Prediction of the size and spatial distribution of free-roaming dog populations in urban areas of Nepal.

A factor constraining the elimination of dog-mediated human rabies is limited information on the size and spatial distribution of free-roaming dog populations (FRDPs). The aim of this study was to develop a statistical model to predict the size of free-roaming dog populations and the spatial distribution of free-roaming dogs in urban areas of Nepal, based on real-world dog census data from the Himalayan Animal Rescue Trust (HART) and Animal Nepal. Candidate explanatory variables included proximity to roads, building density, specific building types, human population density and normalised difference vegetation index (NDVI). A multivariable Poisson point process model was developed to estimate dog population size in four study locations in urban Nepal, with building density and distance from nearest retail food establishment or lodgings as explanatory variables. The proposed model accurately predicted, within a 95 % confidence interval, the surveyed FRDP size and spatial distribution for all four study locations. This model is proposed for further testing and refinement in other locations as a decision-support tool alongside observational dog population size estimates, to inform dog health and public health initiatives including rabies elimination efforts to support the 'zero by 30' global mission.

Beyond prediction error: 25 years of modeling the associations formed in the insect mushroom body.

The insect mushroom body has gained increasing attention as a system in which the computational basis of neural learning circuits can be unraveled. We now understand in detail the key locations in this circuit where synaptic associations are formed between sensory patterns and values leading to actions. However, the actual learning rule (or rules) implemented by neural activity and leading to synaptic change is still an open question. Here, I survey the diversity of answers that have been offered in computational models of this system over the past decades, including the recurring assumption-in line with top-down theories of associative learning-that the core function is to reduce prediction error. However, I will argue, a more bottom-up approach may ultimately reveal a richer algorithmic capacity in this still enigmatic brain neuropil.

Learning with sparse reward in a gap junction network inspired by the insect mushroom body.

Animals can learn in real-life scenarios where rewards are often only available when a goal is achieved. This 'distal' or 'sparse' reward problem remains a challenge for conventional reinforcement learning algorithms. Here we investigate an algorithm for learning in such scenarios, inspired by the possibility that axo-axonal gap junction connections, observed in neural circuits with parallel fibres such as the insect mushroom body, could form a resistive network. In such a network, an active node represents the task state, connections between nodes represent state transitions and their connection to actions, and current flow to a target state can guide decision making. Building on evidence that gap junction weights are adaptive, we propose that experience of a task can modulate the connections to form a graph encoding the task structure. We demonstrate that the approach can be used for efficient reinforcement learning under sparse rewards, and discuss whether it is plausible as an account of the insect mushroom body.

Dynamic antennal positioning allows honeybee followers to decode the dance.

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

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.

Neuromorphic sequence learning with an event camera on routes through vegetation.

For many robotics applications, it is desirable to have relatively low-power and efficient onboard solutions. We took inspiration from insects, such as ants, that are capable of learning and following routes in complex natural environments using relatively constrained sensory and neural systems. Such capabilities are particularly relevant to applications such as agricultural robotics, where visual navigation through dense vegetation remains a challenging task. In this scenario, a route is likely to have high self-similarity and be subject to changing lighting conditions and motion over uneven terrain, and the effects of wind on leaves increase the variability of the input. We used a bioinspired event camera on a terrestrial robot to collect visual sequences along routes in natural outdoor environments and applied a neural algorithm for spatiotemporal memory that is closely based on a known neural circuit in the insect brain. We show that this method is plausible to support route recognition for visual navigation and more robust than SeqSLAM when evaluated on repeated runs on the same route or routes with small lateral offsets. By encoding memory in a spiking neural network running on a neuromorphic computer, our model can evaluate visual familiarity in real time from event camera footage.

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.

CATER: Combined Animal Tracking & Environment Reconstruction.

Quantifying the behavior of small animals traversing long distances in complex environments is one of the most difficult tracking scenarios for computer vision. Tiny and low-contrast foreground objects have to be localized in cluttered and dynamic scenes as well as trajectories compensated for camera motion and drift in multiple lengthy recordings. We introduce CATER, a novel methodology combining an unsupervised probabilistic detection mechanism with a globally optimized environment reconstruction pipeline enabling precision behavioral quantification in natural environments. Implemented as an easy to use and highly parallelized tool, we show its application to recover fine-scale motion trajectories, registered to a high-resolution image mosaic reconstruction, of naturally foraging desert ants from unconstrained field recordings. By bridging the gap between laboratory and field experiments, we gain previously unknown insights into ant navigation with respect to motivational states, previous experience, and current environments and provide an appearance-agnostic method applicable to study the behavior of a wide range of terrestrial species under realistic conditions.

A virtuous cycle between invertebrate and robotics research: perspective on a decade of Living Machines research.

Many invertebrates are ideal model systems on which to base robot design principles due to their success in solving seemingly complex tasks across domains while possessing smaller nervous systems than vertebrates. Three areas are particularly relevant for robot designers: Research on flying and crawling invertebrates has inspired new materials and geometries from which robot bodies (their morphologies) can be constructed, enabling a new generation of softer, smaller, and lighter robots. Research on walking insects has informed the design of new systems for controlling robot bodies (their motion control) and adapting their motion to their environment without costly computational methods. And research combining wet and computational neuroscience with robotic validation methods has revealed the structure and function of core circuits in the insect brain responsible for the navigation and swarming capabilities (their mental faculties) displayed by foraging insects. The last decade has seen significant progress in the application of principles extracted from invertebrates, as well as the application of biomimetic robots to model and better understand how animals function. This Perspectives paper on the past 10 years of the Living Machines conference outlines some of the most exciting recent advances in each of these fields before outlining lessons gleaned and the outlook for the next decade of invertebrate robotic research.

Erratum: Weighted cue integration for straight-line orientation.

[This corrects the article DOI: 10.1016/j.isci.2022.105207.].

Impact of central complex lesions on innate and learnt visual navigation in ants.

Wood ants are excellent navigators, using a combination of innate and learnt navigational strategies to travel between their nest and feeding sites. Visual navigation in ants has been studied extensively, however, we have little direct evidence for the underlying neural mechanisms. Here, we perform lateralized mechanical lesions in the central complex (CX) of wood ants, a midline structure known to allow an insect to keep track of the direction of sensory cues relative to its own orientation and to control movement. We lesioned two groups of ants and observed their behaviour in an arena with a large visual landmark present. The first group of ants were naïve and when intact such ants show a clear innate attraction to the conspicuous landmark. The second group of ants were trained to aim to a food location to the side of the landmark. The general heading of naïve ants towards a visual cue was not altered by the lesions, but the heading of ants trained to a landmark adjacent food position was affected. Thus, CX lesions had a specific impact on learnt visual guidance. We also observed that lateralised lesions altered the fine details of turning with lesioned ants spending less time turning to the side ipsilateral of the lesion. The results confirm the role of the CX in turn control and highlight its important role in the implementation of learnt behaviours that rely on information from other brain regions.

Weighted cue integration for straight-line orientation.

Animals commonly integrate multiple sources of information to guide their behavior. Among insects, previous studies have suggested that the relative reliability of cues affects their weighting in behavior, but have not systematically explored how well alternative integration strategies can account for the observed directional choices. Here, we characterize the directional reliability of an ersatz sun at different elevations and wind at different speeds as guiding cues for a species of ball-rolling dung beetle. The relative reliability is then shown to determine which cue dominates when the cues are put in conflict. We further show through modeling that the results are best explained by continuous integration of the cues as a vector-sum (rather than switching between them) but with non-optimal weighting and small individual biases. The neural circuitry in the insect central complex appears to provide an ideal substrate for this type of vector-sum-based integration mechanism.

A Neural Model for Insect Steering Applied to Olfaction and Path Integration.

Many animal behaviors require orientation and steering with respect to the environment. For insects, a key brain area involved in spatial orientation and navigation is the central complex. Activity in this neural circuit has been shown to track the insect's current heading relative to its environment and has also been proposed to be the substrate of path integration. However, it remains unclear how the output of the central complex is integrated into motor commands. Central complex output neurons project to the lateral accessory lobes (LAL), from which descending neurons project to thoracic motor centers. Here, we present a computational model of a simple neural network that has been described anatomically and physiologically in the LALs of male silkworm moths, in the context of odor-mediated steering. We present and analyze two versions of this network, one rate based and one based on spiking neurons. The modeled network consists of an inhibitory local interneuron and a bistable descending neuron (flip-flop) that both receive input in the LAL. The flip-flop neuron projects onto neck motor neurons to induce steering. We show that this simple computational model not only replicates the basic parameters of male silkworm moth behavior in a simulated odor plume but can also take input from a computational model of path integration in the central complex and use it to steer back to a point of origin. Furthermore, we find that increasing the level of detail within the model improves the realism of the model's behavior, leading to the emergence of looping behavior as an orientation strategy. Our results suggest that descending neurons originating in the LALs, such as flip-flop neurons, are sufficient to mediate multiple steering behaviors. This study is therefore a first step to close the gap between orientation circuits in the central complex and downstream motor centers.

An incentive circuit for memory dynamics in the mushroom body of Drosophila melanogaster.

Insects adapt their response to stimuli, such as odours, according to their pairing with positive or negative reinforcements, such as sugar or shock. Recent electrophysiological and imaging findings in Drosophila melanogaster allow detailed examination of the neural mechanisms supporting the acquisition, forgetting, and assimilation of memories. We propose that this data can be explained by the combination of a dopaminergic plasticity rule that supports a variety of synaptic strength change phenomena, and a circuit structure (derived from neuroanatomy) between dopaminergic and output neurons that creates different roles for specific neurons. Computational modelling shows that this circuit allows for rapid memory acquisition, transfer from short term to long term, and exploration/exploitation trade-off. The model can reproduce the observed changes in the activity of each of the identified neurons in conditioning paradigms and can be used for flexible behavioural control.

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

Solitary foraging insects, such as ants, maintain an estimate of the direction and distance to their starting location as they move away from it, in a process known as path integration. This estimate, commonly known as the "home vector," is updated continuously as the ant moves1-4 and is reset as soon as it enters its nest,5 yet ants prevented from returning to their nest can still use their home vector when released several hours later.6,7 This conjunction of fast update and long persistence of the home vector memory does not directly map to existing accounts of short-, mid-, and long-term memory;2,8-12 hence, the substrate of this memory remains unknown. Chill-coma anesthesia13-15 has previously been shown to affect associative memory retention in fruit flies14,16 and honeybees.9,17,18 We investigate the nature of path integration memory by anesthetizing ants after they have accumulated home vector information and testing if the memory persists on recovery. We show that after anesthesia the memory of the distance ants have traveled is degraded, but the memory of the direction is retained. We also show that this is consistent with models of path integration that maintain the memory in a redundant Cartesian coordinate system and with the hypothesis that chill-coma produces a proportional reduction of the memory, rather than a subtractive reduction or increase of noise. The observed effect is not compatible with a memory based on recurrent circuit activity and points toward an activity-dependent molecular process as the basis of path integration memory.

A 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.

Object manipulation without hands.

Our current understanding of manipulation is based on primate hands, resulting in a detailed but narrow perspective of ways to handle objects. Although most other animals lack hands, they are still capable of flexible manipulation of diverse objects, including food and nest materials, and depend on dexterity in object handling to survive and reproduce. Birds, for instance, use their bills and feet to forage and build nests, while insects carry food and construct nests with their mandibles and legs. Bird bills and insect mandibles are much simpler than a primate hand, resembling simple robotic grippers. A better understanding of manipulation in these and other species would provide a broader comparative perspective on the origins of dexterity. Here we contrast data from primates, birds and insects, describing how they sense and grasp objects, and the neural architectures that control manipulation. Finally, we outline techniques for collecting comparable manipulation data from animals with diverse morphologies and describe the practical applications of studying manipulation in a wide range of species, including providing inspiration for novel designs of robotic manipulators.

Implementing an Insect Brain Computational Circuit Using III-V Nanowire Components in a Single Shared Waveguide Optical Network.

Recent developments in photonics include efficient nanoscale optoelectronic components and novel methods for subwavelength light manipulation. Here, we explore the potential offered by such devices as a substrate for neuromorphic computing. We propose an artificial neural network in which the weighted connectivity between nodes is achieved by emitting and receiving overlapping light signals inside a shared quasi 2D waveguide. This decreases the circuit footprint by at least an order of magnitude compared to existing optical solutions. The reception, evaluation, and emission of the optical signals are performed by neuron-like nodes constructed from known, highly efficient III-V nanowire optoelectronics. This minimizes power consumption of the network. To demonstrate the concept, we build a computational model based on an anatomically correct, functioning model of the central-complex navigation circuit of the insect brain. We simulate in detail the optical and electronic parts required to reproduce the connectivity of the central part of this network using previously experimentally derived parameters. The results are used as input in the full model, and we demonstrate that the functionality is preserved. Our approach points to a general method for drastically reducing the footprint and improving power efficiency of optoelectronic neural networks, leveraging the superior speed and energy efficiency of light as a carrier of information.