Binocular Encoding in the Damselfly Pre-motor Target Tracking System.

Akin to all damselflies, Calopteryx (family Calopterygidae), commonly known as jewel wings or demoiselles, possess dichoptic (separated) eyes with overlapping visual fields of view. In contrast, many dragonfly species possess holoptic (dorsally fused) eyes with limited binocular overlap. We have here compared the neuronal correlates of target tracking between damselfly and dragonfly sister lineages and linked these changes in visual overlap to pre-motor neural adaptations. Although dragonflies attack prey dorsally, we show that demoiselles attack prey frontally. We identify demoiselle target-selective descending neurons (TSDNs) with matching frontal visual receptive fields, anatomically and functionally homologous to the dorsally positioned dragonfly TSDNs. By manipulating visual input using eyepatches and prisms, we show that moving target information at the pre-motor level depends on binocular summation in demoiselles. Consequently, demoiselles encode directional information in a binocularly fused frame of reference such that information of a target moving toward the midline in the left eye is fused with information of the target moving away from the midline in the right eye. This contrasts with dragonfly TSDNs, where receptive fields possess a sharp midline boundary, confining responses to a single visual hemifield in a sagittal frame of reference (i.e., relative to the midline). Our results indicate that, although TSDNs are conserved across Odonata, their neural inputs, and thus the upstream organization of the target tracking system, differ significantly and match divergence in eye design and predatory strategies. VIDEO ABSTRACT.

Grasshopper DCMD: An Undergraduate Electrophysiology Lab for Investigating Single-Unit Responses to Behaviorally-Relevant Stimuli.

Avoiding capture from a fast-approaching predator is an important survival skill shared by many animals. Investigating the neural circuits that give rise to this escape behavior can provide a tractable demonstration of systems-level neuroscience research for undergraduate laboratories. In this paper, we describe three related hands-on exercises using the grasshopper and affordable technology to bring neurophysiology, neuroethology, and neural computation to life and enhance student understanding and interest. We simplified a looming stimuli procedure using the Backyard Brains SpikerBox bioamplifier, an open-source and low-cost electrophysiology rig, to extracellularly record activity of the descending contralateral movement detector (DCMD) neuron from the grasshopper's neck. The DCMD activity underlies the grasshopper's motor responses to looming monocular visual cues and can easily be recorded and analyzed on an open-source iOS oscilloscope app, Spike Recorder. Visual stimuli are presented to the grasshopper by this same mobile application allowing for synchronized recording of stimuli and neural activity. An in-app spike-sorting algorithm is described that allows a quick way for students to record, sort, and analyze their data at the bench. We also describe a way for students to export these data to other analysis tools. With the protocol described, students will be able to prepare the grasshopper, find and record from the DCMD neuron, and visualize the DCMD responses to quantitatively investigate the escape system by adjusting the speed and size of simulated approaching objects. We describe the results from 22 grasshoppers, where 50 of the 57 recording sessions (87.7%) had a reliable DCMD response. Finally, we field-tested our experiment in an undergraduate neuroscience laboratory and found that a majority of students (67%) could perform this exercise in one two-hour lab setting, and had an increase in interest for studying the neural systems that drive behavior.

The Killer Fly Hunger Games: Target Size and Speed Predict Decision to Pursuit.

Predatory animals have evolved to optimally detect their prey using exquisite sensory systems such as vision, olfaction and hearing. It may not be so surprising that vertebrates, with large central nervous systems, excel at predatory behaviors. More striking is the fact that many tiny insects, with their miniscule brains and scaled down nerve cords, are also ferocious, highly successful predators. For predation, it is important to determine whether a prey is suitable before initiating pursuit. This is paramount since pursuing a prey that is too large to capture, subdue or dispatch will generate a substantial metabolic cost (in the form of muscle output) without any chance of metabolic gain (in the form of food). In addition, during all pursuits, the predator breaks its potential camouflage and thus runs the risk of becoming prey itself. Many insects use their eyes to initially detect and subsequently pursue prey. Dragonflies, which are extremely efficient predators, therefore have huge eyes with relatively high spatial resolution that allow efficient prey size estimation before initiating pursuit. However, much smaller insects, such as killer flies, also visualize and successfully pursue prey. This is an impressive behavior since the small size of the killer fly naturally limits the neural capacity and also the spatial resolution provided by the compound eye. Despite this, we here show that killer flies efficiently pursue natural (Drosophila melanogaster) and artificial (beads) prey. The natural pursuits are initiated at a distance of 7.9 ± 2.9 cm, which we show is too far away to allow for distance estimation using binocular disparities. Moreover, we show that rather than estimating absolute prey size prior to launching the attack, as dragonflies do, killer flies attack with high probability when the ratio of the prey's subtended retinal velocity and retinal size is 0.37. We also show that killer flies will respond to a stimulus of an angular size that is smaller than that of the photoreceptor acceptance angle, and that the predatory response is strongly modulated by the metabolic state. Our data thus provide an exciting example of a loosely designed matched filter to Drosophila, but one which will still generate successful pursuits of other suitable prey.

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.

Visual control of prey-capture flight in dragonflies.

Interacting with a moving object poses a computational problem for an animal's 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.

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.