Functional diversity from generic encoding in insect campaniform sensilla

Flow sensing on dragonfly wings

One feature of animal wings is their embedded mechanosensory system that can support flight control. Insect wings are particularly interesting as they are highly deformable yet the actuation is limited to the wing base. It is established that strain sensors on insect wings can directly mediate reflexive control; however, little is known about airflow sensing by insect wings. What information can flow sensors capture and how can flow sensing benefit flight control? Here, we use the dragonfly (Sympetrum striolatum) as a model to explore the function of wing sensory bristles in the context of flight control. Combining our detailed anatomical reconstructions of both the sensor microstructures and wing architecture, we used computational fluid dynamics simulations to ask the following questions. (1) Are there strategic locations on wings that sample flow for estimating aerodynamically relevant parameters such as the local effective angle of attack? (2) Is the sensory bristle distribution on dragonfly wings optimal for flow sensing? (3) What is the aerodynamic effect of microstructures found near the sensory bristles on dragonfly wings? We discuss the benefits of flow sensing for flexible wings and how the evolved sensor placement affects information encoding.

Mechanosensory encoding of forces in walking uphill and downhill: force feedback can stabilize leg movements in stick insects

Force feedback could be valuable in adapting walking to diverse terrains, but the effects of changes in substrate inclination on discharges of sensory receptors that encode forces have rarely been examined. In insects, force feedback is provided by campaniform sensilla, mechanoreceptors that monitor forces as cuticular strains. We neurographically recorded responses of stick insect tibial campaniform sensilla to "naturalistic" forces (joint torques) that occur at the hind leg femur-tibia (FT) joint in uphill, downhill, and level walking. The FT joint torques, obtained in a previous study that used inverse dynamics to analyze data from freely moving stick insects, are quite variable during level walking (including changes in sign) but are larger in magnitude and more consistent when traversing sloped surfaces. Similar to vertebrates, insects used predominantly extension torque in propulsion on uphill slopes and flexion torques to brake forward motion when going downhill. Sensory discharges to joint torques reflected the torque direction but, unexpectedly, often occurred as multiple bursts that encoded the rate of change of positive forces (dF/dt) even when force levels were high. All discharges also showed hysteresis (history dependence), as firing substantially decreased or ceased during transient force decrements. These findings have been tested in simulation in a mathematical model of the sensilla (Szczecinski NS, Dallmann CJ, Quinn RD, Zill SN. Bioinspir Biomim 16: 065001, 2021) that accurately reproduced the biological data. Our results suggest the hypothesis that sensory feedback from the femoro-tibial joint indicating force dynamics (dF/dt) can be used to counter the instability in traversing sloped surfaces in animals and, potentially, in walking machines.NEW & NOTEWORTHY Discharges of sensory receptors (campaniform sensilla) in the hind legs of stick insects can differentially signal forces that occur in walking uphill versus walking downhill. Unexpectedly, sensory firing most closely reflects the rate of change of force (dF/dt) even when the force levels are high. These signals have been replicated in a mathematical model of the receptors and could be used to stabilize leg movements both in the animal and in a walking robot.

Hair, there and everywhere: A comparison of bat wing sensory hair distribution

Bat wing membranes are composed of specialized skin that is covered with small sensory hairs which are likely mechanosensory and have been suggested to help bats sense airflow during flight. These sensory hairs have to date been studied in only a few of the more than 1,400 bat species around the world. Little is known about the diversity of the sensory hair network across the bat phylogeny. In this study, we use high-resolution photomicrographs of preserved bat wings from 17 species in 12 families to characterize the distribution of sensory hairs along the wing and among species. We identify general patterns of sensory hair distribution across species, including the apparent relationships of sensory hairs to intramembranous wing muscles, the network of connective tissues in the wing membrane, and the bones of the forelimb. We also describe distinctive clustering of these sensory structures in some species. We also quantified sensory hair density in several regions of interest in the propatagium, plagiopatagium, and dactylopagatia, finding that sensory hair density was higher proximally than distally. This examination of the anatomical organization of the sensory hair network in a comparative context provides a framework for existing research on sensory hair function and highlights avenues for further research.

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.

Distinctive features of the central synaptic organization of Drosophila larval proprioceptors

Proprioceptive feedback is critically needed for locomotor control, but how this information is incorporated into central proprioceptive processing circuits remains poorly understood. Circuit organization emerges from the spatial distribution of synaptic connections between neurons. This distribution is difficult to discern in model systems where only a few cells can be probed simultaneously. Therefore, we turned to a relatively simple and accessible nervous system to ask: how are proprioceptors' input and output synapses organized in space, and what principles underlie this organization? Using the Drosophila larval connectome, we generated a map of the input and output synapses of 34 proprioceptors in several adjacent body segments (5-6 left-right pairs per segment). We characterized the spatial organization of these synapses, and compared this organization to that of other somatosensory neurons' synapses. We found three distinguishing features of larval proprioceptor synapses: (1) Generally, individual proprioceptor types display segmental somatotopy. (2) Proprioceptor output synapses both converge and diverge in space; they are organized into six spatial domains, each containing a unique set of one or more proprioceptors. Proprioceptors form output synapses along the proximal axonal entry pathway into the neuropil. (3) Proprioceptors receive few inhibitory input synapses. Further, we find that these three features do not apply to other larval somatosensory neurons. Thus, we have generated the most comprehensive map to date of how proprioceptor synapses are centrally organized. This map documents previously undescribed features of proprioceptors, raises questions about underlying developmental mechanisms, and has implications for downstream proprioceptive processing circuits.

Comparative Neuroanatomy of the Mechanosensory Subgenual Organ Complex in the Peruvian Stick Insect, Oreophoetes peruana

The subgenual organ complex in the leg of Polyneoptera (Insecta) consists of several chordotonal organs specialized to detect mechanical stimuli from substrate vibrations and airborne sound. In stick insects (Phasmatodea), the subgenual organ complex contains the subgenual organ and the distal organ located distally to the subgenual organ. The subgenual organ is a highly sensitive detector for substrate vibrations. The distal organ has a characteristic linear organization of sensilla and likely also responds to substrate vibrations. Despite its unique combination of sensory organs, the neuroanatomy of the subgenual organ complex of stick insects has been investigated for only very few species so far. Phylogenomic analysis has established for Phasmatodea the early branching of the sister groups Oriophasmata, the Old World phasmids, and Occidophasmata, the New World phasmids. The species studied for the sensory neuroanatomy, including the Indian stick insect Carausius morosus, belong to the Old World stick insects. Here, the neuroanatomy of the subgenual organ complex is presented for a first species of the New World stick insects, the Peruvian stick insect Oreophoetes peruana. To document the sensory organs in the subgenual organ complex and their innervation pattern, and to compare these between females and males of this species and also to the Old World stick insects, axonal tracing is used. This study documents the same sensory organs for O. peruana, subgenual organ and distal organ, as in other stick insects. Between the sexes of this species, there are no notable differences in the neuroanatomy of their sensory organs. The innervation pattern of tibial nerve branches in O. peruana is identical to other stick insect species, although the innervation pattern of the subgenual organ by a single tibial nerve branch is simpler. The shared organization of the organs in the subgenual organ complex in both groups of Neophasmatodea (Old World and New World stick insects) indicates the sensory importance of the subgenual organ but also of the distal organ. Some variation exists in the innervation of the chordotonal organs in O. peruana though a common innervation pattern can be identified. The findings raise the question for the ancestral neuroanatomical organization and innervation in stick insects.

Morphology and ultrastructure of antennal sensilla of the parasitic wasp Baryscapus dioryctriae (Hymenoptera: Eulophidae)

Baryscapus dioryctriae is an endoparasitic wasp in the pupae of many Pyralidae pests, such as Dioryctria mendacella, Ostrinia furnacalis, and Chilo suppressalis. To provide requisite background for our ongoing research on the mechanisms of host location in B. dioryctriae, the morphology, abundance, distribution, and ultrastructure of the antennal sensilla were investigated using scanning and transmission electron microscopy. The geniculate antennae of B. dioryctriae are composed of scape, pedicel, and flagellum. Eight types of sensilla including Böhm sensilla, chaetica, trichodea, basiconic capitate peg, campaniformia, placodea, coeloconica, and sensilla styloconicum with a long hair were identified on both sexes. Sexual dimorphism exists in the antennae of B. dioryctriae. The number of flagellomere in males is over females, and the subtypes and abundance of sensilla are also different between the sexes. Additionally, the possible functions of distinct sensilla were discussed, which varies from olfaction, contact chemoreceptive, mechanoreception to hygro-/thermoreception, especially, the sensilla trichodea and placodea might be involved in olfactory perception in B. dioryctriae. These results provide an essential basis for further study on chemical communication between B. dioryctriae and their hosts, and contribute to the development of B. dioryctriae becoming an effective biocontrol agent against the pests of agriculture and forestry.

MEMS-Based Micro Sensors for Measuring the Tiny Forces Acting on Insects

Small insects perform agile locomotion, such as running, jumping, and flying. Recently, many robots, inspired by such insect performance, have been developed and are expected to be smaller and more maneuverable than conventional robots. For the development of insect-inspired robots, understanding the mechanical dynamics of the target insect is important. However, evaluating the dynamics via conventional commercialized force sensors is difficult because the exerted force and insect itself are tiny in strength and size. Here, we review force sensor devices, especially fabricated for measuring the tiny forces acting on insects during locomotion. As the force sensor, micro-force plates for measuring the ground reaction force and micro-force probes for measuring the flying force have mainly been developed. In addition, many such sensors have been fabricated via a microelectromechanical system (MEMS) process, due to the process precision and high sensitivity. In this review, we focus on the sensing principle, design guide, fabrication process, and measurement method of each sensor, as well as the technical challenges in each method. Finally, the common process flow of the development of specialized MEMS sensors is briefly discussed.

Structured random receptive fields enable informative sensory encodings

Brains must represent the outside world so that animals survive and thrive. In early sensory systems, neural populations have diverse receptive fields structured to detect important features in inputs, yet significant variability has been ignored in classical models of sensory neurons. We model neuronal receptive fields as random, variable samples from parameterized distributions and demonstrate this model in two sensory modalities using data from insect mechanosensors and mammalian primary visual cortex. Our approach leads to a significant theoretical connection between the foundational concepts of receptive fields and random features, a leading theory for understanding artificial neural networks. The modeled neurons perform a randomized wavelet transform on inputs, which removes high frequency noise and boosts the signal. Further, these random feature neurons enable learning from fewer training samples and with smaller networks in artificial tasks. This structured random model of receptive fields provides a unifying, mathematically tractable framework to understand sensory encodings across both spatial and temporal domains.

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.

Vibration detection in arthropods: Signal transfer, biomechanics and sensory adaptations

In arthropods, the detection of vibrational signals and stimuli is essential in several behaviours, including mate recognition and pair formation, prey detection, and predator evasion. These behaviours have been studied in several species of insects, arachnids, and crustaceans for vibration production and propagation in the environment. Vibration stimuli are transferred over the animals' appendages and the body to vibrosensory organs. Ultimately, the stimuli are transferred to act on the dendrites of the mechanosensitive sensilla. We refer to these two different levels of transfer as macromechanics and micromechanics, respectively. These biomechanical processes have important roles in filtering and pre-processing of stimuli, which are not carried out by neuronal components of sensory organs. Also, the macromechanical transfer is posture-dependent and enables behavioural control of vibration detection. Diverse sensory organs respond to vibrations, including cuticular sensilla (slit sensilla, campaniform sensilla) and internal chordotonal organs. These organs provide various adaptations, as they occur at diverse body positions with different mechanical couplings as input pathways. Macromechanics likely facilitated evolution of vibrosensory organs at specific body locations. Thus, vibration detection is a highly complex sensory capacity, which employs body and sensory mechanics for signal filtering, amplification, and analysis of frequency, intensity and directionality.

A computational model of insect campaniform sensilla predicts encoding of forces during walking

Control of forces is essential in both animals and walking machines. Insects measure forces as strains in their exoskeletons via campaniform sensilla (CS). Deformations of cuticular caps embedded in the exoskeleton excite afferents that project to the central nervous system. CS afferent firing frequency (i.e. 'discharge') is highly dynamic, correlating with the rate of change of the force. Discharges adapt over time to tonic forces and exhibit hysteresis during cyclic loading.In this study we characterized a phenomenological model that predicts CS discharge, in which discharge is proportional to the instantaneous stimulus force relative to an adaptive variable. In contrast to previous studies of sensory adaptation, our model (1) is nonlinear and (2) reproduces the characteristic power-law adaptation with first order dynamics only (i.e. no 'fractional derivatives' are required to explain dynamics). We solve the response of the system analytically in multiple cases and use these solutions to derive the dynamics of the adaptive variable. We show that the model can reproduce responses of insect CS to many different force stimuli after being tuned to reproduce only one response, suggesting that the model captures the underlying dynamics of the system. We show that adaptation to tonic forces, rate-sensitivity, and hysteresis are different manifestations of the same underlying mechanism: the adaptive variable. We tune the model to replicate the dynamics of three different CS groups from two insects (cockroach and stick insect), demonstrating that it is generalizable. We also invert the model to estimate the stimulus force given the discharge recording from the animal. We discuss the adaptive neural and mechanical processes that the model may mimic and the model's use for understanding the role of load feedback in insect motor control. A preliminary model and results were previously published in the proceedings of the Conference on Biohybrid and Biomimetic Systems.

Wing structure and neural encoding jointly determine sensing strategies in insect flight

Animals rely on sensory feedback to generate accurate, reliable movements. In many flying insects, strain-sensitive neurons on the wings provide rapid feedback that is critical for stable flight control. While the impacts of wing structure on aerodynamic performance have been widely studied, the impacts of wing structure on sensing are largely unexplored. In this paper, we show how the structural properties of the wing and encoding by mechanosensory neurons interact to jointly determine optimal sensing strategies and performance. Specifically, we examine how neural sensors can be placed effectively on a flapping wing to detect body rotation about different axes, using a computational wing model with varying flexural stiffness. A small set of mechanosensors, conveying strain information at key locations with a single action potential per wingbeat, enable accurate detection of body rotation. Optimal sensor locations are concentrated at either the wing base or the wing tip, and they transition sharply as a function of both wing stiffness and neural threshold. Moreover, the sensing strategy and performance is robust to both external disturbances and sensor loss. Typically, only five sensors are needed to achieve near-peak accuracy, with a single sensor often providing accuracy well above chance. Our results show that small-amplitude, dynamic signals can be extracted efficiently with spatially and temporally sparse sensors in the context of flight. The demonstrated interaction of wing structure and neural encoding properties points to the importance of understanding each in the context of their joint evolution.

In addition to generating forces for flight, insect wings also serve an important role as sensory structures, providing rapid feedback about wing bending that is used to stabilize flight. While much is known about how wing structure affects aerodynamic performance, the effects of wing structure on sensing remain unexplored. Using a computational model of a flapping wing, we examine how sensing strategies depend on wing stiffness and sensor properties. We show that body rotations can be accurately detected with a small number of sensors on the wing across a wide range of conditions. Optimal sensor locations are clustered at either the wing base or wing tip, depending on a combination of wing stiffness and sensor properties. Moreover, sensing performance is robust to multiple kinds of perturbations. Our work provides a basis for understanding how wing structure impacts incoming sensory information during flight.

A leg to stand on: computational models of proprioception

Dexterous motor control requires feedback from proprioceptors, internal mechanosensory neurons that sense the body's position and movement. An outstanding question in neuroscience is how diverse proprioceptive feedback signals contribute to flexible motor control. Genetic tools now enable targeted recording and perturbation of proprioceptive neurons in behaving animals; however, these experiments can be challenging to interpret, due to the tight coupling of proprioception and motor control. Here, we argue that understanding the role of proprioceptive feedback in controlling behavior will be aided by the development of multiscale models of sensorimotor loops. We review current phenomenological and structural models for proprioceptor encoding and discuss how they may be integrated with existing models of posture, movement, and body state estimation.