@article {69667, title = {Wireless recordings from dragonfly target detecting neurons during prey interception flight}, journal = {bioRxiv}, year = {2024}, month = {2024 Nov 13}, abstract = {

Target interception is a complex sensorimotor behavior which requires fine tuning of the sensory system and its strategic coordination with the motor system. Despite various theories about how interception is achieved, its neural implementation remains unknown. We have previously shown that hunting dragonflies employ a balance of reactive and predictive control to intercept prey, using sophisticated model driven predictions to account for expected prey and self-motion. Here we explore the neural substrate of this interception system by investigating a well-known class of target-selective descending neurons (TSDNs). These cells have long been speculated to underlie interception steering but have never been studied in a behaving dragonfly. We combined detailed neuroanatomy, high-precision kinematics data and state-of-the-art neural telemetry to measure TSDN activity during flight. We found that TSDNs are exquisitely tuned to prey angular size and speed at ethological distances, and that they synapse directly onto neck and wing motoneurons in an unusual manner. However, we found that TSDNs were only weakly active during flight and are thus unlikely to provide the primary steering signal. Instead, they appear to drive the foveating head movements that stabilize prey on the eye before and likely throughout the interception flight. We suggest the TSDN population implements the reactive portion of the interception steering control system, coordinating head and wing movements to compensate for unexpected prey motion.

}, doi = {10.1101/2024.11.12.622977}, url = {https://doi.org/10.1101/2024.11.12.622977}, author = {Lin, Huai-Ti and Siwanowicz, Igor and Leonardo, Anthony} } @article {68081, title = {Recovery mechanisms in the dragonfly righting reflex.}, journal = {Science}, volume = {376}, year = {2022}, month = {2022 May 13}, pages = {754-758}, abstract = {

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{\textquoteright}s visual system to the muscles regulating wing pitch that underly the recovery. The methods developed here offer quantitative tools for inferring insects{\textquoteright} internal actions from their acrobatics, and are applicable to a broad class of natural and robotic flying systems.

}, keywords = {Animals, Biomechanical Phenomena, Flight, Animal, Insecta, Odonata, Reflex, Righting, Wings, Animal}, issn = {1095-9203}, doi = {10.1126/science.abg0946}, author = {Wang, Z Jane and Melfi, James and Leonardo, Anthony} } @article {48374, title = {Heuristic rules underlying dragonfly prey selection and interception.}, journal = {Current Biology : CB}, volume = {27}, year = {2017}, month = {2017 Mar 28}, pages = {1124-37}, abstract = {

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{\textquoteright}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.

}, issn = {1879-0445}, doi = {10.1016/j.cub.2017.03.010}, author = {Lin, Huai-Ti and Leonardo, Anthony} } @article {47203, title = {The role of motion extrapolation in amphibian prey capture.}, journal = {The Journal of Neuroscience : the official journal of the Society for Neuroscience}, volume = {35}, year = {2015}, month = {2015 Nov 18}, pages = {15430-41}, abstract = {

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{\textquoteright}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{\textquoteright}s pre-turn trajectory. These results define the computations and operating regimen for neural circuits that predict target motion.

}, issn = {1529-2401}, doi = {10.1523/JNEUROSCI.3189-15.2015}, author = {Borghuis, Bart G and Leonardo, Anthony} } @article {45792, title = {Theta sequences are essential for internally generated hippocampal firing fields.}, journal = {Nature Neuroscience}, volume = {18}, year = {2015}, month = {2015 Feb}, pages = {282-8}, abstract = {

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.

}, issn = {1546-1726}, doi = {10.1038/nn.3904}, author = {Wang, Yingxue and Romani, Sandro and Lustig, Brian and Leonardo, Anthony and Pastalkova, Eva} } @article {45720, title = {Internal models direct dragonfly interception steering.}, journal = {Nature}, year = {2014}, month = {2014 Dec 10}, abstract = {

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{\textquoteright}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{\textquoteright}s head continuously track the prey{\textquoteright}s angular position. The head-body angles established by prey tracking appear to guide systematic rotations of the dragonfly{\textquoteright}s body to align it with the prey{\textquoteright}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.

}, issn = {1476-4687}, doi = {10.1038/nature14045}, author = {Mischiati, Matteo and Lin, Huai-Ti and Herold, Paul and Imler, Elliot and Olberg, Robert and Leonardo, Anthony} } @article {45186, title = {Large-scale, high-density (up to 512 channels) recording of local circuits in behaving animals.}, journal = {Journal of Neurophysiology}, volume = {111}, year = {2014}, month = {2014 Mar}, pages = {1132-49}, abstract = {

Monitoring representative fractions of neurons from multiple brain circuits in behaving animals is necessary for understanding neuronal computation. Here, we describe a system that allows high-channel-count recordings from a small volume of neuronal tissue using a lightweight signal multiplexing headstage that permits free behavior of small rodents. The system integrates multishank, high-density recording silicon probes, ultraflexible interconnects, and a miniaturized microdrive. These improvements allowed for simultaneous recordings of local field potentials and unit activity from hundreds of sites without confining free movements of the animal. The advantages of large-scale recordings are illustrated by determining the electroanatomic boundaries of layers and regions in the hippocampus and neocortex and constructing a circuit diagram of functional connections among neurons in real anatomic space. These methods will allow the investigation of circuit operations and behavior-dependent interregional interactions for testing hypotheses of neural networks and brain function.

}, issn = {1522-1598}, doi = {10.1152/jn.00785.2013}, author = {Berenyi, Antal and Somogyv{\'a}ri, Zolt{\'a}n and Nagy, Anett J and Roux, Lisa and Long, John D and Fujisawa, Shigeyoshi and Stark, Eran and Leonardo, Anthony and Harris, Timothy D and Buzs{\'a}ki, Gy{\"o}rgy} } @article {45165, title = {A spike-timing mechanism for action selection.}, journal = {Nature Neuroscience}, volume = {17}, year = {2014}, month = {2014 Jul 17}, pages = {962-70}, abstract = {

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.

}, issn = {1546-1726}, doi = {10.1038/nn.3741}, author = {von Reyn, Catherine R and Breads, Patrick and Peek, Martin Y and Zheng, Grace Zhiyu and Williamson, W Ryan and Yee, Alyson L and Leonardo, Anthony and Card, Gwyneth M} } @article {6580, title = {Nonlinear dynamics support a linear population code in a retinal target-tracking circuit.}, journal = {The Journal of Neuroscience: The Official Journal of the Society for Neuroscience}, volume = {33}, year = {2013}, month = {2013 Oct 23}, pages = {16971-82}, abstract = {

A basic task faced by the visual system of many organisms is to accurately track the position of moving prey. The retina is the first stage in the processing of such stimuli; the nature of the transformation here, from photons to spike trains, constrains not only the ultimate fidelity of the tracking signal but also the ease with which it can be extracted by other brain regions. Here we demonstrate that a population of fast-OFF ganglion cells in the salamander retina, whose dynamics are governed by a nonlinear circuit, serve to compute the future position of the target over hundreds of milliseconds. The extrapolated position of the target is not found by stimulus reconstruction but is instead computed by a weighted sum of ganglion cell outputs, the population vector average (PVA). The magnitude of PVA extrapolation varies systematically with target size, speed, and acceleration, such that large targets are tracked most accurately at high speeds, and small targets at low speeds, just as is seen in the motion of real prey. Tracking precision reaches the resolution of single photoreceptors, and the PVA algorithm performs more robustly than several alternative algorithms. If the salamander brain uses the fast-OFF cell circuit for target extrapolation as we suggest, the circuit dynamics should leave a microstructure on the behavior that may be measured in future experiments. Our analysis highlights the utility of simple computations that, while not globally optimal, are efficiently implemented and have close to optimal performance over a limited but ethologically relevant range of stimuli.

}, issn = {1529-2401}, doi = {10.1523/JNEUROSCI.2257-13.2013}, author = {Leonardo, Anthony and Meister, Markus} } @article {6527, title = {A battery-free multichannel digital neural/EMG telemetry system for flying insects.}, journal = {IEEE Transactions on Biomedical Circuits and Systems}, volume = {6}, year = {2012}, month = {2012 Oct}, pages = {424-36}, abstract = {

This paper presents a digital neural/EMG telemetry system small enough and lightweight enough to permit recording from insects in flight. It has a measured flight package mass of only 38 mg. This system includes a single-chip telemetry integrated circuit (IC) employing RF power harvesting for battery-free operation, with communication via modulated backscatter in the UHF (902-928 MHz) band. An on-chip 11-bit ADC digitizes 10 neural channels with a sampling rate of 26.1 kSps and 4 EMG channels at 1.63 kSps, and telemeters this data wirelessly to a base station. The companion base station transceiver includes an RF transmitter of +36 dBm (4 W) output power to wirelessly power the telemetry IC, and a digital receiver with a sensitivity of -70 dBm for 10$^{-}$$^{5}$ BER at 5.0 Mbps to receive the data stream from the telemetry IC. The telemetry chip was fabricated in a commercial 0.35 μ m 4M1P (4 metal, 1 poly) CMOS process. The die measures 2.36 {\texttimes} 1.88 mm, is 250 μm thick, and is wire bonded into a flex circuit assembly measuring 4.6 {\texttimes} 6.8 mm.

}, issn = {1940-9990}, doi = {10.1109/TBCAS.2012.2222881}, author = {Thomas, Stewart J and Harrison, Reid R. and Leonardo, Anthony and Reynolds, Matthew S} } @article {5385, title = {Wireless neural/EMG telemetry systems for small freely moving animals.}, journal = {IEEE Transactions on Biomedical Circuits and Systems}, volume = {5}, year = {2011}, month = {2011 Apr}, pages = {103-11}, abstract = {

We have developed miniature telemetry systems that capture neural, EMG, and acceleration signals from a freely moving insect or other small animal and transmit the data wirelessly to a remote digital receiver. The systems are based on custom low-power integrated circuits (ICs) that amplify, filter, and digitize four biopotential signals using low-noise circuits. One of the chips also digitizes three acceleration signals from an off-chip microelectromechanical-system accelerometer. All information is transmitted over a wireless ~ 900-MHz telemetry link. The first unit, using a custom chip fabricated in a 0.6- μm BiCMOS process, weighs 0.79 g and runs for two hours on two small batteries. We have used this system to monitor neural and EMG signals in jumping and flying locusts as well as transdermal potentials in weakly swimming electric fish. The second unit, using a custom chip fabricated in a 0.35-μ m complementary metal-oxide semiconductor CMOS process, weighs 0.17 g and runs for five hours on a single 1.5-V battery. This system has been used to monitor neural potentials in untethered perching dragonflies.

}, issn = {1932-4545}, doi = {10.1109/TBCAS.2011.2131140}, author = {Harrison, Reid R. and Fotowat, Haleh and Chan, Raymond and Kier, Ryan J and Olberg, Robert and Leonardo, Anthony and Gabbiani, Fabrizio} } @article {1741, title = {Degenerate coding in neural systems.}, journal = {Journal of Comparative Physiology. A, Neuroethology, Sensory, Neural, and Behavioral Physiology}, volume = {191}, year = {2005}, month = {2005 Nov}, pages = {995-1010}, abstract = {

When the dimensionality of a neural circuit is substantially larger than the dimensionality of the variable it encodes, many different degenerate network states can produce the same output. In this review I will discuss three different neural systems that are linked by this theme. The pyloric network of the lobster, the song control system of the zebra finch, and the odor encoding system of the locust, while different in design, all contain degeneracies between their internal parameters and the outputs they encode. Indeed, although the dynamics of song generation and odor identification are quite different, computationally, odor recognition can be thought of as running the song generation circuitry backwards. In both of these systems, degeneracy plays a vital role in mapping a sparse neural representation devoid of correlations onto external stimuli (odors or song structure) that are strongly correlated. I argue that degeneracy between input and output states is an inherent feature of many neural systems, which can be exploited as a fault-tolerant method of reliably learning, generating, and discriminating closely related patterns.

}, keywords = {Animal, Animals, Information Storage and Retrieval, Learning, Models, Nerve Net, Neural Pathways, Neurological, Neuronal Plasticity, Smell, Vocalization}, issn = {0340-7594}, doi = {10.1007/s00359-005-0026-0}, author = {Leonardo, Anthony} } @article {1742, title = {Ensemble coding of vocal control in birdsong.}, journal = {The Journal of Neuroscience: The Official Journal of the Society for Neuroscience}, volume = {25}, year = {2005}, month = {2005 Jan 19}, pages = {652-61}, abstract = {

Zebra finch song is represented in the high-level motor control nucleus high vocal center (HVC) (Reiner et al., 2004) as a sparse sequence of spike bursts. In contrast, the vocal organ is driven continuously by smoothly varying muscle control signals. To investigate how the sparse HVC code is transformed into continuous vocal patterns, we recorded in the singing zebra finch from populations of neurons in the robust nucleus of arcopallium (RA), a premotor area intermediate between HVC and the motor neurons. We found that highly similar song elements are typically produced by different RA ensembles. Furthermore, although the song is modulated on a wide range of time scales (10-100 ms), patterns of neural activity in RA change only on a short time scale (5-10 ms). We suggest that song is driven by a dynamic circuit that operates on a single underlying clock, and that the large convergence of RA neurons to vocal control muscles results in a many-to-one mapping of RA activity to song structure. This permits rapidly changing RA ensembles to drive both fast and slow acoustic modulations, thereby transforming the sparse HVC code into a continuous vocal pattern.

}, keywords = {Action Potentials, Animal, Animals, Brain, Finches, Male, Models, Muscles, Neural Pathways, Neurological, Neurons, Sound Spectrography, Time Factors, Vocalization}, issn = {1529-2401}, doi = {10.1523/JNEUROSCI.3036-04.2005}, author = {Leonardo, Anthony and Fee, Michale S} } @article {1743, title = {Experimental test of the birdsong error-correction model.}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, volume = {101}, year = {2004}, month = {2004 Nov 30}, pages = {16935-40}, abstract = {

Adult zebra finches require auditory feedback to maintain their songs. It has been proposed that the lateral magnocellular nucleus of the anterior nidopallium (LMAN) mediates song plasticity based on auditory feedback. In this model, neurons in LMAN, tuned to the spectral and temporal properties of the bird{\textquoteright}s own song (BOS), are thought to compute the difference between the auditory feedback from the bird{\textquoteright}s vocalizations and an internal song template. This error-correction signal is then used to initiate changes in the motor system that make future vocalizations a better match to the song template. This model was tested by recording from single LMAN neurons while manipulating the auditory feedback heard by singing birds. In contrast to the model predictions, LMAN spike patterns are insensitive to manipulations of auditory feedback. These results suggest that BOS tuning in LMAN is not used for error detection and constrain the nature of any error signal from LMAN to the motor system. Finally, LMAN neurons produce spikes locked precisely to the bird{\textquoteright}s song, independent of the auditory feedback heard by the bird. This finding suggests that a large portion of the input to this nucleus is from the motor control signals that generate the song rather than from auditory feedback.

}, keywords = {Action Potentials, Animal Communication, Animals, Feedback, Finches}, issn = {0027-8424}, doi = {10.1073/pnas.0407870101}, author = {Leonardo, Anthony} }