Insect Orientation: The Drosophila Wind Compass Pathway

Synaptic ring attractor: A unified framework for attractor dynamics and multiple cues integration

Effective cue integration is essential for an animal's survival. The ring attractor network has emerged as a powerful framework for understanding how animals seamlessly integrate various cues. This network not only elucidates neural dynamics within the brain, especially in spatial encoding systems like the heading direction (HD) system, but also sheds light on cue integration within decision-making processes. Yet, many significant phenomena across different fields lack clear explanations. For instance, in physiology, the integration mechanism of Drosophila's compass neuron when confronted with conflicting self-motion cues and external sensory cues with varying gain control settings is not well elucidated. Similarly, in ethology, the decision-making system shows Bayesian integration (BI) under minimal cue conflicts, but shifts to a winner-take-all (WTA) mode as conflicts surpass a certain threshold. To address these gaps, we introduce a ring attractor network with asymmetrical neural connections and synaptic dynamics in this paper. A thorough series of simulations has been conducted to assess its ability to track external cues and integrate conflicting cues. The results from these simulations demonstrate that the proposed model replicates observed neural dynamics and offers a framework for modeling biologically plausible cue integration behaviors. Furthermore, our findings yield several testable predictions that could inform future neuroethological research, providing insights into the role of ring attractor dynamics in the animal brain.

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.

A Fast-Tracking-Particle-Inspired Flow-Aided Control Approach for Air Vehicles in Turbulent Flow

Natural phenomena such as insect migration and the thermal soaring of birds in turbulent environments demonstrate animals' abilities to exploit complex flow structures without knowledge of global velocity profiles. Similar energy-harvesting features can be observed in other natural phenomena such as particle transport in turbulent fluids. This paper presents a new feedback control approach inspired by experimental studies on particle transport that have recently illuminated particles' ability to traverse homogeneous turbulence through the so-called fast-tracking effect. While in nature fast tracking is observed only in particles with inertial characteristics that match the flow parameters, the new fast-tracking feedback control approach presented in this paper employs available propulsion and actuation to allow the vehicle to respond to the surrounding flow in the same manner as ideal fast-tracking particles would. The resulting fast-tracking closed-loop controlled vehicle is then able to leverage homogeneous turbulent flow structures, such as sweeping eddies, to reduce travel time and energy consumption. The fast-tracking approach is shown to significantly outperform existing optimal control solutions, such as linear quadratic regulator and bang-bang control, and to be robust to changes in the vehicle characteristics and/or turbulent flow parameters.

A place learning assay for tethered walking Drosophila

BACKGROUND

Drosophila shows a range of visually guided memory and learning behaviors, including place learning. Investigating the dynamics of neural circuits underlying such behaviors requires learning assays in tethered animals, compatible with in vivo imaging experiments.

NEW METHOD

Here, we introduce an assay for place learning for tethered walking flies. A cylindrical arena is rotated and translated in real time around the fly in concert with the rotational and translational walking activity measured with an air supported ball, resulting in a mechanical virtual reality (VR).

RESULTS

Navigation together with heat-based operant conditioning allows flies to learn the location of a cool spot with respect to a visual landmark. Flies optimize the time and distance required to find the cool spot over a similar number of trials as observed in assays with freely moving flies. Additionally, a fraction of flies remembers the location of the cool spot also after the conditioning heat is removed.

COMPARISON WITH EXISTING METHODS

Learning tasks have been implemented in tethered flying as well as walking flies. Mechanically translating and rotating an arena in concert with the fly's walking activity enables navigation in a three dimensional environment.

CONCLUSION

In the developed mechanical VR flies can learn to remember the location of a cool place within an otherwise hot environment with respect to a visual landmark. Implementing place learning in a tethered walking configuration is a precondition for investigating the underlying circuit dynamics using functional imaging.

Insect odorant receptor-based biosensors: Current status and prospects

Whilst the senses of vision and hearing have been successfully automated and miniaturized in portable formats (e.g. smart phone), this is yet to be achieved with the sense of smell. This is because the sensing challenge is not trivial as it involves navigating a chemosensory space comprising thousands of volatile organic compounds. Distinct aroma recognition is based on detecting unique combinations of volatile organic compounds. In natural olfactory systems this is accomplished by employing odorant receptors (ORs) with varying specificities, together with combinatorial neural coding mechanisms. Attempts to mimic the remarkable sensitivity and accuracy of natural olfactory systems has therefore been challenging. Current portable chemical sensors for odorant detection are neither sensitive nor selective, prompting research exploring artificial olfactory devices that use natural OR proteins for sensing. Much research activity to develop OR based biosensors has concentrated on mammalian ORs, however, insect ORs have not been explored as extensively. Insects possess an extraordinary sense of smell due to a repertoire of odorant receptors evolved to interpret olfactory cues vital to the insects' survival. The potential of insect ORs as sensing elements is only now being unlocked through recent research efforts to understand their structure, ligand binding mechanisms and development of odorant biosensors. Like their mammalian counterparts, there are many challenges with working with insect ORs. These include expression, purification and presentation of the insect OR in a stable display format compatible with an effective transduction methodology while maintaining OR structure and function. Despite these challenges, significant progress has been demonstrated in developing OR-based biosensors which exploit insect ORs in cells, lipid bilayers, liposomes and nanodisc formats. Ultrasensitive and highly selective detection of volatile organic compounds has been validated by coupling these insect OR display formats with transduction methodologies spanning optical (fluorescence) and electrical (field effect transistors, electrochemical impedance spectroscopy) techniques. This review summarizes the current status of insect OR based biosensors and their future outlook.

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.