Intraspecific variation in the placement of campaniform sensilla on the wings of the hawkmoth Manduca sexta.

Flight control requires active sensory feedback, and insects have many sensors that help them estimate their current locomotor state, including campaniform sensilla, which are mechanoreceptors that sense strain resulting from deformation of the cuticle. Campaniform sensilla on the wing detect bending and torsional forces encountered during flight, providing input to the flight feedback control system. During flight, wings experience complex spatio-temporal strain patterns. Because campaniform sensilla detect only local strain, their placement on the wing is presumably critical for determining the overall representation of wing deformation; however, how these sensilla are distributed across wings is largely unknown. Here, we test the hypothesis that campaniform sensilla are found in stereotyped locations across individuals of Manduca sexta, a hawkmoth. We found that although campaniform sensilla are consistently found on the same veins or in the same regions of the wings, their total number and distribution can vary extensively. This suggests that there is some robustness to variation in sensory feedback in the insect flight control system. The regions where campaniform sensilla are consistently found provide clues to their functional roles, although some patterns might be reflective of developmental processes. Collectively, our results on intraspecific variation in campaniform sensilla placement on insect wings will help reshape our thinking on the utility of mechanosensory feedback for insect flight control and guide further experimental and comparative studies.

A complete biomechanical model of Hydra contractile behaviors, from neural drive to muscle to movement.

How does neural activity drive muscles to produce behavior? The recent development of genetic lines in Hydra that allow complete calcium imaging of both neuronal and muscle activity, as well as systematic machine learning quantification of behaviors, makes this small cnidarian an ideal model system to understand and model the complete transformation from neural firing to body movements. To achieve this, we have built a neuromechanical model of Hydra's fluid-filled hydrostatic skeleton, showing how drive by neuronal activity activates distinct patterns of muscle activity and body column biomechanics. Our model is based on experimental measurements of neuronal and muscle activity and assumes gap junctional coupling among muscle cells and calcium-dependent force generation by muscles. With these assumptions, we can robustly reproduce a basic set of Hydra's behaviors. We can further explain puzzling experimental observations, including the dual timescale kinetics observed in muscle activation and the engagement of ectodermal and endodermal muscles in different behaviors. This work delineates the spatiotemporal control space of Hydra movement and can serve as a template for future efforts to systematically decipher the transformations in the neural basis of behavior.

Nonuniform structural properties of wings confer sensing advantages.

Sensory feedback is essential to both animals and robotic systems for achieving coordinated, precise movements. Mechanosensory feedback, which provides information about body deformation, depends not only on the properties of sensors but also on the structure in which they are embedded. In insects, wing structure plays a particularly important role in flapping flight: in addition to generating aerodynamic forces, wings provide mechanosensory feedback necessary for guiding flight while undergoing dramatic deformations during each wingbeat. However, the role that wing structure plays in determining mechanosensory information is relatively unexplored. Insect wings exhibit characteristic stiffness gradients and are subject to both aerodynamic and structural damping. Here we examine how both of these properties impact sensory performance, using finite element analysis combined with sensor placement optimization approaches. We show that wings with nonuniform stiffness exhibit several advantages over uniform stiffness wings, resulting in higher accuracy of rotation detection and lower sensitivity to the placement of sensors on the wing. Moreover, we show that higher damping generally improves the accuracy with which body rotations can be detected. These results contribute to our understanding of the evolution of the nonuniform stiffness patterns in insect wings, as well as suggest design principles for robotic systems.

Abdominal Movements in Insect Flight Reshape the Role of Non-Aerodynamic Structures for Flight Maneuverability I: Model Predictive Control for Flower Tracking.

Research on insect flight control has focused primarily on the role of wings. Yet abdominal deflections during flight can potentially influence the dynamics of flight. This paper assesses the role of airframe deformations in flight, and asks to what extent the abdomen contributes to flight maneuverability. To address this, we use a combination of both a Model Predictive Control (MPC)-inspired computational inertial dynamics model, and free flight experiments in the hawkmoth, Manduca sexta. We explored both underactuated (i.e., number of outputs are greater than the number of inputs) and fully actuated (equal number of outputs and inputs) systems. Using metrics such as the non-dimensionalized tracking error and cost of transport to evaluate flight performance of the inertial dynamics model, we show that fully actuated simulations minimized the tracking error and cost of transport. Additionally, we tested the effect of restricted abdomen movement on free flight in live hawkmoths by fixing a carbon fiber rod over the thoracic-abdomen joint. Moths with a restricted abdomen performed worse than sham treatment moths. This study finds that abdominal motions contribute to flight control and maneuverability. Such motions of non-aerodynamic structures, found in all flying taxa, can inform the development of multi-actuated micro air vehicles.

Pruning deep neural networks generates a sparse, bio-inspired nonlinear controller for insect flight.

Insect flight is a strongly nonlinear and actuated dynamical system. As such, strategies for understanding its control have typically relied on either model-based methods or linearizations thereof. Here we develop a framework that combines model predictive control on an established flight dynamics model and deep neural networks (DNN) to create an efficient method for solving the inverse problem of flight control. We turn to natural systems for inspiration since they inherently demonstrate network pruning with the consequence of yielding more efficient networks for a specific set of tasks. This bio-inspired approach allows us to leverage network pruning to optimally sparsify a DNN architecture in order to perform flight tasks with as few neural connections as possible, however, there are limits to sparsification. Specifically, as the number of connections falls below a critical threshold, flight performance drops considerably. We develop sparsification paradigms and explore their limits for control tasks. Monte Carlo simulations also quantify the statistical distribution of network weights during pruning given initial random weights of the DNNs. We demonstrate that on average, the network can be pruned to retain a small amount of original network weights and still perform comparably to its fully-connected counterpart. The relative number of remaining weights, however, is highly dependent on the initial architecture and size of the network. Overall, this work shows that sparsely connected DNNs are capable of predicting the forces required to follow flight trajectories. Additionally, sparsification has sharp performance limits.

Wind dispersal of battery-free wireless devices.

Plants cover a large fraction of the Earth's land mass despite most species having limited to no mobility. To transport their propagules, many plants have evolved mechanisms to disperse their seeds using the wind1-4. A dandelion seed, for example, has a bristly filament structure that decreases its terminal velocity and helps orient the seed as it wafts to the ground5. Inspired by this, we demonstrate wind dispersal of battery-free wireless sensing devices. Our millimetre-scale devices weigh 30 milligrams and are designed on a flexible substrate using programmable, off-the-shelf parts to enable scalability and flexibility for various sensing and computing applications. The system is powered using lightweight solar cells and an energy harvesting circuit that is robust to low and variable light conditions, and has a backscatter communication link that enables data transmission. To achieve the wide-area dispersal and upright landing that is necessary for solar power harvesting, we developed dandelion-inspired, thin-film porous structures that achieve a terminal velocity of 0.87 ± 0.02 metres per second and aerodynamic stability with a probability of upright landing of over 95%. Our results in outdoor environments demonstrate that these devices can travel 50-100 metres in gentle to moderate breeze. Finally, in natural systems, variance in individual seed morphology causes some seeds to fall closer and others to travel farther. We adopt a similar approach and show how we can modulate the porosity and diameter of the structures to achieve dispersal variation across devices.

Learning to feed in the dark: how light level influences feeding in the hawkmoth Manduca sexta.

Nocturnal insects like moths are essential for pollination, providing resilience to the diurnal pollination networks. Moths use both vision and mechanosensation to locate the nectary opening in the flowers with their proboscis. However, increased light levels due to artificial light at night (ALAN) pose a serious threat to nocturnal insects. Here, we examined how light levels influence the efficacy by which the crepuscular hawkmoth Manduca sexta locates the nectary. We used three-dimensional-printed artificial flowers fitted with motion sensors in the nectary and machine vision to track the motion of hovering moths under two light levels: 0.1 lux (moonlight) and 50 lux (dawn/dusk). We found that moths in higher light conditions took significantly longer to find the nectary, even with repeated visits to the same flower. In addition to taking longer, moths in higher light conditions hovered further from the flower during feeding. Increased light levels adversely affect learning and motor control in these animals.

A mechanism for sarcomere breathing: volume change and advective flow within the myofilament lattice.

During muscle contraction, myosin motors anchored to thick filaments bind to and slide actin thin filaments. These motors rely on energy derived from ATP, supplied, in part, by diffusion from the sarcoplasm to the interior of the lattice of actin and myosin filaments. The radial spacing of filaments in this lattice may change or remain constant during contraction. If the lattice is isovolumetric, it must expand when the muscle shortens. If, however, the spacing is constant or has a different pattern of axial and radial motion, then the lattice changes volume during contraction, driving fluid motion and assisting in the transport of molecules between the contractile lattice and the surrounding intracellular space. We first create an advective-diffusive-reaction flow model and show that the flow into and out of the sarcomere lattice would be significant in the absence of lattice expansion. Advective transport coupled to diffusion has the potential to substantially enhance metabolite exchange within the crowded sarcomere. Using time-resolved x-ray diffraction of contracting muscle, we next show that the contractile lattice is neither isovolumetric nor constant in spacing. Instead, lattice spacing is time varying, depends on activation, and can manifest as an effective time-varying Poisson ratio. The resulting fluid flow in the sarcomere lattice of synchronous insect flight muscles is even greater than expected for constant lattice spacing conditions. Lattice spacing depends on a variety of factors that produce radial force, including cross-bridges, titin-like molecules, and other structural proteins. Volume change and advective transport varies with the phase of muscle stimulation during periodic contraction but remains significant at all conditions. Although varying in magnitude, advective transport will occur in all cases in which the sarcomere is not isovolumetric. Akin to "breathing," advective-diffusive transport in sarcomeres is sufficient to promote metabolite exchange and may play a role in the regulation of contraction itself.

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.

Fluid flow in the sarcomere.

A highly organized and densely packed lattice of molecular machinery within the sarcomeres of muscle cells powers contraction. Although many of the proteins that drive contraction have been studied extensively, the mechanical impact of fluid shearing within the lattice of molecular machinery has received minimal attention. It was recently proposed that fluid flow augments substrate transport in the sarcomere, however, this analysis used analytical models of fluid flow in the molecular machinery that could not capture its full complexity. By building a finite element model of the sarcomere, we estimate the explicit flow field, and contrast it with analytical models. Our results demonstrate that viscous drag forces on sliding filaments are surprisingly small in contrast to the forces generated by single myosin molecular motors. This model also indicates that the energetic cost of fluid flow through viscous shearing with lattice proteins is likely minimal. The model also highlights a steep velocity gradient between sliding filaments and demonstrates that the maximal radial fluid velocity occurs near the tips of the filaments. To our knowledge, this is the first computational analysis of fluid flow within the highly structured sarcomere.

The Sliding Filament Theory Since Andrew Huxley: Multiscale and Multidisciplinary Muscle Research.

Two groundbreaking papers published in 1954 laid out the theory of the mechanism of muscle contraction based on force-generating interactions between myofilaments in the sarcomere that cause filaments to slide past one another during muscle contraction. The succeeding decades of research in muscle physiology have revealed a unifying interest: to understand the multiscale processes-from atom to organ-that govern muscle function. Such an understanding would have profound consequences for a vast array of applications, from developing new biomimetic technologies to treating heart disease. However, connecting structural and functional properties that are relevant at one spatiotemporal scale to those that are relevant at other scales remains a great challenge. Through a lens of multiscale dynamics, we review in this article current and historical research in muscle physiology sparked by the sliding filament theory.

Tactile active sensing in an insect plant pollinator.

The interaction between insects and the flowers they pollinate has driven the evolutionary diversity of both insects and flowering plants, two groups with the most numerous species on Earth. Insects use vision and olfaction to localize host plants, but we know relatively little about how they find the tiny nectary opening in the flower, which can be well beyond their visual resolution. Especially when vision is limited, touch becomes crucial in successful insect-plant pollination interactions. Here, we studied the remarkable feeding behavior of crepuscular hawkmoths Manduca sexta, which use their long, actively controlled, proboscis to expertly explore flower-like surfaces. Using machine vision and 3D-printed artificial flower-like feeders, we revealed a novel behavior that shows moths actively probe surfaces, sweeping their proboscis from the feeder edge to its center repeatedly until they locate the nectary opening. Moreover, naive moths rapidly learn to exploit these flowers, and they adopt a tactile search strategy to more directly locate the nectary opening in as few as three to five consecutive visits. Our results highlight the proboscis as a unique active sensory structure and emphasize the central role of touch in nectar foraging insect-plant pollinator interactions.

A bio-hybrid odor-guided autonomous palm-sized air vehicle.

Biohybrid systems integrate living materials with synthetic devices, exploiting their respective advantages to solve challenging engineering problems. One challenge of critical importance to society is detecting and localizing airborne volatile chemicals. Many flying animals depend their ability to detect and locate the source of aerial chemical plumes for finding mates and food sources. A robot with comparable capability could reduce human hazard and drastically improve performance on tasks such as locating disaster survivors, hazardous gas leaks, incipient fires, or explosives. Three advances are needed before they can rival their biological counterparts: (1) a chemical sensor with a much faster response time that nevertheless satisfies the size, weight, and power constraints of flight, (2) a design, sensor suite, and control system that allows it to move toward the source of a plume fully autonomously while navigating obstacles, and (3) the ability to detect the plume with high specificity and sensitivity among the assortment of chemicals that invariably exist in the air. Here we address the first two, introducing a human-safe palm-sized air vehicle equipped with the odor-sensing antenna of an insect, the first odor-sensing biohybrid robot system to fly. Using this sensor along with a suite of additional navigational sensors, as well as passive wind fins, our robot orients upwind and navigates autonomously toward the source of airborne plumes. Our robot is the first flying biohybrid system to successfully perform odor localization in a confined space, and it is able to do so while detecting and avoiding obstacles in its flight path. We show that insect antennae respond more quickly than metal oxide gas sensors, enabling odor localization at an improved speed over previous flying robots. By using the insect antennae, we anticipate a feasible path toward improved chemical specificity and sensitivity by leveraging recent advances in gene editing.

In vivo X-ray diffraction and simultaneous EMG reveal the time course of myofilament lattice dilation and filament stretch.

Muscle function within an organism depends on the feedback between molecular and meter-scale processes. Although the motions of muscle's contractile machinery are well described in isolated preparations, only a handful of experiments have documented the kinematics of the lattice occurring when multi-scale interactions are fully intact. We used time-resolved X-ray diffraction to record the kinematics of the myofilament lattice within a normal operating context: the tethered flight of Manduca sexta As the primary flight muscles of M.sexta are synchronous, we used these results to reveal the timing of in vivo cross-bridge recruitment, which occurred 24 ms (s.d. 26) following activation. In addition, the thick filaments stretched an average of 0.75% (s.d. 0.32) and thin filaments stretched 1.11% (s.d. 0.65). In contrast to other in vivo preparations, lattice spacing changed an average of 2.72% (s.d. 1.47). Lattice dilation of this magnitude significantly affects shortening velocity and force generation, and filament stretching tunes force generation. While the kinematics were consistent within individual trials, there was extensive variation between trials. Using a mechanism-free machine learning model we searched for patterns within and across trials. Although lattice kinematics were predictable within trials, the model could not create predictions across trials. This indicates that the variability we see across trials may be explained by latent variables occurring in this naturally functioning system. The diverse kinematic combinations we documented mirror muscle's adaptability and may facilitate its robust function in unpredictable conditions.

Morphospace exploration reveals divergent fitness optima between plants and pollinators.

The obligate mutualism and exquisite specificity of many plant-pollinator interactions lead to the expectation that flower phenotypes (e.g., corolla tube length) and corresponding pollinator traits (e.g., hawkmoth proboscis length) are congruent as a result of coevolution by natural selection. However, the effect of variation in flower morphology on the fitness of plants and their pollinators has not been quantified systematically. In this study, we employed the theoretical morphospace paradigm using a combination of 3D printing, electronic sensing, and machine vision technologies to determine the influence of two flower morphological features (corolla curvature and nectary diameter) on the fitness of both parties: the artificial flower and its hawkmoth pollinator. Contrary to the expectation that the same flower morphology maximizes the fitness of both plant and pollinator, we found that the two parties have divergent optima for corolla curvature, with non-overlapping fitness peaks in flower morphospace. The divergent fitness optima between plants and pollinators could lead to evolutionary diversification in both groups.

Neural-inspired sensors enable sparse, efficient classification of spatiotemporal data.

Sparse sensor placement is a central challenge in the efficient characterization of complex systems when the cost of acquiring and processing data is high. Leading sparse sensing methods typically exploit either spatial or temporal correlations, but rarely both. This work introduces a sparse sensor optimization that is designed to leverage the rich spatiotemporal coherence exhibited by many systems. Our approach is inspired by the remarkable performance of flying insects, which use a few embedded strain-sensitive neurons to achieve rapid and robust flight control despite large gust disturbances. Specifically, we identify neural-inspired sensors at a few key locations on a flapping wing that are able to detect body rotation. This task is particularly challenging as the rotational twisting mode is three orders of magnitude smaller than the flapping modes. We show that nonlinear filtering in time, built to mimic strain-sensitive neurons, is essential to detect rotation, whereas instantaneous measurements fail. Optimized sparse sensor placement results in efficient classification with approximately 10 sensors, achieving the same accuracy and noise robustness as full measurements consisting of hundreds of sensors. Sparse sensing with neural-inspired encoding establishes an alternative paradigm in hyperefficient, embodied sensing of spatiotemporal data and sheds light on principles of biological sensing for agile flight control.

A Spatially Explicit Model Shows How Titin Stiffness Modulates Muscle Mechanics and Energetics.

In striated muscle, the giant protein titin spans the entire length of a half-sarcomere and extends from the backbone of the thick filament, reversibly attaches to the thin filaments, and anchors to the dense protein network of the z-disk capping the end of the half-sarcomere. However, little is known about the relationship between the basic mechanical properties of titin and muscle contractility. Here, we build upon our previous multi-filament, spatially explicit computational model of the half-sarcomere by incorporating the nonlinear mechanics of titin filaments in the I-band. We vary parameters of the nonlinearity to understand the effects of titin stiffness on contraction dynamics and efficiency. We do so by simulating isometric contraction for a range of sarcomere lengths (SLs; 1.6-3.25 µm). Intermediate values of titin stiffness accurately reproduce the passive force-SL relation for skeletal muscle. The maximum force-SL relation is not affected by titin for SL≤2.5 µm. However, as titin stiffness increases, maximum force for the four thick filament system at SL = 3.0 µm significantly decreases from 103.2 ± 2 to 58.8 ± 1 pN. Additionally, by monitoring ATP consumption, we measure contraction efficiency as a function of titin stiffness. We find that at SL = 3.0 µm, efficiency significantly decreases from 13.9 ± 0.4 to 7.0 ± 0.3 pN/ATP when increasing titin stiffness, with little or no effect below 2.5 µm. Taken together, our results suggest that, despite an increase in the fraction of motors bound to actin along the descending limb when titin is stiffer, the force-generating capacity of the motors is reduced. These results suggest that titin stiffness has the potential to affect contractile efficiency.

Integration of parallel mechanosensory and visual pathways resolved through sensory conflict.

The acquisition of information from parallel sensory pathways is a hallmark of coordinated movement in animals. Insect flight, for example, relies on both mechanosensory and visual pathways. Our challenge is to disentangle the relative contribution of each modality to the control of behavior. Toward this end, we show an experimental and analytical framework leveraging sensory conflict, a means for independently exciting and modeling separate sensory pathways within a multisensory behavior. As a model, we examine the hovering flower-feeding behavior in the hawkmoth Manduca sexta In the laboratory, moths feed from a robotically actuated two-part artificial flower that allows independent presentation of visual and mechanosensory cues. Freely flying moths track lateral flower motion stimuli in an assay spanning both coupled motion, in which visual and mechanosensory cues follow the same motion trajectory, and sensory conflict, in which the two sensory modalities encode different motion stimuli. Applying a frequency-domain system identification analysis, we find that the tracking behavior is, in fact, multisensory and arises from a linear summation of visual and mechanosensory pathways. The response dynamics are highly preserved across individuals, providing a model for predicting the response to novel multimodal stimuli. Surprisingly, we find that each pathway in and of itself is sufficient for driving tracking behavior. When multiple sensory pathways elicit strong behavioral responses, this parallel architecture furnishes robustness via redundancy.

INSECT FLIGHT. Luminance-dependent visual processing enables moth flight in low light.

Animals must operate under an enormous range of light intensities. Nocturnal and twilight flying insects are hypothesized to compensate for dim conditions by integrating light over longer times. This slowing of visual processing would increase light sensitivity but should also reduce movement response times. Using freely hovering moths tracking robotic moving flowers, we showed that the moth's visual processing does slow in dim light. These longer response times are consistent with models of how visual neurons enhance sensitivity at low light intensities, but they could pose a challenge for moths feeding from swaying flowers. Dusk-foraging moths avoid this sensorimotor tradeoff; their nervous systems slow down but not so much as to interfere with their ability to track the movements of real wind-blown flowers.

Dual dimensionality reduction reveals independent encoding of motor features in a muscle synergy for insect flight control.

What are the features of movement encoded by changing motor commands? Do motor commands encode movement independently or can they be represented in a reduced set of signals (i.e. synergies)? Motor encoding poses a computational and practical challenge because many muscles typically drive movement, and simultaneous electrophysiology recordings of all motor commands are typically not available. Moreover, during a single locomotor period (a stride or wingstroke) the variation in movement may have high dimensionality, even if only a few discrete signals activate the muscles. Here, we apply the method of partial least squares (PLS) to extract the encoded features of movement based on the cross-covariance of motor signals and movement. PLS simultaneously decomposes both datasets and identifies only the variation in movement that relates to the specific muscles of interest. We use this approach to explore how the main downstroke flight muscles of an insect, the hawkmoth Manduca sexta, encode torque during yaw turns. We simultaneously record muscle activity and turning torque in tethered flying moths experiencing wide-field visual stimuli. We ask whether this pair of muscles acts as a muscle synergy (a single linear combination of activity) consistent with their hypothesized function of producing a left-right power differential. Alternatively, each muscle might individually encode variation in movement. We show that PLS feature analysis produces an efficient reduction of dimensionality in torque variation within a wingstroke. At first, the two muscles appear to behave as a synergy when we consider only their wingstroke-averaged torque. However, when we consider the PLS features, the muscles reveal independent encoding of torque. Using these features we can predictably reconstruct the variation in torque corresponding to changes in muscle activation. PLS-based feature analysis provides a general two-sided dimensionality reduction that reveals encoding in high dimensional sensory or motor transformations.