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Leonardo Lab / Publications
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23 Publications

Showing 1-10 of 23 results
05/13/22 | Recovery mechanisms in the dragonfly righting reflex.
Wang ZJ, Melfi J, Leonardo A
Science. 2022 May 13;376(6594):754-758. doi: 10.1126/science.abg0946

Insects have evolved sophisticated reflexes to right themselves in mid-air. Their recovery mechanisms involve complex interactions among the physical senses, muscles, body, and wings, and they must obey the laws of flight. We sought to understand the key mechanisms involved in dragonfly righting reflexes and to develop physics-based models for understanding the control strategies of flight maneuvers. Using kinematic analyses, physical modeling, and three-dimensional flight simulations, we found that a dragonfly uses left-right wing pitch asymmetry to roll its body 180 degrees to recover from falling upside down in ~200 milliseconds. Experiments of dragonflies with blocked vision further revealed that this rolling maneuver is initiated by their ocelli and compound eyes. These results suggest a pathway from the dragonfly's visual system to the muscles regulating wing pitch that underly the recovery. The methods developed here offer quantitative tools for inferring insects' internal actions from their acrobatics, and are applicable to a broad class of natural and robotic flying systems.

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Jumping in planthopper and froghopper insects is propelled by a catapult-like mechanism requiring mechanical storage of energy and its quick release to accelerate the hind legs rapidly. To understand the functional biomechanics involved in these challenging movements, the internal skeleton, tendons and muscles involved were reconstructed in 3-D from confocal scans in unprecedented detail. Energy to power jumping was generated by slow contractions of hind leg depressor muscles and then stored by bending specialised elements of the thoracic skeleton that are composites of the rubbery protein resilin sandwiched between layers of harder cuticle with air-filled tunnels reducing mass. The images showed that the lever arm of the power-producing muscle changed in magnitude during jumping, but at all joint angles would cause depression, suggesting a mechanism by which the stored energy is released. This methodological approach illuminates how miniaturized components interact and function in complex and rapid movements of small animals.

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03/28/17 | Heuristic rules underlying dragonfly prey selection and interception.
Lin H, Leonardo A
Current Biology : CB. 2017 Mar 28;27(8):1124-37. doi: 10.1016/j.cub.2017.03.010

Animals use rules to initiate behaviors. Such rules are often described as triggers that determine when behavior begins. However, although less explored, these selection rules are also an opportunity to establish sensorimotor constraints that influence how the behavior ends. These constraints may be particularly significant in influencing success in prey capture. Here we explore this in dragonfly prey interception. We found that in the moments leading up to takeoff, perched dragonflies employ a series of sensorimotor rules that determine the time of takeoff and increase the probability of successful capture. First, the dragonfly makes a head saccade followed by smooth pursuit movements to orient its direction-of-gaze at potential prey. Second, the dragonfly assesses whether the prey's angular size and speed co-vary within a privileged range. Finally, the dragonfly times the moment of its takeoff to a prediction of when the prey will cross the zenith. Each of these processes serves a purpose. The angular size-speed criteria biases interception flights to catchable prey, while the head movements and the predictive takeoff ensure flights begin with the prey visually fixated and directly overhead-the key parameters that underlie interception steering. Prey that do not elicit takeoff generally fail at least one of the criterion, and the loss of prey fixation or overhead positioning during flight is strongly correlated with terminated flights. Thus from an abundance of potential targets, the dragonfly selects a stereotyped set of takeoff conditions based on the prey and body states most likely to end in successful capture.

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11/16/16 | The genome of the crustacean Parhyale hawaiensis: a model for animal development, regeneration, immunity and lignocellulose digestion.
Kao D, Lai AG, Stamataki E, Rosic S, Konstantinides N, Jarvis E, Di Donfrancesco A, Pouchkina-Stantcheva N, Semon M, Grillo M, Bruce H, Kumar S, Siwanowicz I, Le A, Lemire A, Extavour C, Browne W, Wolff C, Averof M, et al
eLife. 2016 Nov 16;5:e20062. doi: 10.7554/eLife.20062

Parhyale hawaiensis is a blossoming model system for studies of developmental mechanisms and more recently adult regeneration. We have sequenced the genome allowing annotation of all key signaling pathways, small non-coding RNAs and transcription factors that will enhance ongoing functional studies. Parhayle is a member of the Malacostraca, which includes crustacean food crop species. We analysed the immunity related genes of Parhyale as an important comparative system for these species, where immunity related aquaculture problems have increased as farming has intensified. We also find that Parhyale and other species within Multicrustacea contain the enzyme sets necessary to perform lignocellulose digestion (wood eating), suggesting this ability may predate the diversification of this lineage. Our data provide an essential resource for further development of the Parhyale model. The first Malacostracan genome sequence will underpin ongoing comparative work in important food crop species and research investigating lignocellulose as energy source.

Publication first appeared in BioRxiv on August 2, 2016.

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09/26/16 | Flight of the dragonflies and damselflies.
Bomphrey RJ, Nakata T, Henningsson P, Lin H
Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences. 2016 Sep 26;371(1704):. doi: 10.1098/rstb.2015.0389

This work is a synthesis of our current understanding of the mechanics, aerodynamics and visually mediated control of dragonfly and damselfly flight, with the addition of new experimental and computational data in several key areas. These are: the diversity of dragonfly wing morphologies, the aerodynamics of gliding flight, force generation in flapping flight, aerodynamic efficiency, comparative flight performance and pursuit strategies during predatory and territorial flights. New data are set in context by brief reviews covering anatomy at several scales, insect aerodynamics, neuromechanics and behaviour. We achieve a new perspective by means of a diverse range of techniques, including laser-line mapping of wing topographies, computational fluid dynamics simulations of finely detailed wing geometries, quantitative imaging using particle image velocimetry of on-wing and wake flow patterns, classical aerodynamic theory, photography in the field, infrared motion capture and multi-camera optical tracking of free flight trajectories in laboratory environments. Our comprehensive approach enables a novel synthesis of datasets and subfields that integrates many aspects of flight from the neurobiology of the compound eye, through the aeromechanical interface with the surrounding fluid, to flight performance under cruising and higher-energy behavioural modes.This article is part of the themed issue 'Moving in a moving medium: new perspectives on flight'.

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11/18/15 | The role of motion extrapolation in amphibian prey capture.
Borghuis BG, Leonardo A
The Journal of Neuroscience : the official journal of the Society for Neuroscience. 2015 Nov 18;35(46):15430-41. doi: 10.1523/JNEUROSCI.3189-15.2015

UNLABELLED: Sensorimotor delays decouple behaviors from the events that drive them. The brain compensates for these delays with predictive mechanisms, but the efficacy and timescale over which these mechanisms operate remain poorly understood. Here, we assess how prediction is used to compensate for prey movement that occurs during visuomotor processing. We obtained high-speed video records of freely moving, tongue-projecting salamanders catching walking prey, emulating natural foraging conditions. We found that tongue projections were preceded by a rapid head turn lasting ∼130 ms. This motor lag, combined with the ∼100 ms phototransduction delay at photopic light levels, gave a ∼230 ms visuomotor response delay during which prey typically moved approximately one body length. Tongue projections, however, did not significantly lag prey position but were highly accurate instead. Angular errors in tongue projection accuracy were consistent with a linear extrapolation model that predicted prey position at the time of tongue contact using the average prey motion during a ∼175 ms period one visual latency before the head movement. The model explained successful strikes where the tongue hit the fly, and unsuccessful strikes where the fly turned and the tongue hit a phantom location consistent with the fly's earlier trajectory. The model parameters, obtained from the data, agree with the temporal integration and latency of retinal responses proposed to contribute to motion extrapolation. These results show that the salamander predicts future prey position and that prediction significantly improves prey capture success over a broad range of prey speeds and light levels.

SIGNIFICANCE STATEMENT: Neural processing delays cause actions to lag behind the events that elicit them. To cope with these delays, the brain predicts what will happen in the future. While neural circuits in the retina and beyond have been suggested to participate in such predictions, few behaviors have been explored sufficiently to constrain circuit function. Here we show that salamanders aim their tongues by using extrapolation to estimate future prey position, thereby compensating for internal delays from both visual and motor processing. Predictions made just before a prey turn resulted in the tongue being projected to a position consistent with the prey's pre-turn trajectory. These results define the computations and operating regimen for neural circuits that predict target motion.

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02/01/15 | Theta sequences are essential for internally generated hippocampal firing fields.
Wang Y, Romani S, Lustig B, Leonardo A, Pastalkova E
Nature Neuroscience. 2015 Feb;18(2):282-8. doi: 10.1038/nn.3904

Sensory cue inputs and memory-related internal brain activities govern the firing of hippocampal neurons, but which specific firing patterns are induced by either of the two processes remains unclear. We found that sensory cues guided the firing of neurons in rats on a timescale of seconds and supported the formation of spatial firing fields. Independently of the sensory inputs, the memory-related network activity coordinated the firing of neurons not only on a second-long timescale, but also on a millisecond-long timescale, and was dependent on medial septum inputs. We propose a network mechanism that might coordinate this internally generated firing. Overall, we suggest that two independent mechanisms support the formation of spatial firing fields in hippocampus, but only the internally organized system supports short-timescale sequential firing and episodic memory.

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12/10/14 | Internal models direct dragonfly interception steering.
Mischiati M, Lin H, Herold P, Imler E, Olberg R, Leonardo A
Nature. 2014 Dec 10:. doi: 10.1038/nature14045

Sensorimotor control in vertebrates relies on internal models. When extending an arm to reach for an object, the brain uses predictive models of both limb dynamics and target properties. Whether invertebrates use such models remains unclear. Here we examine to what extent prey interception by dragonflies (Plathemis lydia), a behaviour analogous to targeted reaching, requires internal models. By simultaneously tracking the position and orientation of a dragonfly's head and body during flight, we provide evidence that interception steering is driven by forward and inverse models of dragonfly body dynamics and by models of prey motion. Predictive rotations of the dragonfly's head continuously track the prey's angular position. The head-body angles established by prey tracking appear to guide systematic rotations of the dragonfly's body to align it with the prey's flight path. Model-driven control thus underlies the bulk of interception steering manoeuvres, while vision is used for reactions to unexpected prey movements. These findings illuminate the computational sophistication with which insects construct behaviour.

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12/01/14 | Bone-free: soft mechanics for adaptive locomotion.
Trimmer BA, Lin H
Integrative and Comparative Biology. 2014 Dec;54(6):1122-35. doi: 10.1093/icb/icu076

Muscular hydrostats (such as mollusks), and fluid-filled animals (such as annelids), can exploit their constant-volume tissues to transfer forces and displacements in predictable ways, much as articulated animals use hinges and levers. Although larval insects contain pressurized fluids, they also have internal air tubes that are compressible and, as a result, they have more uncontrolled degrees of freedom. Therefore, the mechanisms by which larval insects control their movements are expected to reveal useful strategies for designing soft biomimetic robots. Using caterpillars as a tractable model system, it is now possible to identify the biomechanical and neural strategies for controlling movements in such highly deformable animals. For example, the tobacco hornworm, Manduca sexta, can stiffen its body by increasing muscular tension (and therefore body pressure) but the internal cavity (hemocoel) is not iso-barometric, nor is pressure used to directly control the movements of its limbs. Instead, fluid and tissues flow within the hemocoel and the body is soft and flexible to conform to the substrate. Even the gut contributes to the biomechanics of locomotion; it is decoupled from the movements of the body wall and slides forward within the body cavity at the start of each step. During crawling the body is kept in tension for part of the stride and compressive forces are exerted on the substrate along the axis of the caterpillar, thereby using the environment as a skeleton. The timing of muscular activity suggests that crawling is coordinated by proleg-retractor motoneurons and that the large segmental muscles produce anterograde waves of lifting that do not require precise timing. This strategy produces a robust form of locomotion in which the kinematics changes little with orientation. In different species of caterpillar, the presence of prolegs on particular body segments is related to alternative kinematics such as "inching." This suggests a mechanism for the evolution of different gaits through changes in the usage of prolegs, rather than, through extensive alterations in the motor program controlling the body wall. Some of these findings are being used to design and test novel control-strategies for highly deformable robots. These "softworm" devices are providing new insights into the challenges faced by any soft animal navigating in a terrestrial environment.

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07/17/14 | A spike-timing mechanism for action selection.
von Reyn CR, Breads P, Peek MY, Zheng GZ, Williamson WR, Yee AL, Leonardo A, Card GM
Nature Neuroscience. 2014 Jul 17;17(7):962-70. doi: 10.1038/nn.3741

We discovered a bimodal behavior in the genetically tractable organism Drosophila melanogaster that allowed us to directly probe the neural mechanisms of an action selection process. When confronted by a predator-mimicking looming stimulus, a fly responds with either a long-duration escape behavior sequence that initiates stable flight or a distinct, short-duration sequence that sacrifices flight stability for speed. Intracellular recording of the descending giant fiber (GF) interneuron during head-fixed escape revealed that GF spike timing relative to parallel circuits for escape actions determined which of the two behavioral responses was elicited. The process was well described by a simple model in which the GF circuit has a higher activation threshold than the parallel circuits, but can override ongoing behavior to force a short takeoff. Our findings suggest a neural mechanism for action selection in which relative activation timing of parallel circuits creates the appropriate motor output.

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