Representation of Haltere Oscillations and Integration with Visual Inputs in the Fly Central Complex

Phase shift between joint rotation and actuation reflects dominant forces and predicts muscle activation patterns

During behavior, the work done by actuators on the body can be resisted by the body's inertia, elastic forces, gravity, or viscosity. The dominant forces that resist actuation have major consequences on the control of that behavior. In the literature, features and actuation of locomotion, for example, have been successfully predicted by nondimensional numbers (e.g. Froude number and Reynolds number) that generally express the ratio between two of these forces (gravitational, inertial, elastic, and viscous). However, animals of different sizes or motions at different speeds may not share the same dominant forces within a behavior, making ratios of just two of these forces less useful. Thus, for a broad comparison of behavior across many orders of magnitude of limb length and cycle period, a dimensionless number that includes gravitational, inertial, elastic, and viscous forces is needed. This study proposes a nondimensional number that relates these four forces: the phase shift (ϕ) between the displacement of the limb and the actuator force that moves it. Using allometric scaling laws, ϕ for terrestrial walking is expressed as a function of the limb length and the cycle period at which the limb steps. Scale-dependent values of ϕ are used to explain and predict the electromyographic (EMG) patterns employed by different animals as they walk.

Mechanosensory Control of Locomotion in Animals and Robots: Moving Forward

While animals swim, crawl, walk, and fly with apparent ease, building robots capable of robust locomotion remains a significant challenge. In this review, we draw attention to mechanosensation-the sensing of mechanical forces generated within and outside the body-as a key sense that enables robust locomotion in animals. We discuss differences between mechanosensation in animals and current robots with respect to (1) the encoding properties and distribution of mechanosensors and (2) the integration and regulation of mechanosensory feedback. We argue that robotics would benefit greatly from a detailed understanding of these aspects in animals. To that end, we highlight promising experimental and engineering approaches to study mechanosensation, emphasizing the mutual benefits for biologists and engineers that emerge from moving forward together.

The Role of Central Complex Neurons in Prey Detection and Tracking in the Freely Moving Praying Mantis (Tenodera sinensis)

Complex tasks like hunting moving prey in an unpredictable environment require high levels of motor and sensory integration. An animal needs to detect and track suitable prey objects, measure their distance and orientation relative to its own position, and finally produce the correct motor output to approach and capture the prey. In the insect brain, the central complex (CX) is one target area where integration is likely to take place. In this study, we performed extracellular multi-unit recordings on the CX of freely hunting praying mantises (Tenodera sinensis). Initially, we recorded the neural activity of freely moving mantises as they hunted live prey. The recordings showed activity in cells that either reflected the mantis's own movements or the actions of a prey individual, which the mantises focused on. In the latter case, the activity increased as the prey moved and decreased when it stopped. Interestingly, cells ignored the movement of the other prey than the one to which the mantis attended. To obtain quantitative data, we generated simulated prey targets presented on an LCD screen positioned below the clear floor of the arena. The simulated target oscillated back and forth at various angles and distances. We identified populations of cells whose activity patterns were strongly linked to the appearance, movement, and relative position of the virtual prey. We refer to these as sensory responses. We also found cells whose activity preceded orientation movement toward the prey. We call these motor responses. Some cells showed both sensory and motor properties. Stimulation through tetrodes in some of the preparations could also generate similar movements. These results suggest the crucial importance of the CX to prey-capture behavior in predatory insects like the praying mantis and, hence, further emphasize its role in behaviorally and ecologically relevant contexts.

The two-body problem: Proprioception and motor control across the metamorphic divide

Like a rocket being propelled into space, evolution has engineered flies to launch into adulthood via multiple stages. Flies develop and deploy two distinct bodies, linked by the transformative process of metamorphosis. The fly larva is a soft hydraulic tube that can crawl to find food and avoid predators. The adult fly has a stiff exoskeleton with articulated limbs that enable long-distance navigation and rich social interactions. Because the larval and adult forms are so distinct in structure, they require distinct strategies for sensing and moving the body. The metamorphic divide thus presents an opportunity for comparative analysis of neural circuits. Here, we review recent progress toward understanding the neural mechanisms of proprioception and motor control in larval and adult Drosophila. We highlight commonalities that point toward general principles of sensorimotor control and differences that may reflect unique constraints imposed by biomechanics. Finally, we discuss emerging opportunities for comparative analysis of neural circuit architecture in the fly and other animal species.

Flight-induced compass representation in the monarch butterfly heading network

For navigation, animals use a robust internal compass. Compass navigation is crucial for long-distance migrating animals like monarch butterflies, which use the sun to navigate over 4,000 km to their overwintering sites every fall. Sun-compass neurons of the central complex have only been recorded in immobile butterflies, and experimental evidence for encoding the animal's heading in these neurons is still missing. Although the activity of central-complex neurons exhibits a locomotor-dependent modulation in many insects, the function of such modulations remains unexplored. Here, we developed tetrode recordings from tethered flying monarch butterflies to reveal how flight modulates heading representation. We found that, during flight, heading-direction neurons change their tuning, transforming the central-complex network to function as a global compass. This compass is characterized by the dominance of processing steering feedback and allows for robust heading representation even under unreliable visual scenarios, an ideal strategy for maintaining a migratory heading over enormous distances.

A connectome of the Drosophila central complex reveals network motifs suitable for flexible navigation and context-dependent action selection

Flexible behaviors over long timescales are thought to engage recurrent neural networks in deep brain regions, which are experimentally challenging to study. In insects, recurrent circuit dynamics in a brain region called the central complex (CX) enable directed locomotion, sleep, and context- and experience-dependent spatial navigation. We describe the first complete electron microscopy-based connectome of the Drosophila CX, including all its neurons and circuits at synaptic resolution. We identified new CX neuron types, novel sensory and motor pathways, and network motifs that likely enable the CX to extract the fly's head direction, maintain it with attractor dynamics, and combine it with other sensorimotor information to perform vector-based navigational computations. We also identified numerous pathways that may facilitate the selection of CX-driven behavioral patterns by context and internal state. The CX connectome provides a comprehensive blueprint necessary for a detailed understanding of network dynamics underlying sleep, flexible navigation, and state-dependent action selection.

Takeoff diversity in Diptera

The order Diptera (true flies) are named for their two wings because their hindwings have evolved into specialized mechanosensory organs called halteres. Flies use halteres to detect body rotations and maintain stability during flight and other behaviours. The most recently diverged dipteran monophyletic subsection, the Calyptratae, is highly successful, accounting for approximately 12% of dipteran diversity, and includes common families like house flies. These flies move their halteres independently from their wings and oscillate their halteres during walking. Here, we demonstrate that this subsection of flies uses their halteres to stabilize their bodies during takeoff, whereas non-Calyptratae flies do not. We find that flies of the Calyptratae are able to take off more rapidly than non-Calyptratae flies without sacrificing stability. Haltere removal decreased both velocity and stability in the takeoffs of Calyptratae, but not other flies. The loss of takeoff velocity following haltere removal in Calyptratae (but not other flies) is a direct result of a decrease in leg extension speed. A closely related non-Calyptratae species (D. melanogaster) also has a rapid takeoff, but takeoff duration and stability are unaffected by haltere removal. Haltere use thus allows for greater speed and stability during fast escapes, but only in the Calyptratae clade.

Encoding and control of orientation to airflow by a set of Drosophila fan-shaped body neurons

The insect central complex (CX) is thought to underlie goal-oriented navigation but its functional organization is not fully understood. We recorded from genetically-identified CX cell types in Drosophila and presented directional visual, olfactory, and airflow cues known to elicit orienting behavior. We found that a group of neurons targeting the ventral fan-shaped body (ventral P-FNs) are robustly tuned for airflow direction. Ventral P-FNs did not generate a 'map' of airflow direction. Instead, cells in each hemisphere were tuned to 45° ipsilateral, forming a pair of orthogonal bases. Imaging experiments suggest that ventral P-FNs inherit their airflow tuning from neurons that provide input from the lateral accessory lobe (LAL) to the noduli (NO). Silencing ventral P-FNs prevented flies from selecting appropriate corrective turns following changes in airflow direction. Our results identify a group of CX neurons that robustly encode airflow direction and are required for proper orientation to this stimulus.

Using virtual worlds to understand insect navigation for bio-inspired systems

Insects perform a wide array of intricate behaviors over large spatial and temporal scales in complex natural environments. A mechanistic understanding of insect cognition has direct implications on how brains integrate multimodal information and can inspire bio-based solutions for autonomous robots. Virtual Reality (VR) offers an opportunity assess insect neuroethology while presenting complex, yet controlled, stimuli. Here, we discuss the use of insects as inspiration for artificial systems, recent advances in different VR technologies, current knowledge gaps, and the potential for application of insect VR research to bio-inspired robots. Finally, we advocate the need to diversify our model organisms, behavioral paradigms, and embrace the complexity of the natural world. This will help us to uncover the proximate and ultimate basis of brain and behavior and extract general principles for common challenging problems.

A dynamical model exploring sensory integration in the insect central complex substructures

It is imperative that an animal has the ability to contextually integrate received sensory information to formulate appropriate behavioral responses. Determining a body heading based on a multitude of ego-motion cues and visual landmarks is an example of such a task that requires this context dependent integration. The work presented here simulates a sensory integrator in the insect brain called the central complex (CX). Based on the architecture of the CX, we assembled a dynamical neural simulation of two structures called the protocerebral bridge (PB) and the ellipsoid body (EB). Using non-spiking neuronal dynamics, our simulation was able to recreate in vivo neuronal behavior such as correlating body rotation direction and speed to activity bumps within the EB as well as updating the believed heading with quick secondary system updates. With this model, we performed sensitivity analysis of certain neuronal parameters as a possible means to control multi-system gains during sensory integration. We found that modulation of synapses in the memory network and EB inhibition are two possible mechanisms in which a sensory system could affect the memory stability and gain of another input, respectively. This model serves as an exploration in network design for integrating simultaneous idiothetic and allothetic cues in the task of body tracking and determining contextually dependent behavioral outputs.