Many insect species maintain a nest around which their foraging behaviour is centered, and can use path integration to maintain an accurate estimate of their distance and direction (a vector) to their nest. Some species, such as bees and ants, can also store the vector information for multiple salient locations in the world, such as food sources, in a common coordinate system. They can also use remembered views of the terrain around salient locations or along travelled routes to guide return. Recent modelling of these abilities shows convergence on a small set of algorithms and assumptions that appear sufficient to account for a wide range of behavioural data, and which can be mapped to specific insect brain circuits. Notably, this does not include any significant topological knowledge: the insect does not need to recover the information (implicit in their vector memory) about the relationships between salient places; nor to maintain any connectedness or ordering information between view memories; nor to form any associations between views and vectors. However, there remains some experimental evidence not fully explained by these algorithms that may point towards the existence of a more complex or integrated mental map in insects.

Several species of insects have been observed to perform accurate path integration, constantly updating a vector memory of their location relative to a starting position, which they can use to take a direct return path. Foraging insects such as bees and ants are also able to store and recall the vectors to return to food locations, and to take novel shortcuts between these locations. Other insects, such as dung beetles, are observed to integrate multimodal directional cues in a manner well described by vector addition. All these processes appear to be functions of the Central Complex, a highly conserved and strongly structured circuit in the insect brain. Modelling this circuit, at the single neuron level, suggests it has general capabilities for vector encoding, vector memory, vector addition and vector rotation that can support a wide range of directed and navigational behaviours.

Our world is full of living creatures that must share information to survive and reproduce. As humans, we easily forget how hard it is to communicate within natural environments. So how do organisms solve this challenge, using only natural resources? Ideas from computer science, physics and mathematics, such as energetic cost, compression, and detectability, define universal criteria that almost all communication systems must meet. We use insect swarms as a model system for identifying how organisms harness the dynamics of communication signals, perform spatiotemporal integration of these signals, and propagate those signals to neighboring organisms. In this talk I will focus on two types of communication in insect swarms: visual communication, in which fireflies communicate over long distances using light signals, and chemical communication, in which bees serve as signal amplifiers to propagate pheromone-based information about the queen’s location.

The western honey bee (Apis mellifera) is a domesticated pollinator famous for living in highly social colonies. In the spring, thousands of worker bees and a queen fly from their hive in search of a new home. They self-assemble into a swarm that hangs from a tree branch for several days. We reconstruct the non-isotropic arrangement of worker bees inside swarms made up of 3000 - 8000 bees using x-ray computed tomography. Some bees are stationary and hang from the attachment board or link their bodies into hanging chains to support the swarm structure. The remaining bees use the chains as pathways to walk around the swarm, potentially to feed the queen or communicate with one another. The top layers of bees bear more weight per bee than the remainder of the swarm, suggesting that bees are optimizing for additional factors besides weight distribution. Despite not having a clear leader, honey bees are able to organize into a swarm that protects the queen and remains stable until scout bees locate a new hive.

Join the Department of Bioengineering on the 26th May at 9:00am for The 2021 Annual Bioengineering Lecture + Bioinspired Guidance, Navigation and Control Symposium. This year’s lecture speaker will be distinguished bioengineer and neuroscientist Professor Mandyam V. Srinivasan AM FRS, from the University of Queensland. Professor Srinivasan studies visual systems, particularly those of bees and birds. His research has revealed how flying insects negotiate narrow gaps, regulate the height and speed of flight, estimate distance flown, and orchestrate smooth landings. Apart from enhancing fundamental knowledge, these findings are leading to novel, biologically inspired approaches to the design of guidance systems for unmanned aerial vehicles with applications in the areas of surveillance, security and planetary exploration. Following Professor Srinivasan’s lecture will be the Bioinspired GNC Mini Symposium with guest speakers from Google Deepmind, Imperial College London, the University of Würzburg and the University of Konstanz giving talks on their research into autonomous robot navigation, neural mechanisms of compass orientation in insects and computational approaches to motor control.

Collective behavior of organisms creates environmental micro-niches that buffer them from environmental fluctuations e.g., temperature, humidity, mechanical perturbations, etc., thus coupling organismal physiology, environmental physics, and population ecology. This talk will focus on a combination of biological experiments, theory, and computation to understand how a collective of bees can integrate physical and behavioral cues to attain a non-equilibrium steady state that allows them to resist and respond to environmental fluctuations of forces and flows. We analyze how bee clusters change their shape and connectivity and gain stability by spread-eagling themselves in response to mechanical perturbations. Similarly, we study how bees in a colony respond to environmental thermal perturbations by deploying a fanning strategy at the entrance that they use to create a forced ventilation stream that allows the bees to collectively maintain a constant hive temperature. When combined with quantitative analysis and computations in both systems, we integrate the sensing of the environmental cues (acceleration, temperature, flow) and convert them to behavioral outputs that allow the swarms to achieve a dynamic homeostasis.

I shall present two recent piece of work investigating how shape effects the transport of active particles in shear. Firstly we will consider the sedimentation of particles in 2D laminar flow fields of increasing complexity; and how insights from this can help explain why turbulence can enhance the sedimentation of negatively buoyant diatoms [1]. Secondly, we will consider the 3D transport of elongated active particles under the action of an aligning force (e.g. gyrotactic swimmers) in some simple flow fields; and will see how shape can influence the vertical distribution, for example changing the structure of thin layers [2]. [1] Enhanced sedimentation of elongated plankton in simple flows (2018). IMA Journal of Applied Mathematics W Clifton, RN Bearon, & MA Bees. [2] Elongation enhances migration through hydrodynamic shear (in Prep), RN Bearon & WM Durham.

Ants, bees and other insects have the ability to return to their nest or hive using a navigation strategy known as path integration. Similarly, fruit flies employ path integration to return to a previously visited food source. An important component of path integration is the ability of the insect to keep track of its heading relative to salient visual cues. A highly conserved brain region known as the central complex has been identified as being of key importance for the computations required for an insect to keep track of its heading. However, the similarities or differences of the underlying heading tracking circuit between species are not well understood. We sought to address this shortcoming by using reverse engineering techniques to derive the effective underlying neural circuits of two evolutionary distant species, the fruit fly and the locust. Our analysis revealed that regardless of the anatomical differences between the two species the essential circuit structure has not changed. Both effective neural circuits have the structural topology of a ring attractor with an eight-fold radial symmetry (Fig. 1). However, despite the strong similarities between the two ring attractors, there remain differences. Using computational modelling we found that two apparently small anatomical differences have significant functional effect on the ability of the two circuits to track fast rotational movements and to maintain a stable heading signal. In particular, the fruit fly circuit responds faster to abrupt heading changes of the animal while the locust circuit maintains a heading signal that is more robust to inhomogeneities in cell membrane properties and synaptic weights. We suggest that the effects of these differences are consistent with the behavioural ecology of the two species. On the one hand, the faster response of the ring attractor circuit in the fruit fly accommodates the fast body saccades that fruit flies are known to perform. On the other hand, the locust is a migratory species, so its behaviour demands maintenance of a defined heading for a long period of time. Our results highlight that even seemingly small differences in the distribution of dendritic fibres can have a significant effect on the dynamics of the effective ring attractor circuit with consequences for the behavioural capabilities of each species. These differences, emerging from morphologically distinct single neurons highlight the importance of a comparative approach to neuroscience.