Robert M. Olberg

Prey pursuit and interception in dragonflies.

Abstract Perching dragonflies (Libellulidae; Odonata) are sit-and-wait predators, which take off and pursue small flying insects. To investigate their prey pursuit strategy, we videotaped 36 prey-capture flights of male dragonflies, Erythemis simplicicollis and Leucorrhinia intacta, for frame-by-frame analysis. We found that dragonflies fly directly toward the point of prey interception by steering to minimize the movement of the preys image on the retina. This behavior could be guided by target-selective descending interneurons which show directionally selective visual responses to small-object movement. We investigated how dragonflies discriminate distance of potential prey. We found a peak in angular velocity of the prey shortly before take-off which might cue the dragonfly to nearby flying targets. Parallax information from head movements was not required for successful prey pursuit.

Internal models direct dragonfly interception steering

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.

Identified target-selective visual interneurons descending from the dragonfly brain

Summary1.Eight large interneurons descending in the dragonfly (Aeshna umbrosa, Anax junius) ventral nerve cord from the brain to the thoracic ganglia were identified anatomically with intracellular dye injection (Fig. 3). All eight were strictly visual and responded only to movements of small patterns, such as black squares, ‘targets’, moving on a white background.2.The target interneurons all projected from the protocerebrum at least as far as the metathoracic ganglion. Within the protocerebrum they arborized in the posterodorsal neuropil region, near the base of the circumesophageal connectives (Fig. 3).3.The receptive fields of six of the cells were large, including most of the forward hemisphere of vision. For five of these, spiking responses were often restricted to a much smaller region within the receptive field, with stimulation of other areas yielding only subthreshold responses (Figs. 4 and 5, Table 1).4.The pattern of selectivity for target size varied, with some neurons responding only to small targets, some showing consistent responses over a wide range of target sizes, and one preferring larger targets (Fig. 6, Table 1).5.Five of the interneurons were directionally selective. Movement in the antipreferred direction elicited hyperpolarizing responses in two of them. Movements of large patterns, such as a checkerboard pattern covering the forward hemisphere, elicited opposite directional responses, i.e., hyperpolarizations in the preferred target direction and subthreshold depolarizations in the antipreferred direction (Fig. 7). A large pattern moving in any direction inhibited the response to target movement (Fig. 8).6.These neurons mediate, in part, the visual control of flight orientation. I propose that they convey turning signals to the wing motor in response to objects moving relative to the animal.

The Information Content of Receptive Fields

The nervous system must observe a complex world and produce appropriate, sometimes complex, behavioral responses. In contrast to this complexity, neural responses are often characterized through very simple descriptions such as receptive fields or tuning curves. Do these characterizations adequately reflect the true dimensionality reduction that takes place in the nervous system, or are they merely convenient oversimplifications? Here we address this question for the target-selective descending neurons (TSDNs) of the dragonfly. Using extracellular multielectrode recordings of a population of TSDNs, we quantify the completeness of the receptive field description of these cells and conclude that the information in independent instantaneous position and velocity receptive fields accounts for 70%-90% of the total information in single spikes. Thus, we demonstrate that this simple receptive field model is close to a complete description of the features in the stimulus that evoke TSDN response.

Visual control of prey-capture flight in dragonflies.

Interacting with a moving object poses a computational problem for an animals nervous system. This problem has been elegantly solved by the dragonfly, a formidable visual predator on flying insects. The dragonfly computes an interception flight trajectory and steers to maintain it during its prey-pursuit flight. This review summarizes current knowledge about pursuit behavior and neurons thought to control interception in the dragonfly. When understood, this system has the potential for explaining how a small group of neurons can control complex interactions with moving objects.

Wireless Neural/EMG Telemetry Systems for Small Freely Moving Animals

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.

Visual receptive field properties of feature detecting neurons in the dragonfly

The dragonfly, (Aeshna, Anax) which feeds on small flying insects, requires a visual system capable of signaling the movements of airborne prey. A group of 8 descending feature detectors in the dragonfly are tuned exclusively to moving contrasting objects. These “target-selective” descending neurons project from the brain to the thoracic ganglia. Their activity drives steering movement of the wings.In this study, we recorded target-selective descending neuron activity intracellularly.To define their receptive fields, we recorded responses to the movement of black square targets projected onto a screen in front of the animal. Each neuron was identified by dye injection.Target-selective descending neurons exhibit several receptive field properties. Our results show that they are strongly directionally selective. Two TSDNs, exclusively tuned to small targets, have receptive fields restricted to visual midline. Others, which are not selective for target size, have asymmetric receptive fields centered laterally.We suggest that the behavioral function of these specialized feature detectors is to steer the dragonfly during prey-tracking so as to fix the position of the prey image on the retina. If the dragonfly maintains a constant visual bearing to its prey over time it will intercept its prey.

Pheromone-modulated optomotor response in male gypsy moths, Lymantria dispar L.: Directionally selective visual interneurons in the ventral nerve cord

SummaryIn response female pheromone the male gypsy moth flies a zigzagging path upwind to locate the source of odor. He determines wind direction visually. To learn more about the mechanism underlying this behavior, we studied descending interneurons with dye-filled micro-electrodes. We studied the interneuronal responses to combinations of pheromone and visual stimuli.1.We recorded 5 neurons whose directionally selective visual responses to wide field pattern movement were amplified by pheromone (Figs. 2–6).2.The activity of the above neurons was more closely correlated with the position of the moving pattern than with its velocity (Fig. 4).3.One neuron showed no clearly directional visual response and no response to pheromone. Yet in the presence of pheromone it showed directionally selective visual responses (Fig. 6).4.We recorded 4 neurons whose directionally selective visual responses were not modulated by pheromone (Fig. 7), ruling out the possibility that the effect of the pheromone was simply to raise the activity of all visual neurons.5.Our results suggest that female pheromone amplifies some neural pathways mediating male optomotor responses, especially the directionally selective responses to the transverse movement of the image, both below and above the animal.

Eight pairs of descending visual neurons in the dragonfly give wing motor centers accurate population vector of prey direction

Intercepting a moving object requires prediction of its future location. This complex task has been solved by dragonflies, who intercept their prey in midair with a 95% success rate. In this study, we show that a group of 16 neurons, called target-selective descending neurons (TSDNs), code a population vector that reflects the direction of the target with high accuracy and reliability across 360°. The TSDN spatial (receptive field) and temporal (latency) properties matched the area of the retina where the prey is focused and the reaction time, respectively, during predatory flights. The directional tuning curves and morphological traits (3D tracings) for each TSDN type were consistent among animals, but spike rates were not. Our results emphasize that a successful neural circuit for target tracking and interception can be achieved with few neurons and that in dragonflies this information is relayed from the brain to the wing motor centers in population vector form.

The mapping of visual space by identified large second-order neurons in the dragonfly median ocellus

In adult dragonflies, the compound eyes are augmented by three simple eyes known as the dorsal ocelli. The outputs of ocellar photoreceptors converge on relatively few second-order neurons with large axonal diameters (L-neurons). We determine L-neuron morphology by iontophoretic dye injection combined with three-dimensional reconstructions. Using intracellular recording and white noise analysis, we also determine the physiological receptive fields of the L-neurons, in order to identify the extent to which they preserve spatial information. We find a total of 11 median ocellar L-neurons, consisting of five symmetrical pairs and one unpaired neuron. L-neurons are distinguishable by the extent and location of their terminations within the ocellar plexus and brain. In the horizontal dimension, L-neurons project to different regions of the ocellar plexus, in close correlation with their receptive fields. In the vertical dimension, dendritic arborizations overlap widely, paralleled by receptive fields that are narrow and do not differ between different neurons. These results provide the first evidence for the preservation of spatial information by the second-order neurons of any dorsal ocellus. The system essentially forms a one-dimensional image of the equator over a wide azimuthal area, possibly forming an internal representation of the horizon. Potential behavioural roles for the system are discussed.