependent action selection

Flexible behaviors over long timescales are thought to engage recurrent neural networks in deep brain regions, which are experimentally challenging to study. In insects, recurrent circuit dynamics in a brain region called the central complex (CX) enable directed locomotion, sleep, and context- and experience-dependent spatial navigation. We describe the first complete electron-microscopy-based connectome of the Drosophila CX, including all its neurons and circuits at synaptic resolution. We identified new CX neuron types, novel sensory and motor pathways, and network motifs that likely enable the CX to extract the fly's head-direction, maintain it with attractor dynamics, and combine it with other sensorimotor information to perform vector-based navigational computations. We also identified numerous pathways that may facilitate the selection of CX-driven behavioral patterns by context and internal state. The CX connectome provides a comprehensive blueprint necessary for a detailed understanding of network dynamics underlying sleep, flexible navigation, and state-dependent action selection.

A projectome of the bumblebee central complex

Insects have evolved diverse and remarkable strategies for navigating in various ecologies all over the world. Regardless of species, insects share the presence of a group of morphologically conserved neuropils known collectively as the central complex (CX). The CX is a navigational center, involved in sensory integration and coordinated motor activity. Despite the fact that our understanding of navigational behavior comes predominantly from ants and bees, most of what we know about the underlying neural circuitry of such behavior comes from work in fruit flies. Here, we aim to close this gap, by providing the first comprehensive map of all major columnar neurons and their projection patterns in the CX of a bee. We find numerous components of the circuit that appear to be highly conserved between the fly and the bee, but also highlight several key differences which are likely to have important functional ramifications.

A model of head direction and landmark coding in complex environments

Environmental information is required to stabilize estimates of head direction (HD) based on angular path integration. However, it is unclear how this happens in real-world (visually complex) environments. We present a computational model of how visual feedback can stabilize HD information in environments that contain multiple cues of varying stability and directional specificity. We show how combinations of feature-specific visual inputs can generate a stable unimodal landmark bearing signal, even in the presence of multiple cues and ambiguous directional specificity. This signal is associated with the retrosplenial HD signal (inherited from thalamic HD cells) and conveys feedback to the subcortical HD circuitry. The model predicts neurons with a unimodal encoding of the egocentric orientation of the array of landmarks, rather than any one particular landmark. The relationship between these abstract landmark bearing neurons and head direction cells is reminiscent of the relationship between place cells and grid cells. Their unimodal encoding is formed from visual inputs via a modified version of Oja's Subspace Algorithm. The rule allows the landmark bearing signal to disconnect from directionally unstable or ephemeral cues, incorporate newly added stable cues, support orientation across many different environments (high memory capacity), and is consistent with recent empirical findings on bidirectional HD firing reported in the retrosplenial cortex. Our account of visual feedback for HD stabilization provides a novel perspective on neural mechanisms of spatial navigation within richer sensory environments, and makes experimentally testable predictions.

A unified mechanism for innate and learned visual landmark guidance in the insect central complex

Insects can navigate efficiently in both novel and familiar environments, and this requires flexiblity in how they are guided by sensory cues. A prominent landmark, for example, can elicit strong innate behaviours (attraction or menotaxis) but can also be used, after learning, as a specific directional cue as part of a navigation memory. However, the mechanisms that allow both pathways to co-exist, interact or override each other are largely unknown. Here we propose a model for the behavioural integration of innate and learned guidance based on the neuroanatomy of the central complex (CX), adapted to control landmark guided behaviours. We consider a reward signal provided either by an innate attraction to landmarks or a long-term visual memory in the mushroom bodies (MB) that modulates the formation of a local vector memory in the CX. Using an operant strategy for a simulated agent exploring a simple world containing a single visual cue, we show how the generated short-term memory can support both innate and learned steering behaviour. In addition, we show how this architecture is consistent with the observed effects of unilateral MB lesions in ants that cause a reversion to innate behaviour. We suggest the formation of a directional memory in the CX can be interpreted as transforming rewarding (positive or negative) sensory signals into a mapping of the environment that describes the geometrical attractiveness (or repulsion). We discuss how this scheme might represent an ideal way to combine multisensory information gathered during the exploration of an environment and support optimal cue integration.

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.

Tyrosine hydroxylase immunostaining in the central complex of dicondylian insects

Dopamine acts as a neurohormone and neurotransmitter in the insect nervous system and controls a variety of physiological processes. Dopaminergic neurons also innervate the central complex (CX), a multisensory center of the insect brain involved in sky compass navigation, goal-directed locomotion and sleep control. To infer a possible influence of evolutionary history and lifestyle on the neurochemical architecture of the CX, we have studied the distribution of neurons immunoreactive to tyrosine hydroxylase (TH), the rate-limiting enzyme in dopamine biosynthesis. Analysis of representatives from 12 insect orders ranging from firebrats to flies revealed high conservation of immunolabeled neurons. One type of TH-immunoreactive neuron was found in all species studied. The neurons have somata in the pars intercerebralis, arborizations in the lateral accessory lobes, and axonal ramifications in the central body and noduli. In all pterygote species, a second type of tangential neuron of the upper division of the central body was TH-immunoreactive. The neurons have cell bodies near the calyces and arborizations in the superior protocerebrum. Both types of neuron showed species-specific variations in cell number and in the innervated areas outside and inside the CX. Additional neurons were found in only two taxa: one type of columnar neuron showed TH immunostaining in the water strider Gerris lacustris, but not in other Heteroptera, and a tritocerebral neuron innervating the protocerebral bridge was immunolabeled in Diptera. The data show largely taxon-specific variations of a common ground pattern of putatively dopaminergic neurons that may be commonly involved in state-dependent modulation of CX function.

A visual pathway for skylight polarization processing in Drosophila

Many insects use patterns of polarized light in the sky to orient and navigate. Here, we functionally characterize neural circuitry in the fruit fly, Drosophila melanogaster, that conveys polarized light signals from the eye to the central complex, a brain region essential for the fly's sense of direction. Neurons tuned to the angle of polarization of ultraviolet light are found throughout the anterior visual pathway, connecting the optic lobes with the central complex via the anterior optic tubercle and bulb, in a homologous organization to the 'sky compass' pathways described in other insects. We detail how a consistent, map-like organization of neural tunings in the peripheral visual system is transformed into a reduced representation suited to flexible processing in the central brain. This study identifies computational motifs of the transformation, enabling mechanistic comparisons of multisensory integration and central processing for navigation in the brains of insects.

Reverse Engineering and Robotics as Tools for Analyzing Neural Circuits

Understanding neuronal circuits that have evolved over millions of years to control adaptive behavior may provide us with alternative solutions to problems in robotics. Recently developed genetic tools allow us to study the connectivity and function of the insect nervous system at the single neuron level. However, neuronal circuits are complex, so the question remains, can we unravel the complex neuronal connectivity to understand the principles of the computations it embodies? Here, I illustrate the plausibility of incorporating reverse engineering to analyze part of the central complex, an insect brain structure essential for navigation behaviors such as maintaining a specific compass heading and path integration. I demonstrate that the combination of reverse engineering with simulations allows the study of both the structure and function of the underlying circuit, an approach that augments our understanding of both the computation performed by the neuronal circuit and the role of its components.

The neuroecology of insect-plant interactions: the importance of physiological state and sensory integration

Natural behaviorally important stimuli are combinations of cues that are integrated by the nervous system to elicit behavior. Nonetheless, these cues dynamically change in time and space. In turn, the animal's internal state can cause changes in the encoding and representation of these stimuli. Despite abundant behavioral examples, links between the neural bases of sensory integration and the internal state-dependency of these responses remains an active study area. Recent studies in different insect models have provided new insights into how plasticity and the insect's internal state may influence odor representation. These studies show that complex stimuli are represented in unique percepts that are different from their sensory channels and that the representations may be modulated by physiological state.