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. Bumblebees like many other bees, wasps and ants learn views of their nest surroundings while they face their nest. We find 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 significance of the coordinated manoeuvre is apparent during return flights after foraging. Bees then adopt a similar preferred body-orientation when they are close to the nest. How does a bee, unacquainted with its surroundings, know when it is facing its nest? A likely answer is path integration which gives bees continuously updated information about the current direction of their nest. Path integration also enables bees to fixate the nest when the body points in the appropriate 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 adopting a preferred body orientation and flight direction within the visual surroundings of the nest are geocentric.

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 DoPI (The Database of Pollinator Interactions) was built from a systematic review of the scientific literature and unpublished datasets requested from researchers and organizations.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.

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.

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.

Parsimony versus Reductionism: How Can Crowd Psychology be Introduced into Computer Simulation?

Computer simulations are increasingly being used to monitor and predict the movement behavior of crowds. This can enhance crowd safety at large events and transport hubs, and increase efficiency such as capacity utilization in public transport systems. However, the models used are mainly based on video observations, not an understanding of human decision making. Theories of crowd psychology can elucidate the factors underpinning collective behavior in human crowds. Yet, in contrast to psychology, computer science must rely upon mathematical formulations in order to implement algorithms and keep models manageable. Here, we address the problems and possible solutions encountered when incorporating social psychological theories of collective behavior in computer modeling. We identify that one primary issue is retaining parsimony in a model while avoiding reductionism by excluding necessary aspects of crowd psychology, such as the behavior of groups. We propose cognitive heuristics as a potential avenue to create a parsimonious model that incorporates core concepts of collective behavior derived from empirical research in crowd psychology.

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.

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.

Signals through VEGF receptor 2 (VEGFR2) increase vascular permeability, promoting cancer progression. Here the authors show that a point mutation in VEGFR2 preventing its auto-phosphorylation leads to reduced metastatic spread and improved response to chemotherapy in tumor-bearing mice, without affecting tumor inflammation.

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.

Head movements and the optic flow generated during the learning flights of bumblebees

Insects inform themselves about the 3D structure of their surroundings through motion parallax. During flight, they often simplify this task by minimising rotational image movement. Coordinated head and body movements generate rapid shifts of gaze separated by periods of almost zero rotational movement, during which the distance of objects from the insect can be estimated through pure translational optic flow. This saccadic strategy is less appropriate for assessing the distance between objects. Bees and wasps face this problem when learning the position of their nest-hole relative to objects close to it. They acquire the necessary information during specialised flights performed on leaving the nest. Here, we show that the bumblebee's saccadic strategy differs from other reported cases. In the fixations between saccades, a bumblebee's head continues to turn slowly, generating rotational flow. At specific points in learning flights these imperfect fixations generate a form of 'pivoting parallax', which is centred on the nest and enhances the visibility of features near the nest. Bumblebees may thus utilize an alternative form of motion parallax to that delivered by the standard 'saccade and fixate' strategy in which residual rotational flow plays a role in assessing the distances of objects from a focal point of interest.

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.

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.

Insect-inspired navigation algorithm for an aerial agent using satellite imagery

Humans have long marveled at the ability of animals to navigate swiftly, accurately, and across long distances. Many mechanisms have been proposed for how animals acquire, store, and retrace learned routes, yet many of these hypotheses appear incongruent with behavioral observations and the animals' neural constraints. The "Navigation by Scene Familiarity Hypothesis" proposed originally for insect navigation offers an elegantly simple solution for retracing previously experienced routes without the need for complex neural architectures and memory retrieval mechanisms. This hypothesis proposes that an animal can return to a target location by simply moving toward the most familiar scene at any given point. Proof of concept simulations have used computer-generated ant's-eye views of the world, but here we test the ability of scene familiarity algorithms to navigate training routes across satellite images extracted from Google Maps. We find that Google satellite images are so rich in visual information that familiarity algorithms can be used to retrace even tortuous routes with low-resolution sensors. We discuss the implications of these findings not only for animal navigation but also for the potential development of visual augmentation systems and robot guidance algorithms.

Formin-mediated actin polymerization at endothelial junctions is required for vessel lumen formation and stabilization

During blood vessel formation, endothelial cells (ECs) establish cell-cell junctions and rearrange to form multicellular tubes. Here, we show that during lumen formation, the actin nucleator and elongation factor, formin-like 3 (fmnl3), localizes to EC junctions, where filamentous actin (F-actin) cables assemble. Fluorescent actin reporters and fluorescence recovery after photobleaching experiments in zebrafish embryos identified a pool of dynamic F-actin with high turnover at EC junctions in vessels. Knockdown of fmnl3 expression, chemical inhibition of formin function, and expression of dominant-negative fmnl3 revealed that formin activity maintains a stable F-actin content at EC junctions by continual polymerization of F-actin cables. Reduced actin polymerization leads to destabilized endothelial junctions and consequently to failure in blood vessel lumenization and lumen instability. Our findings highlight the importance of formin activity in blood vessel morphogenesis.

The 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 these memories may be encoded by a common neural population within the ant brain.

Do endothelial cells dream of eclectic shape?

Endothelial cells (ECs) exhibit dramatic plasticity of form at the single- and collective-cell level during new vessel growth, adult vascular homeostasis, and pathology. Understanding how, when, and why individual ECs coordinate decisions to change shape, in relation to the myriad of dynamic environmental signals, is key to understanding normal and pathological blood vessel behavior. However, this is a complex spatial and temporal problem. In this review we show that the multidisciplinary field of Adaptive Systems offers a refreshing perspective, common biological language, and straightforward toolkit that cell biologists can use to untangle the complexity of dynamic, morphogenetic systems.

Visual scanning behaviours and their role in the navigation of the Australian desert ant Melophorus bagoti

Ants are excellent navigators, using a combination of innate strategies and learnt information to guide habitual routes. The mechanisms underlying this behaviour are little understood though one avenue of investigation is to explore how innate sensori-motor routines are used to accomplish route navigation. For instance, Australian desert ant foragers are occasionally observed to cease translation and rotate on the spot. Here, we investigate this behaviour using high-speed videography and computational analysis. We find that scanning behaviour is saccadic with pauses separated by fast rotations. Further, we have identified four situations where scanning is typically displayed: (1) by naïve ants on their first departure from the nest; (2) by experienced ants departing from the nest for their first foraging trip of the day; (3) by experienced ants when the familiar visual surround was experimentally modified, in which case frequency and duration of scans were proportional to the degree of modification; (4) when the information from visual cues is at odds with the direction indicated by the ant's path integration system. Taken together, we see a general relationship between scanning behaviours and periods of uncertainty.

Coordinating compass-based and nest-based flight directions during bumblebee learning and return flights

Bumblebees tend to face their nest over a limited range of compass directions when learning the nest's location on departure and finding it on their approach after foraging. They thus obtain similar views of the nest and its surroundings on their learning and return flights. How do bees coordinate their flights relative to nest-based and compass-based reference frames to get such similar views? We show, first, that learning and return flights contain straight segments that are directed along particular compass bearings, which are independent of the orientation of a bee's body. Bees are thus free within limits to adjust their viewing direction relative to the nest, without disturbing flight direction. Second, we examine the coordination of nest-based and compass-based control during likely information gathering segments of these flights: loops during learning flights and zigzags on return flights. We find that bees tend to start a loop or zigzag when flying within a restricted range of compass directions and to fly towards the nest and face it after a fixed change in compass direction, without continuous interactions between their nest-based and compass-based directions of flight. A preferred trajectory of compass-based flight over the course of a motif, combined with the tendency of the bees to keep their body oriented towards the nest automatically narrows the range of compass directions over which bees view the nest. Additionally, the absence of interactions between the two reference frames allows loops and zigzags to have a stereotyped form that can generate informative visual feedback.

Bumblebee calligraphy: the design and control of flight motifs in the learning and return flights of Bombus terrestris

Many wasps and bees learn the position of their nest relative to nearby visual features during elaborate ‘learning’ flights that they perform on leaving the nest. Return flights to the nest are thought to be patterned so that insects can reach their nest by matching their current view to views of their surroundings stored during learning flights. To understand how ground-nesting bumblebees might implement such a matching process, we have video-recorded the bees' learning and return flights and analysed the similarities and differences between the principal motifs of their flights. Loops that take bees away from and bring them back towards the nest are common during learning flights and less so in return flights. Zigzags are more prominent on return flights. Both motifs tend to be nest based. Bees often both fly towards and face the nest in the middle of loops and at the turns of zigzags. Before and after flight direction and body orientation are aligned, the two diverge from each other so that the nest is held within the bees' fronto-lateral visual field while flight direction relative to the nest can fluctuate more widely. These and other parallels between loops and zigzags suggest that they are stable variations of an underlying pattern, which enable bees to store and reacquire similar nest-focused views during learning and return flights.

Many hands make light work: further studies in group evolution

When niching or speciation is required to perform a task that has several different component parts, standard genetic algorithms (GAs) struggle. They tend to evaluate and select all individuals on the same part of the task, which leads to genetic convergence within the population. The goal of evolutionary niching methods is to enforce diversity in the population so that this genetic convergence is avoided. One drawback with some of these niching methods is that they require a priori knowledge or assumptions about the specific fitness landscape in order to work; another is that many such methods are not set up to work on cooperative tasks where fitness is only relevant at the group level. Here we address these problems by presenting the group GA, described earlier by the authors, which is a group-based evolutionary algorithm that can lead to emergent niching. After demonstrating the group GA on an immune system matching task, we extend the previous work and present two modified versions where the number of niches does not need to be specified ahead of time. In the random-group-size GA, the number of niches is varied randomly during evolution, and in the evolved-group-size GA the number of niches is optimized by evolution. This provides a framework in which we can evolve groups of individuals to collectively perform tasks with minimal a priori knowledge of how many subtasks there are or how they should be shared out.

Snapshots in ants? New interpretations of paradigmatic experiments

Ants can use visual information to guide long idiosyncratic routes and accurately pinpoint locations in complex natural environments. It has often been assumed that the world knowledge of these foragers consists of multiple discrete views that are retrieved sequentially for breaking routes into sections controlling approaches to a goal. Here we challenge this idea using a model of visual navigation that does not store and use discrete views to replicate the results from paradigmatic experiments that have been taken as evidence that ants navigate using such discrete snapshots. Instead of sequentially retrieving views, the proposed architecture gathers information from all experienced views into a single memory network, and uses this network all along the route to determine the most familiar heading at a given location. This algorithm is consistent with the navigation of ants in both laboratory and natural environments, and provides a parsimonious solution to deal with visual information from multiple locations.

A model of ant route navigation driven by scene familiarity

In this paper we propose a model of visually guided route navigation in ants that captures the known properties of real behaviour whilst retaining mechanistic simplicity and thus biological plausibility. For an ant, the coupling of movement and viewing direction means that a familiar view specifies a familiar direction of movement. Since the views experienced along a habitual route will be more familiar, route navigation can be re-cast as a search for familiar views. This search can be performed with a simple scanning routine, a behaviour that ants have been observed to perform. We test this proposed route navigation strategy in simulation, by learning a series of routes through visually cluttered environments consisting of objects that are only distinguishable as silhouettes against the sky. In the first instance we determine view familiarity by exhaustive comparison with the set of views experienced during training. In further experiments we train an artificial neural network to perform familiarity discrimination using the training views. Our results indicate that, not only is the approach successful, but also that the routes that are learnt show many of the characteristics of the routes of desert ants. As such, we believe the model represents the only detailed and complete model of insect route guidance to date. What is more, the model provides a general demonstration that visually guided routes can be produced with parsimonious mechanisms that do not specify when or what to learn, nor separate routes into sequences of waypoints.

A model of ant route navigation driven by scene familiarity

In this paper we propose a model of visually guided route navigation in ants that captures the known properties of real behaviour whilst retaining mechanistic simplicity and thus biological plausibility. For an ant, the coupling of movement and viewing direction means that a familiar view specifies a familiar direction of movement. Since the views experienced along a habitual route will be more familiar, route navigation can be re-cast as a search for familiar views. This search can be performed with a simple scanning routine, a behaviour that ants have been observed to perform. We test this proposed route navigation strategy in simulation, by learning a series of routes through visually cluttered environments consisting of objects that are only distinguishable as silhouettes against the sky. In the first instance we determine view familiarity by exhaustive comparison with the set of views experienced during training. In further experiments we train an artificial neural network to perform familiarity discrimination using the training views. Our results indicate that, not only is the approach successful, but also that the routes that are learnt show many of the characteristics of the routes of desert ants. As such, we believe the model represents the only detailed and complete model of insect route guidance to date. What is more, the model provides a general demonstration that visually guided routes can be produced with parsimonious mechanisms that do not specify when or what to learn, nor separate routes into sequences of waypoints.

How might ants use panoramic views for route navigation?

Studies of insect navigation have demonstrated that insects possess an interesting and sophisticated repertoire of visual navigation behaviours. Ongoing research seeks to help us understand how these behaviours are controlled in natural complex environments. A necessary complement to behavioural studies is an understanding of the sensory ecology within which an animal behaves. To this end we have analysed ants'-perspective views of a habitat within which desert ant navigation is well studied. Results from our analysis suggest that: parsimonious visual strategies for homing and route guidance are effective over behaviourally useful distances even in cluttered environments; that these strategies can function effectively using only the skyline heights as input; and that the simplicity and efficacy of using stored views as a visual compass makes it a viable and robust mechanism for route guidance.

Reconciling the STDP and BCM models of synaptic plasticity in a spiking recurrent neural network

Rate-coded Hebbian learning, as characterized by the BCM formulation, is an established computational model of synaptic plasticity. Recently it has been demonstrated that changes in the strength of synapses in vivo can also depend explicitly on the relative timing of pre- and postsynaptic firing. Computational modeling of this spike-timing-dependent plasticity (STDP) has demonstrated that it can provide inherent stability or competition based on local synaptic variables. However, it has also been demonstrated that these properties rely on synaptic weights being either depressed or unchanged by an increase in mean stochastic firing rates, which directly contradicts empirical data. Several analytical studies have addressed this apparent dichotomy and identified conditions under which distinct and disparate STDP rules can be reconciled with rate-coded Hebbian learning. The aim of this research is to verify, unify, and expand on these previous findings by manipulating each element of a standard computational STDP model in turn. This allows us to identify the conditions under which this plasticity rule can replicate experimental data obtained using both rate and temporal stimulation protocols in a spiking recurrent neural network. Our results describe how the relative scale of mean synaptic weights and their dependence on stochastic pre- or postsynaptic firing rates can be manipulated by adjusting the exact profile of the asymmetric learning window and temporal restrictions on spike pair interactions respectively. These findings imply that previously disparate models of rate-coded autoassociative learning and temporally coded heteroassociative learning, mediated by symmetric and asymmetric connections respectively, can be implemented in a single network using a single plasticity rule. However, we also demonstrate that forms of STDP that can be reconciled with rate-coded Hebbian learning do not generate inherent synaptic competition, and thus some additional mechanism is required to guarantee long-term input-output selectivity.

Spike-timing dependent plasticity and the cognitive map

Since the discovery of place cells – single pyramidal neurons that encode spatial location – it has been hypothesized that the hippocampus may act as a cognitive map of known environments. This putative function has been extensively modeled using auto-associative networks, which utilize rate-coded synaptic plasticity rules in order to generate strong bi-directional connections between concurrently active place cells that encode for neighboring place fields. However, empirical studies using hippocampal cultures have demonstrated that the magnitude and direction of changes in synaptic strength can also be dictated by the relative timing of pre- and post-synaptic firing according to a spike-timing dependent plasticity (STDP) rule. Furthermore, electrophysiology studies have identified persistent “theta-coded” temporal correlations in place cell activity in vivo, characterized by phase precession of firing as the corresponding place field is traversed. It is not yet clear if STDP and theta-coded neural dynamics are compatible with cognitive map theory and previous rate-coded models of spatial learning in the hippocampus. Here, we demonstrate that an STDP rule based on empirical data obtained from the hippocampus can mediate rate-coded Hebbian learning when pre- and post-synaptic activity is stochastic and has no persistent sequence bias. We subsequently demonstrate that a spiking recurrent neural network that utilizes this STDP rule, alongside theta-coded neural activity, allows the rapid development of a cognitive map during directed or random exploration of an environment of overlapping place fields. Hence, we establish that STDP and phase precession are compatible with rate-coded models of cognitive map development.

Preferred viewing directions of bumblebees (Bombus terrestrisL.) when learning and approaching their nest site

Many bees and wasps learn about the immediate surroundings of their nest during learning flights, in which they look back towards the nest and acquire visual information that guides their subsequent returns. Visual guidance to the nest is simplified by the insects' tendency to adopt similar viewing directions during learning and return flights. To understand better the factors determining the particular viewing directions that insects choose, we have recorded the learning and return flights of a ground-nesting bumblebee in two visual environments – an enclosed garden with a partly open view between north and west, and a flat roof with a more open panorama. In both places, bees left and returned to an inconspicuous nest hole in the centre of a tabletop, with the hole marked by one or more nearby cylinders. In all experiments, bees adopted similar preferred orientations on their learning and return flights. Bees faced predominantly either north or south, suggesting the existence of two attractors. The bees' selection between attractors seems to be influenced both by the distribution of light, as determined by the shape of the skyline, and by the direction of wind. In the partly enclosed garden with little or no wind, bees tended to face north throughout the day, i.e. towards the pole in the brighter half of their surroundings. When white curtains,which distributed skylight more evenly, were placed around the table, bees faced both north and south. The bees on the roof tended to face south or north when the wind came from a wide arc of directions from the south or north,respectively. We suggest that bees switch facing orientation between north and south as a compromise between maintaining a single viewing direction for efficient view-based navigation and responding to the distribution of light for the easier detection of landmarks seen against the ground or to the direction of the wind for exploiting olfactory cues.

What can be learnt from analysing insect orientation flights using probabilistic SLAM?

In this paper, we provide an analysis of orientation flights in bumblebees, employing a novel technique based on simultaneous localisation and mapping (SLAM) a probabilistic approach from autonomous robotics. We use SLAM to determine what bumblebees might learn about the locations of objects in the world through the arcing behaviours that are typical of these flights. Our results indicate that while the bees are clearly influenced by the presence of a conspicuous landmark, there is little evidence that they structure their flights to specifically learn about the position of the landmark.

Enhanced fidelity of diffusive nitric oxide signalling by the spatial segregation of source and target neurones in the memory centre of an insect brain

The messenger molecule nitric oxide (NO) is a key mediator of memory formation that can diffuse in the brain over tens of micrometres. It would seem therefore that NO derived from many individual neurones may merge into a volume signal that is inevitably ambiguous, relatively unspecific and thus unreliable. Here we report on the neuronal architecture that supports the NO-cyclic GMP signalling pathway in the mushroom body of an insect brain, the key centre for associative learning. We show that, in the locust (Schistocerca gregaria), parallel axons of intrinsic neurones (Kenyon cells) form tubular NO-producing zones surrounding central cores of NO-receptive Kenyon cell axons, which do not produce NO. This segregated architecture requires NO to spread at physiological concentrations up to 60 microm from the tube walls into the central NO-receptive cores. By modelling NO diffusion we show that a segregated architecture, which requires NO to act at a distance, affords significant advantages over a system where the same sources and targets intermingle. Segregation enhances the precision of NO volume signals by reducing noise and ambiguity, achieving a reliable integration of the activity of thousands of NO-source neurones. In a neural structure that forms NO-dependent associations, these properties of the segregated architecture may reduce the likelihood of forming spurious memories.

Modeling cooperative volume signaling in a plexus of nitric-oxide-synthase-expressing neurons

In vertebrate and invertebrate brains, nitric oxide (NO) synthase (NOS) is frequently expressed in extensive meshworks (plexuses) of exceedingly fine fibers. In this paper, we investigate the functional implications of this morphology by modeling NO diffusion in fiber systems of varying fineness and dispersal. Because size severely limits the signaling ability of an NO-producing fiber, the predominance of fine fibers seems paradoxical. Our modeling reveals, however, that cooperation between many fibers of low individual efficacy can generate an extensive and strong volume signal. Importantly, the signal produced by such a system of cooperating dispersed fibers is significantly more homogeneous in both space and time than that produced by fewer larger sources. Signals generated by plexuses of fine fibers are also better centered on the active region and less dependent on their particular branching morphology. We conclude that an ultrafine plexus is configured to target a volume of the brain with a homogeneous volume signal. Moreover, by translating only persistent regional activity into an effective NO volume signal, dispersed sources integrate neural activity over both space and time. In the mammalian cerebral cortex, for example, the NOS plexus would preferentially translate persistent regional increases in neural activity into a signal that targets blood vessels residing in the same region of the cortex, resulting in an increased regional blood flow. We propose that the fineness-dependent properties of volume signals may in part account for the presence of similar NOS plexus morphologies in distantly related animals.

Four-dimensional neuronal signaling by nitric oxide: a computational analysis

Nitric oxide (NO) is now recognized as a transmitter of neurons that express the neuronal isoform of the enzyme nitric oxide synthase. NO, however, violates some of the key tenets of chemical transmission, which is classically regarded as occurring at points of close apposition between neurons. It is the ability of NO to diffuse isotropically in aqueous and lipid environments that has suggested a radically different form of signaling in which the transmitter acts four-dimensionally in space and time, affecting volumes of the brain containing many neurons and synapses. Although "volume signaling" clearly challenges simple connectionist models of neural processing, crucial to its understanding are the spatial and temporal dynamics of the spread of NO within the brain. Existing models of NO diffusion, however, have serious shortcomings because they represent solutions for "point-sources," which have no physical dimensions. Methods for overcoming these difficulties are presented here, and results are described that show how NO spreads from realistic neural architectures with both simple symmetrical and irregular shapes. By highlighting the important influence of the geometry of NO sources, our results provide insights into the four-dimensional spread of a diffusing messenger. We show for example that reservoirs of NO that accumulate in volumes of the nervous system where NO is not synthesized contribute significantly to the temporal and spatial dynamics of NO spread.

The afterdepolarization in Rana temporaria muscle fibres following osmotic shock

Rana temporaria sartorius muscle fibres were exposed to varied sequences of solution and temperature changes that have been employed hitherto in procedures that sought to decouple the transverse tubules from the surface membrane. The incidence of such detubulation was assessed in large numbers of fibres through demonstrating a loss or otherwise of the after-depolarization that normally reflects successful tubular propagation of the surface action potential. This criterion yielded assessments of the existing detubulation techniques in agreement with earlier results. The experiments then developed an improved detubulation procedure that required only brief (15 min) exposures to glycerol, its replacement in a single step by a Ca2+/Mg(2+)-Ringer solution for 30 min, and rapid cooling from room temperature (19-21 degrees C) to 6-10 degrees C prior to final restoration of the normal Ringer solution. This sequence of steps yielded an optimal incidence (98%) of detubulation in viable surface fibres that were amenable to electrophysiological studies. Studies that systematically modified the detubulation procedure demonstrated that the omission of any one step in the protocol significantly reduced the incidence of detubulation with or without accompanying deteriorations in fibre resting potentials. Successful detubulation accordingly required an initial exposure to an optimal glycerol concentration that lasted for a minimal duration and for its abrupt withdrawal. Inclusion of a cooling step within 30 min after glycerol withdrawal was coincident with, and critical to, optimal tubular isolation. Thus, cooling steps that either preceded, or that followed the glycerol withdrawal step by more than 60 min, resulted in a sharp reduction in the incidence of detubulation. Similarly, a critical period of exposure to Ca2+/Mg2+ Ringer solution also promoted detubulation without compromising the recovery of stable and satisfactory resting potentials. The findings reported here remain consistent with a primarily osmotic mechanism for detubulation. However, they demonstrated additional and important influences of temperature and of divalent cation concentration on the extent of tubular detachment when such factors were modified during the time course of the expected volume changes that followed each adjustment in osmotic condition.

Extension of the mitochondrial transporter super-family: sequences of five members from the nematode worm, Caenorhabditis elegans

The sequences are presented of cDNAs encoding five related proteins from the nematode worm, Caenorhabditis elegans. Three of them can be recognised as the homologues of the ADP/ATP, phosphate and oxoglutarate/malate carrier proteins that have been found in the inner membranes of mitochondria in other species. These carrier proteins, and the uncoupling protein from the mitochondria in mammalian brown adipose tissue, have common features in their primary and secondary structures, and are members of the same protein super-family. Members of this super-family have polypeptide chains approximately 300 amino acid long that consist of three tandem related sequences of about 100 amino acids. The tandem repeats from the different proteins are inter-related, and each repeat is probably folded into a common secondary structural motif consisting of two hydrophobic stretches of amino acids with the potential to form membrane spanning alpha-helices, linked by an extensive hydrophilic region. The common characteristic features of this family of proteins are also present in sequences of two further proteins, named C1 and C2, encoded in nematode cDNAs, and in four published protein sequences from various sources. Neither the transport properties nor the subcellular locations of any of this latter group of six proteins are known. Therefore, currently the super-family of mitochondrial carrier proteins has at least ten different members.

The metabolism of 2,5-diphenyloxazole (PPO) in human lymphocytes and rat liver microsomes

The oxidative metabolism of 2,5-diphenyloxazole (PPO) is associated with 3-methylcholanthrene inducible cytochrome P-450. The major metabolite formed has m/z of 237, corresponding to hydroxylated PPO. All the possible hydroxylated metabolites of PPO were synthesized and characterized, enabling the assignment of a structure for the major metabolite and two minor metabolites. The metabolites are easily extracted and their fluorescence is quantifiable in alkaline medium with a sample fluorescence to blank fluorescence ratio of 400:1. A sensitive HPLC assay of PPO metabolism was also developed. PPO metabolism is readily catalyzed by 3-methylcholanthrene-induced rat liver microsomes and strongly inhibited by alpha-naphthoflavone, but poorly inhibited by metyrapone or SKF 525A, indicating the involvement of cytochrome P-448 or P1-450 in the metabolism of PPO. With human lymphocytes the method has proven to be a good indicator of "aryl hydrocarbon hydroxylase" (AHH) activity, correlating well with AHH assays using benzo(alpha)pyrene (BP) as a substrate. Both the induced BP and PPO metabolism by human lymphocytes is inhibited by alpha-naphthoflavone, but not by metyrapone.

Identification and synthesis of O-methylcatechol metabolites of phenobarbital and some N-alkyl derivatives

5-Ethyl-5-(4-hydroxy-3-methoxyphenyl) barbituric acid was identified as a new, minor metabolite of phenobarbital in man. The identity of this O-methylcatechol metabolite was confirmed by an unequivocal chemical synthesis, and by GC-MS studies. Mephobarbital and the 1,3-dimethyl, 1-ethyl, and 1,3-diethyl analogues of phenobarbital yielded the corresponding N-alkylated O-methylcatechol metabolites, all of which were confirmed by synthesis. The N-alkyl barbiturates each gave additionally at least one O-methylcatechol metabolite in which N-dealkylation had occurred. These metabolites accounted for approximately 1-5% of the orally administered dose in man.

The Unambiguous Syntheses of the 2,5-Diphenyloxazole Metabolites

The syntheses of the six possible monohydroxy derivatives and the N- oxide of 2,5-diphenyloxazole are described. Attempts towards the synthesis of 2,5-diphenyloxazol-4(5H)-one are also reported.