How bumblebees coordinate path integration and body orientation at the start of their first learning flight.

The start of a bumblebee's first learning flight from its nest provides an opportunity to examine the bee's learning behaviour during its initial view of the nest's unfamiliar surroundings. Like many other hymenopterans, bumblebees store views of their nest surroundings while facing their nest. We found that a bumblebee's first fixation of the nest is a coordinated manoeuvre in which the insect faces the nest with its body oriented towards a particular visual feature within its surroundings. This conjunction of nest fixation and body orientation is preceded and reached by means of a translational scan during which the bee flies perpendicularly to its preferred body orientation. The utility of the coordinated manoeuvre is apparent during the bees' first return flight after foraging. Bees then adopt a similar preferred body orientation when close to the nest. How does a bee, unacquainted with its surroundings, know when it is facing its nest? A likely answer is through path integration, which gives bees continuously updated information about the current direction of their nest. Path integration also gives bees the possibility to fixate the nest when their body points in a desired direction. The three components of this coordinated manoeuvre are discussed in relation to current understanding of the central complex in the insect brain, noting that nest fixation is egocentric, whereas the preferred body orientation and flight direction that the bee adopts within the visual surroundings of the nest are geocentric.

Wood ants learn the magnetic direction of a route but express uncertainty because of competing directional cues.

Wood ants were trained indoors to follow a magnetically specified route that went from the centre of an arena to a drop of sucrose at the edge. The arena, placed in a white cylinder, was in the centre of a 3D coil system generating an inclined Earth-strength magnetic field in any horizontal direction. The specified direction was rotated between each trial. The ants' knowledge of the route was tested in trials without food. Tests given early in the day, before any training, show that ants remember the magnetic route direction overnight. During the first 2 s of a test, ants mostly faced in the specified direction, but thereafter were often misdirected, with a tendency to face briefly in the opposite direction. Uncertainty about the correct path to take may stem in part from competing directional cues linked to the room. In addition to facing along the route, there is evidence that ants develop magnetically directed home and food vectors dependent upon path integration. A second experiment asked whether ants can use magnetic information contextually. In contrast to honeybees given a similar task, ants failed this test. Overall, we conclude that magnetic directional cues can be sufficient for route learning.

DoPI: The Database of Pollinator Interactions.

Despite the importance of pollinating insects to natural environments and agriculture, there have been few attempts to unite the existing plant-pollinator interaction datasets into a single depository using a common format. Accordingly, we have created one of the world's first online, open-access, and searchable pollinator-plant interaction databases. DoPI (The Database of Pollinator Interactions) was built from a systematic review of the scientific literature and unpublished datasets requested from researchers and organizations. We collated records of interactions between British plant and insect flower-visitor species (or genera), together with associated metadata (date, location, habitat, source publication) when available. The dataset currently (December 2021) contains 101,539 records, detailing over 320,000 interactions. The number of interactions (i.e., the number of times a pairwise species interaction was recorded per occasion) varies considerably among records, averaging 3.6. These include records from 1888 pollinator species and 1241 plant species, totaling >17,000 pairwise species interactions. By combining a large volume of information in a single repository, DoPI can be used to answer fundamental ecological questions on the dynamics of pollination interactions in space and time, as well as applied questions in conservation practice. We hope this dynamic database will be a useful tool not only for researchers, but also for conservationists, funding agencies, governmental departments, beekeepers, agronomists, and gardeners. We request that this paper is cited when using the data in publications and individual studies when appropriate. Researchers and organizations are encouraged to add further data in the future. The database can be accessed at: https://www.dopi.org.uk/.

Learning with reinforcement prediction errors in a model of the Drosophila mushroom body.

Effective decision making in a changing environment demands that accurate predictions are learned about decision outcomes. In Drosophila, such learning is orchestrated in part by the mushroom body, where dopamine neurons signal reinforcing stimuli to modulate plasticity presynaptic to mushroom body output neurons. Building on previous mushroom body models, in which dopamine neurons signal absolute reinforcement, we propose instead that dopamine neurons signal reinforcement prediction errors by utilising feedback reinforcement predictions from output neurons. We formulate plasticity rules that minimise prediction errors, verify that output neurons learn accurate reinforcement predictions in simulations, and postulate connectivity that explains more physiological observations than an experimentally constrained model. The constrained and augmented models reproduce a broad range of conditioning and blocking experiments, and we demonstrate that the absence of blocking does not imply the absence of prediction error dependent learning. Our results provide five predictions that can be tested using established experimental methods.

Small and Large Bumblebees Invest Differently when Learning about Flowers.

Honeybees1 and bumblebees2 perform learning flights when leaving a newly discovered flower. During these flights, bees spend a portion of the time turning back to face the flower when they can memorize views of the flower and its surroundings. In honeybees, learning flights become longer when the reward offered by a flower is increased.3 We show here that bumblebees behave in a similar way, and we add that bumblebees face an artificial flower more when the concentration of the sucrose solution that the flower provides is higher. The surprising finding is that a bee's size determines what a bumblebee regards as a "low" or "high" concentration and so affects its learning behavior. The larger bees in a sample of foragers only enhance their flower facing when the sucrose concentration is in the upper range of the flowers that are naturally available to bees.4 In contrast, smaller bees invest the same effort in facing flowers whether the concentration is high or low, but their effort is less than that of larger bees. The way in which different-sized bees distribute their effort when learning about flowers parallels the foraging behavior of a colony. Large bumblebees5,6 are able to carry larger loads and explore further from the nest than smaller ones.7 Small ones with a smaller flight range and carrying capacity cannot afford to be as selective and so accept a wider range of flowers. VIDEO ABSTRACT.

Evolved Transistor Array Robot Controllers.

For the first time, a field programmable transistor array (FPTA) was used to evolve robot control circuits directly in analog hardware. Controllers were successfully incrementally evolved for a physical robot engaged in a series of visually guided behaviours, including finding a target in a complex environment where the goal was hidden from most locations. Circuits for recognising spoken commands were also evolved and these were used in conjunction with the controllers to enable voice control of the robot, triggering behavioural switching. Poor quality visual sensors were deliberately used to test the ability of evolved analog circuits to deal with noisy uncertain data in realtime. Visual features were coevolved with the controllers to automatically achieve dimensionality reduction and feature extraction and selection in an integrated way. An efficient new method was developed for simulating the robot in its visual environment. This allowed controllers to be evaluated in a simulation connected to the FPTA. The controllers then transferred seamlessly to the real world. The circuit replication issue was also addressed in experiments where circuits were evolved to be able to function correctly in multiple areas of the FPTA. A methodology was developed to analyse the evolved circuits which provided insights into their operation. Comparative experiments demonstrated the superior evolvability of the transistor array medium.

Encoding Temporal Regularities and Information Copying in Hippocampal Circuits.

Discriminating, extracting and encoding temporal regularities is a critical requirement in the brain, relevant to sensory-motor processing and learning. However, the cellular mechanisms responsible remain enigmatic; for example, whether such abilities require specific, elaborately organized neural networks or arise from more fundamental, inherent properties of neurons. Here, using multi-electrode array technology, and focusing on interval learning, we demonstrate that sparse reconstituted rat hippocampal neural circuits are intrinsically capable of encoding and storing sub-second-order time intervals for over an hour timescale, represented in changes in the spatial-temporal architecture of firing relationships among populations of neurons. This learning is accompanied by increases in mutual information and transfer entropy, formal measures related to information storage and flow. Moreover, temporal relationships derived from previously trained circuits can act as templates for copying intervals into untrained networks, suggesting the possibility of circuit-to-circuit information transfer. Our findings illustrate that dynamic encoding and stable copying of temporal relationships are fundamental properties of simple in vitro networks, with general significance for understanding elemental principles of information processing, storage and replication.

Simulating Soft-Bodied Swimmers with Particle-Based Physics.

In swimming virtual creatures, there is often a disparity between the level of detail in simulating a swimmer's body and that of the fluid it moves in. To address this disparity, we have developed a new approach to modeling swimming virtual creatures using pseudo-soft bodies and particle-based fluids, which has sufficient realism to investigate a larger range of body-environment interactions than are usually included. As this comes with increased computational costs, which may be severe, we have also developed a means of reducing the volume of fluid that must be simulated.

Walking together: behavioural signatures of psychological crowds.

Research in crowd psychology has demonstrated key differences between the behaviour of physical crowds where members are in the same place at the same time, and the collective behaviour of psychological crowds where the entire crowd perceive themselves to be part of the same group through a shared social identity. As yet, no research has investigated the behavioural effects that a shared social identity has on crowd movement at a pedestrian level. To investigate the direction and extent to which social identity influences the movement of crowds, 280 trajectories were tracked as participants walked in one of two conditions: (1) a psychological crowd primed to share a social identity; (2) a naturally occurring physical crowd. Behaviour was compared both within and between the conditions. In comparison to the physical crowd, members of the psychological crowd (i) walked slower, (ii) walked further, and (iii) maintained closer proximity. In addition, pedestrians who had to manoeuvre around the psychological crowd behaved differently to pedestrians who had to manoeuvre past the naturally occurring crowd. We conclude that the behavioural differences between physical and psychological crowds must be taken into account when considering crowd behaviour in event safety management and computer models of crowds.

Neural coding in the visual system of Drosophila melanogaster: How do small neural populations support visually guided behaviours?

All organisms wishing to survive and reproduce must be able to respond adaptively to a complex, changing world. Yet the computational power available is constrained by biology and evolution, favouring mechanisms that are parsimonious yet robust. Here we investigate the information carried in small populations of visually responsive neurons in Drosophila melanogaster. These so-called 'ring neurons', projecting to the ellipsoid body of the central complex, are reported to be necessary for complex visual tasks such as pattern recognition and visual navigation. Recently the receptive fields of these neurons have been mapped, allowing us to investigate how well they can support such behaviours. For instance, in a simulation of classic pattern discrimination experiments, we show that the pattern of output from the ring neurons matches observed fly behaviour. However, performance of the neurons (as with flies) is not perfect and can be easily improved with the addition of extra neurons, suggesting the neurons' receptive fields are not optimised for recognising abstract shapes, a conclusion which casts doubt on cognitive explanations of fly behaviour in pattern recognition assays. Using artificial neural networks, we then assess how easy it is to decode more general information about stimulus shape from the ring neuron population codes. We show that these neurons are well suited for encoding information about size, position and orientation, which are more relevant behavioural parameters for a fly than abstract pattern properties. This leads us to suggest that in order to understand the properties of neural systems, one must consider how perceptual circuits put information at the service of behaviour.

Vision for navigation: What can we learn from ants?

The visual systems of all animals are used to provide information that can guide behaviour. In some cases insects demonstrate particularly impressive visually-guided behaviour and then we might reasonably ask how the low-resolution vision and limited neural resources of insects are tuned to particular behavioural strategies. Such questions are of interest to both biologists and to engineers seeking to emulate insect-level performance with lightweight hardware. One behaviour that insects share with many animals is the use of learnt visual information for navigation. Desert ants, in particular, are expert visual navigators. Across their foraging life, ants can learn long idiosyncratic foraging routes. What's more, these routes are learnt quickly and the visual cues that define them can be implemented for guidance independently of other social or personal information. Here we review the style of visual navigation in solitary foraging ants and consider the physiological mechanisms that underpin it. Our perspective is to consider that robust navigation comes from the optimal interaction between behavioural strategy, visual mechanisms and neural hardware. We consider each of these in turn, highlighting the value of ant-like mechanisms in biomimetic endeavours.

Sleep and the heart: Interoceptive differences linked to poor experiential sleep quality in anxiety and depression.

Interoception is the sense through which internal bodily changes are signalled and perceived. Individual differences in interoception are linked to emotional style and vulnerability to affective disorders. Here we test how experiential sleep quality relates to dimensions of interoceptive ability. 180 adults (42 'non-clinical' individuals, 138 patients accessing mental health services) rated their quality of sleep before performing tests of cardiac interoception. Poor sleep quality was associated with lower measures of interoceptive performance accuracy, and higher self-report measures of interoceptive sensibility in individuals with diagnoses of depression and/or anxiety. Additionally, poor sleep quality was associated with impaired metacognitive interoceptive awareness in patients with diagnoses of depression (alone or with anxiety). Thus, poor sleep quality, a common early expression of psychological disorder, impacts cardiac interoceptive ability and experience across diagnoses. Sleep disruption can contribute to the expression of affective psychopathology through effects on perceptual and interpretative dimensions of bodily awareness.

Mind-wandering and alterations to default mode network connectivity when listening to naturalistic versus artificial sounds.

Naturalistic environments have been demonstrated to promote relaxation and wellbeing. We assess opposing theoretical accounts for these effects through investigation of autonomic arousal and alterations of activation and functional connectivity within the default mode network (DMN) of the brain while participants listened to sounds from artificial and natural environments. We found no evidence for increased DMN activity in the naturalistic compared to artificial or control condition, however, seed based functional connectivity showed a shift from anterior to posterior midline functional coupling in the naturalistic condition. These changes were accompanied by an increase in peak high frequency heart rate variability, indicating an increase in parasympathetic activity in the naturalistic condition in line with the Stress Recovery Theory of nature exposure. Changes in heart rate and the peak high frequency were correlated with baseline functional connectivity within the DMN and baseline parasympathetic tone respectively, highlighting the importance of individual neural and autonomic differences in the response to nature exposure. Our findings may help explain reported health benefits of exposure to natural environments, through identification of alterations to autonomic activity and functional coupling within the DMN when listening to naturalistic sounds.

Unsupervised Learning in an Ensemble of Spiking Neural Networks Mediated by ITDP.

We propose a biologically plausible architecture for unsupervised ensemble learning in a population of spiking neural network classifiers. A mixture of experts type organisation is shown to be effective, with the individual classifier outputs combined via a gating network whose operation is driven by input timing dependent plasticity (ITDP). The ITDP gating mechanism is based on recent experimental findings. An abstract, analytically tractable model of the ITDP driven ensemble architecture is derived from a logical model based on the probabilities of neural firing events. A detailed analysis of this model provides insights that allow it to be extended into a full, biologically plausible, computational implementation of the architecture which is demonstrated on a visual classification task. The extended model makes use of a style of spiking network, first introduced as a model of cortical microcircuits, that is capable of Bayesian inference, effectively performing expectation maximization. The unsupervised ensemble learning mechanism, based around such spiking expectation maximization (SEM) networks whose combined outputs are mediated by ITDP, is shown to perform the visual classification task well and to generalize to unseen data. The combined ensemble performance is significantly better than that of the individual classifiers, validating the ensemble architecture and learning mechanisms. The properties of the full model are analysed in the light of extensive experiments with the classification task, including an investigation into the influence of different input feature selection schemes and a comparison with a hierarchical STDP based ensemble architecture.

Active Shape Discrimination with Compliant Bodies as Reservoir Computers.

Compliant bodies with complex dynamics can be used both to simplify control problems and to lead to adaptive reflexive behavior when engaged with the environment in the sensorimotor loop. By revisiting an experiment introduced by Beer and replacing the continuous-time recurrent neural network therein with reservoir computing networks abstracted from compliant bodies, we demonstrate that adaptive behavior can be produced by an agent in which the body is the main computational locus. We show that bodies with complex dynamics are capable of integrating, storing, and processing information in meaningful and useful ways, and furthermore that with the addition of the simplest of nervous systems such bodies can generate behavior that could equally be described as reflexive or minimally cognitive.

VEGFR2 pY949 signalling regulates adherens junction integrity and metastatic spread.

The specific role of VEGFA-induced permeability and vascular leakage in physiology and pathology has remained unclear. Here we show that VEGFA-induced vascular leakage depends on signalling initiated via the VEGFR2 phosphosite Y949, regulating dynamic c-Src and VE-cadherin phosphorylation. Abolished Y949 signalling in the mouse mutant Vegfr2(Y949F/Y949F) leads to VEGFA-resistant endothelial adherens junctions and a block in molecular extravasation. Vessels in Vegfr2(Y949F/Y949F) mice remain sensitive to inflammatory cytokines, and vascular morphology, blood pressure and flow parameters are normal. Tumour-bearing Vegfr2(Y949F/Y949F) mice display reduced vascular leakage and oedema, improved response to chemotherapy and, importantly, reduced metastatic spread. The inflammatory infiltration in the tumour micro-environment is unaffected. Blocking VEGFA-induced disassembly of endothelial junctions, thereby suppressing tumour oedema and metastatic spread, may be preferable to full vascular suppression in the treatment of certain cancer forms.

How do field of view and resolution affect the information content of panoramic scenes for visual navigation? A computational investigation.

The visual systems of animals have to provide information to guide behaviour and the informational requirements of an animal's behavioural repertoire are often reflected in its sensory system. For insects, this is often evident in the optical array of the compound eye. One behaviour that insects share with many animals is the use of learnt visual information for navigation. As ants are expert visual navigators it may be that their vision is optimised for navigation. Here we take a computational approach in asking how the details of the optical array influence the informational content of scenes used in simple view matching strategies for orientation. We find that robust orientation is best achieved with low-resolution visual information and a large field of view, similar to the optical properties seen for many ant species. A lower resolution allows for a trade-off between specificity and generalisation for stored views. Additionally, our simulations show that orientation performance increases if different portions of the visual field are considered as discrete visual sensors, each giving an independent directional estimate. This suggests that ants might benefit by processing information from their two eyes independently.

From Mindless Masses to Small Groups: Conceptualizing Collective Behavior in Crowd Modeling.

Computer simulations are increasingly used to monitor and predict behavior at large crowd events, such as mass gatherings, festivals and evacuations. We critically examine the crowd modeling literature and call for future simulations of crowd behavior to be based more closely on findings from current social psychological research. A systematic review was conducted on the crowd modeling literature (N = 140 articles) to identify the assumptions about crowd behavior that modelers use in their simulations. Articles were coded according to the way in which crowd structure was modeled. It was found that 2 broad types are used: mass approaches and small group approaches. However, neither the mass nor the small group approaches can accurately simulate the large collective behavior that has been found in extensive empirical research on crowd events. We argue that to model crowd behavior realistically, simulations must use methods which allow crowd members to identify with each other, as suggested by self-categorization theory.

Acquisition and expression of memories of distance and direction in navigating wood ants.

Wood ants, like other central place foragers, rely on route memories to guide them to and from a reliable food source. They use visual memories of the surrounding scene and probably compass information to control their direction. Do they also remember the length of their route and do they link memories of direction and distance? To answer these questions, we trained wood ant (Formica rufa) foragers in a channel to perform either a single short foraging route or two foraging routes in opposite directions. By shifting the starting position of the route within the channel, but keeping the direction and distance fixed, we tried to ensure that the ants would rely upon vector memories rather than visual memories to decide when to stop. The homeward memories that the ants formed were revealed by placing fed or unfed ants directly into a channel and assessing the direction and distance that they walked without prior performance of the food-ward leg of the journey. This procedure prevented the distance and direction walked being affected by a home vector derived from path integration. Ants that were unfed walked in the feeder direction. Fed ants walked in the opposite direction for a distance related to the separation between start and feeder. Vector memories of a return route can thus be primed by the ants' feeding state and expressed even when the ants have not performed the food-ward route. Tests on ants that have acquired two routes indicate that memories of the direction and distance of the return routes are linked, suggesting that they may be encoded by a common neural population within the ant brain.

Navigation-specific neural coding in the visual system of Drosophila.

Drosophila melanogaster are a good system in which to understand the minimal requirements for widespread visually guided behaviours such as navigation, due to their small brains (adults possess only 100,000 neurons) and the availability of neurogenetic techniques which allow the identification of task-specific cell types. Recently published data describe the receptive fields for two classes of visually responsive neurons (R2 and R3/R4d ring neurons in the central complex) that are essential for visual tasks such as orientation memory for salient objects and simple pattern discriminations. What is interesting is that these cells have very large receptive fields and are very small in number, suggesting that each sub-population of cells might be a bottleneck in the processing of visual information for a specific behaviour, as each subset of cells effectively condenses information from approximately 3000 visual receptors in the eye, to fewer than 50 neurons in total. It has recently been shown how R1 ring neurons, which receive input from the same areas as the R2 and R3/R4d cells, are necessary for place learning in Drosophila. However, how R1 neurons enable place learning is unknown. By examining the information provided by different populations of hypothetical visual neurons in simulations of experimental arenas, we show that neurons with ring neuron-like receptive fields are sufficient for defining a location visually. In this way we provide a link between the type of information conveyed by ring neurons and the behaviour they support.