Neural mechanisms underlying target detection in a dragonfly centrifugal neuron

Dragonfly visual evolutionary neural network: A novel bionic optimizer with related LSGO and engineering design optimization

Biological visual systems intrinsically include multiple kinds of motion-sensitive neurons. Some of them have been successfully used to construct neural computational models for problem-specific engineering applications such as motion detection, object tracking, etc. Nevertheless, it remains unclear how these neurons' response mechanisms can be contributed to the topic of optimization. Hereby, the dragonfly's visual response mechanism is integrated with the inspiration of swarm evolution to develop a dragonfly visual evolutionary neural network for large-scale global optimization (LSGO) problems. Therein, a grayscale image input-based dragonfly visual neural network online outputs multiple global learning rates, and later, such learning rates guide a population evolution-like state update strategy to seek the global optimum. The comparative experiments show that the neural network is a competitive optimizer capable of effectively solving LSGO benchmark suites with 2000 dimensions per example and the design of an operational amplifier.

Descending neurons of the hoverfly respond to pursuits of artificial targets

Many animals use motion vision information to control dynamic behaviors. Predatory animals, for example, show an exquisite ability to detect rapidly moving prey, followed by pursuit and capture. Such target detection is not only used by predators but is also important in conspecific interactions, such as for male hoverflies defending their territories against conspecific intruders. Visual target detection is believed to be subserved by specialized target-tuned neurons found in a range of species, including vertebrates and arthropods. However, how these target-tuned neurons respond to actual pursuit trajectories is currently not well understood. To redress this, we recorded extracellularly from target-selective descending neurons (TSDNs) in male Eristalis tenax hoverflies. We show that they have dorso-frontal receptive fields with a preferred direction up and away from the visual midline. We reconstructed visual flow fields as experienced during pursuits of artificial targets (black beads). We recorded TSDN responses to six reconstructed pursuits and found that each neuron responded consistently at remarkably specific time points but that these time points differed between neurons. We found that the observed spike probability was correlated with the spike probability predicted from each neuron's receptive field and size tuning. Interestingly, however, the overall response rate was low, with individual neurons responding to only a small part of each reconstructed pursuit. In contrast, the TSDN population responded to substantially larger proportions of the pursuits but with lower probability. This large variation between neurons could be useful if different neurons control different parts of the behavioral output.

Sparse spike trains and the limitation of rate codes underlying rapid behaviours

Animals live in dynamic worlds where they use sensorimotor circuits to rapidly process information and drive behaviours. For example, dragonflies are aerial predators that react to movements of prey within tens of milliseconds. These pursuits are likely controlled by identified neurons in the dragonfly, which have well-characterized physiological responses to moving targets. Predominantly, neural activity in these circuits is interpreted in context of a rate code, where information is conveyed by changes in the number of spikes over a time period. However, such a description of neuronal activity is difficult to achieve in real-world, real-time scenarios. Here, we contrast a neuroscientists' post-hoc view of spiking activity with the information available to the animal in real-time. We describe how performance of a rate code is readily overestimated and outline a rate code's significant limitations in driving rapid behaviours.

Mathematical study of neural feedback roles in small target motion detection

Building an efficient and reliable small target motion detection visual system is challenging for artificial intelligence robotics because a small target only occupies few pixels and hardly displays visual features in images. Biological visual systems that have evolved over millions of years could be ideal templates for designing artificial visual systems. Insects benefit from a class of specialized neurons, called small target motion detectors (STMDs), which endow them with an excellent ability to detect small moving targets against a cluttered dynamic environment. Some bio-inspired models featured in feed-forward information processing architectures have been proposed to imitate the functions of the STMD neurons. However, feedback, a crucial mechanism for visual system regulation, has not been investigated deeply in the STMD-based neural circuits and its roles in small target motion detection remain unclear. In this paper, we propose a time-delay feedback STMD model for small target motion detection in complex backgrounds. The main contributions of this study are as follows. First, a feedback pathway is designed by transmitting information from output-layer neurons to lower-layer interneurons in the STMD pathway and the role of the feedback is analyzed from the view of mathematical analysis. Second, to estimate the feedback constant, the existence and uniqueness of solutions for nonlinear dynamical systems formed by feedback loop are analyzed via Schauder's fixed point theorem and contraction mapping theorem. Finally, an iterative algorithm is designed to solve the nonlinear problem and the performance of the proposed model is tested by experiments. Experimental results demonstrate that the feedback is able to weaken background false positives while maintaining a minor effect on small targets. It outperforms existing STMD-based models regarding the accuracy of fast-moving small target detection in visual clutter. The proposed feedback approach could inspire the relevant modeling of robust motion perception robotics visual systems.

Preattentive facilitation of target trajectories in a dragonfly visual neuron

The ability to pursue targets in visually cluttered and distraction-rich environments is critical for predators such as dragonflies. Previously, we identified Centrifugal Small-Target Motion Detector 1 (CSTMD1), a dragonfly visual neuron likely involved in such target-tracking behaviour. CSTMD1 exhibits facilitated responses to targets moving along a continuous trajectory. Moreover, CSTMD1 competitively selects a single target out of a pair. Here, we conducted in vivo, intracellular recordings from CSTMD1 to examine the interplay between facilitation and selection, in response to the presentation of paired targets. We find that neuronal responses to both individual trajectories of simultaneous, paired targets are facilitated, rather than being constrained to the single, selected target. Additionally, switches in selection elicit suppression which is likely an important attribute underlying target pursuit. However, binocular experiments reveal these results are constrained to paired targets within the same visual hemifield, while selection of a target in one visual hemifield establishes ocular dominance that prevents facilitation or response to contralaterally presented targets. These results reveal that the dragonfly brain preattentively represents more than one target trajectory, to balance between attentional flexibility and resistance against distraction.

A dragonfly visual neuron independently facilitates responses to rival targets within the same visual field, mediating selective attention.

Dragonfly Neurons Selectively Attend to Targets Within Natural Scenes

Aerial predators, such as the dragonfly, determine the position and movement of their prey even when both are moving through complex, natural scenes. This task is likely supported by a group of neurons in the optic lobe which respond to moving targets that subtend less than a few degrees. These Small Target Motion Detector (STMD) neurons are tuned to both target size and velocity, whilst also exhibiting facilitated responses to targets traveling along continuous trajectories. When presented with a pair of targets, some STMDs generate spiking activity that represent a competitive selection of one target, as if the alternative does not exist (i.e., selective attention). Here, we describe intracellular responses of CSTMD1 (an identified STMD) to the visual presentation of targets embedded within cluttered, natural scenes. We examine CSTMD1 response changes to target contrast, as well as a range of target and background velocities. We find that background motion affects CSTMD1 responses via the competitive selection between features within the natural scene. Here, robust discrimination of our artificially embedded “target” is limited to scenarios when its velocity is matched to, or greater than, the background velocity. Additionally, the background’s direction of motion affects discriminability, though not in the manner observed in STMDs of other flying insects. Our results highlight that CSTMD1’s competitive responses are to those features best matched to the neuron’s underlying spatiotemporal tuning, whether from the embedded target or other features in the background clutter. In many scenarios, CSTMD1 responds robustly to targets moving through cluttered scenes. However, whether this neuronal system could underlie the task of competitively selecting slow moving prey against fast-moving backgrounds remains an open question.

Modeling Nonlinear Dendritic Processing of Facilitation in a Dragonfly Target-Tracking Neuron

Dragonflies are highly skilled and successful aerial predators that are even capable of selectively attending to one target within a swarm. Detection and tracking of prey is likely to be driven by small target motion detector (STMD) neurons identified from several insect groups. Prior work has shown that dragonfly STMD responses are facilitated by targets moving on a continuous path, enhancing the response gain at the present and predicted future location of targets. In this study, we combined detailed morphological data with computational modeling to test whether a combination of dendritic morphology and nonlinear properties of NMDA receptors could explain these observations. We developed a hybrid computational model of neurons within the dragonfly optic lobe, which integrates numerical and morphological components. The model was able to generate potent facilitation for targets moving on continuous trajectories, including a localized spotlight of maximal sensitivity close to the last seen target location, as also measured during in vivo recordings. The model did not, however, include a mechanism capable of producing a traveling or spreading wave of facilitation. Our data support a strong role for the high dendritic density seen in the dragonfly neuron in enhancing non-linear facilitation. An alternative model based on the morphology of an unrelated type of motion processing neuron from a dipteran fly required more than three times higher synaptic gain in order to elicit similar levels of facilitation, despite having only 20% fewer synapses. Our data support a potential role for NMDA receptors in target tracking and also demonstrate the feasibility of combining biologically plausible dendritic computations with more abstract computational models for basic processing as used in earlier studies.

Spike bursting in a dragonfly target-detecting neuron

Dragonflies visually detect prey and conspecifics, rapidly pursuing these targets via acrobatic flights. Over many decades, studies have investigated the elaborate neuronal circuits proposed to underlie this rapid behaviour. A subset of dragonfly visual neurons exhibit exquisite tuning to small, moving targets even when presented in cluttered backgrounds. In prior work, these neuronal responses were quantified by computing the rate of spikes fired during an analysis window of interest. However, neuronal systems can utilize a variety of neuronal coding principles to signal information, so a spike train’s information content is not necessarily encapsulated by spike rate alone. One example of this is burst coding, where neurons fire rapid bursts of spikes, followed by a period of inactivity. Here we show that the most studied target-detecting neuron in dragonflies, CSTMD1, responds to moving targets with a series of spike bursts. This spiking activity differs from those in other identified visual neurons in the dragonfly, indicative of different physiological mechanisms underlying CSTMD1’s spike generation. Burst codes present several advantages and disadvantages compared to other coding approaches. We propose functional implications of CSTMD1’s burst coding activity and show that spike bursts enhance the robustness of target-evoked responses.

The visual neuroecology of anisoptera

Dragonflies belong to the oldest known lineage of flying animals, found across the globe around streams, ponds and forests. They are insect predators, specialising in ambush attack as aquatic larvae and rapid pursuit as adults. Dragonfly adults hunt amidst swarms in conditions that confuse many predatory species, and exhibit capture rates above 90%. Underlying the performance of such a remarkable predator is a finely tuned visual system capable of tracking targets amidst distractors and background clutter. The dragonfly performs a complex repertoire of flight behaviours, from near-motionless hovering to acute turns at high speeds. Here, we review the optical, neuronal, and behavioural adaptations that underlie the dragonflies' ability to achieve such remarkable predatory success.

Comparison of Transparency and Shrinkage During Clearing of Insect Brains Using Media With Tunable Refractive Index

Improvement of imaging quality has the potential to visualize previously unseen building blocks of the brain and is therefore one of the great challenges in neuroscience. Rapid development of new tissue clearing techniques in recent years have attempted to solve imaging compromises in thick brain samples, particularly for high resolution optical microscopy, where the clearing medium needs to match the high refractive index of the objective immersion medium. These problems are exacerbated in insect tissue, where numerous (initially air-filled) tracheal tubes branching throughout the brain increase the scattering of light. To date, surprisingly few studies have systematically quantified the benefits of such clearing methods using objective transparency and tissue shrinkage measurements. In this study we compare a traditional and widely used insect clearing medium, methyl salicylate combined with permanent mounting in Permount (“MS/P”) with several more recently applied clearing media that offer tunable refractive index (n): 2,2′-thiodiethanol (TDE), “SeeDB2” (in variants SeeDB2S and SeeDB2G matched to oil and glycerol immersion, n = 1.52 and 1.47, respectively) and Rapiclear (also with n = 1.52 and 1.47). We measured transparency and tissue shrinkage by comparing freshly dissected brains with cleared brains from dipteran flies, with or without addition of vacuum or ethanol pre-treatments (dehydration and rehydration) to evacuate air from the tracheal system. The results show that ethanol pre-treatment is very effective for improving transparency, regardless of the subsequent clearing medium, while vacuum treatment offers little measurable benefit. Ethanol pre-treated SeeDB2G and Rapiclear brains show much less shrinkage than using the traditional MS/P method. Furthermore, at lower refractive index, closer to that of glycerol immersion, these recently developed media offer outstanding transparency compared to TDE and MS/P. Rapiclear protocols were less laborious compared to SeeDB2, but both offer sufficient transparency and refractive index tunability to permit super-resolution imaging of local volumes in whole mount brains from large insects, and even light-sheet microscopy. Although long-term permanency of Rapiclear stored samples remains to be established, our samples still showed good preservation of fluorescence after storage for more than a year at room temperature.

Non-canonical Receptive Field Properties and Neuromodulation of Feature-Detecting Neurons in Flies

Several fundamental aspects of motion vision circuitry are prevalent across flies and mice. Both taxa segregate ON and OFF signals. For any given spatial pattern, motion detectors in both taxa are tuned to speed, selective for one of four cardinal directions, and modulated by catecholamine neurotransmitters. These similarities represent conserved, canonical properties of the functional circuits and computational algorithms for motion vision. Less is known about feature detectors, including how receptive field properties differ from the motion pathway or whether they are under neuromodulatory control to impart functional plasticity for the detection of salient objects from a moving background. Here, we investigated 19 types of putative feature selective lobula columnar (LC) neurons in the optic lobe of the fruit fly Drosophila melanogaster to characterize divergent properties of feature selection. We identified LC12 and LC15 as feature detectors. LC15 encodes moving bars, whereas LC12 is selective for the motion of discrete objects, mostly independent of size. Neither is selective for contrast polarity, speed, or direction, highlighting key differences in the underlying algorithms for feature detection and motion vision. We show that the onset of background motion suppresses object responses by LC12 and LC15. Surprisingly, the application of octopamine, which is released during flight, reverses the suppressive influence of background motion, rendering both LCs able to track moving objects superimposed against background motion. Our results provide a comparative framework for the function and modulation of feature detectors and new insights into the underlying neuronal mechanisms involved in visual feature detection.

Object-Displacement-Sensitive Visual Neurons Drive Freezing in Drosophila

Visual systems are often equipped with neurons that detect small moving objects, which may represent prey, predators, or conspecifics. Although the processing properties of those neurons have been studied in diverse organisms, links between the proposed algorithms and animal behaviors or circuit mechanisms remain elusive. Here, we have investigated behavioral function, computational algorithm, and neurochemical mechanisms of an object-selective neuron, LC11, in Drosophila. With genetic silencing and optogenetic activation, we show that LC11 is necessary for a visual object-induced stopping behavior in walking flies, a form of short-term freezing, and its activity can promote stopping. We propose a new quantitative model for small object selectivity based on the physiology and anatomy of LC11 and its inputs. The model accurately reproduces LC11 responses by pooling fast-adapting, tightly size-tuned inputs. Direct visualization of neurotransmitter inputs to LC11 confirmed the model conjectures about upstream processing. Our results demonstrate how adaptation can enhance selectivity for behaviorally relevant, dynamic visual features.

Contrast sensitivity and visual acuity of Queensland fruit flies (Bactrocera tryoni)

This study examines the visual acuity of Queensland fruit flies (Bactrocera tryoni) by analysing their turning responses to an immersive visual stimulus consisting of a pattern of vertical stripes presented at various angular periods and rotational rates. The results infer that these flies possess an interommatidial angle of approximately [Formula: see text], and an ommatidial acceptance angle of approximately [Formula: see text]. This suggests that the visual acuity of Queensland fruit flies is substantially better than that of the classical vinegar fly (Drosophila melanogaster), and is comparable to those of the housefly (Musca domestica) and the honeybee (Apis mellifera). The contrast sensitivity of Queensland fruit flies is comparable to that of the housefly.

Strategies of locomotion composition

This review aims to highlight the importance of saccades during locomotion as a strategy to reduce sensory information loss while the subject is moving. Acquiring sensory data from the environment during movement results in a temporal flow of information, as the sensory precept changes with the position of the observer. Accordingly, the movement pattern shapes the sensory flow. Therefore, the requirements of locomotion and sensation have to be balanced in the behaviour of the organism. Insect vision provides deep insight into the interplay between action and perception. Insects can shape their optic flow by reducing their rotational movements to fast and short saccades. This generates prolonged phases of translations which provide depth information. Extensive behavioural and physiological studies on insects show how shaping the optic flow facilitates the coding of motion vision. Indeed the saccadic strategy provides an elegant solution to optimise sensory flow. Complementary studies in other taxa reported similar locomotion strategies emphasising the crucial influence of sensory flow on locomotion.

A Target-Detecting Visual Neuron in the Dragonfly Locks on to Selectively Attended Targets

The visual world projects a complex and rapidly changing image onto the retina of many animal species. This presents computational challenges for those animals reliant on visual processing to provide an accurate representation of the world. One such challenge is parsing a visual scene for the most salient targets, such as the selection of prey amid a swarm. The ability to selectively prioritize processing of some stimuli over others is known as 'selective attention'. We recently identified a dragonfly visual neuron called 'Centrifugal Small Target Motion Detector 1' (CSTMD1) that exhibits selective attention when presented with multiple, equally salient targets. Here we conducted in vivo, electrophysiological recordings from CSTMD1 in wild-caught male dragonflies (Hemicordulia tau), while presenting visual stimuli on an LCD monitor. To identify the target selected in any given trial, we uniquely modulated the intensity of the moving targets (frequency tagging). We found that the frequency information of the selected target is preserved in the neuronal response, while the distracter is completely ignored. We also show that the competitive system that underlies selection in this neuron can be biased by the presentation of a preceding target on the same trajectory, even when it is of lower contrast than an abrupt, novel distracter. With this improved method for identifying and biasing target selection in CSTMD1, the dragonfly provides an ideal animal model system to probe the neuronal mechanisms underlying selective attention.SIGNIFICANCE STATEMENT We present the first application of frequency tagging to intracellular neuronal recordings, demonstrating that the frequency component of a stimulus is encoded in the spiking response of an individual neuron. Using this technique as an identifier, we demonstrate that CSTMD1 'locks on' to a selected target and encodes the absolute strength of this target, even in the presence of abruptly appearing, high-contrast distracters. The underlying mechanism also permits the selection mechanism to switch between targets mid-trial, even among equivalent targets. Together, these results demonstrate greater complexity in this selective attention system than would be expected in a winner-takes-all network. These results are in contrast to typical findings in the primate and avian brain, but display intriguing resemblance to observations in human psychophysics.

Properties of predictive gain modulation in a dragonfly visual neuron

Dragonflies pursue and capture tiny prey and conspecifics with extremely high success rates. These moving targets represent a small visual signal on the retina and successful chases require accurate detection and amplification by downstream neuronal circuits. This amplification has been observed in a population of neurons called small target motion detectors (STMDs), through a mechanism we term predictive gain modulation. As targets drift through the neuron's receptive field, spike frequency builds slowly over time. This increased likelihood of spiking or gain is modulated across the receptive field, enhancing sensitivity just ahead of the target's path, with suppression of activity in the remaining surround. Whilst some properties of this mechanism have been described, it is not yet known which stimulus parameters modulate the amount of response gain. Previous work suggested that the strength of gain enhancement was predominantly determined by the duration of the target's prior path. Here, we show that predictive gain modulation is more than a slow build-up of responses over time. Rather, the strength of gain is dependent on the velocity of a prior stimulus combined with the current stimulus attributes (e.g. angular size). We also describe response variability as a major challenge of target-detecting neurons and propose that the role of predictive gain modulation is to drive neurons towards response saturation, thus minimising neuronal variability despite noisy visual input signals.

A neuronal correlate of insect stereopsis

A puzzle for neuroscience—and robotics—is how insects achieve surprisingly complex behaviours with such tiny brains. One example is depth perception via binocular stereopsis in the praying mantis, a predatory insect. Praying mantids use stereopsis, the computation of distances from disparities between the two retinal images, to trigger a raptorial strike of their forelegs when prey is within reach. The neuronal basis of this ability is entirely unknown. Here we show the first evidence that individual neurons in the praying mantis brain are tuned to specific disparities and eccentricities, and thus locations in 3D-space. Like disparity-tuned cortical cells in vertebrates, the responses of these mantis neurons are consistent with linear summation of binocular inputs followed by an output nonlinearity. Our study not only proves the existence of disparity sensitive neurons in an insect brain, it also reveals feedback connections hitherto undiscovered in any animal species.

The praying mantis, a predatory insect, estimates depth via binocular vision. In this way, the animal decides whether prey is within reach. Here, the authors explore the neural correlates of binocular distance estimation and report that individual neurons are tuned to specific locations in 3D space.

Towards Computational Models and Applications of Insect Visual Systems for Motion Perception: A Review

Motion perception is a critical capability determining a variety of aspects of insects' life, including avoiding predators, foraging, and so forth. A good number of motion detectors have been identified in the insects' visual pathways. Computational modeling of these motion detectors has not only been providing effective solutions to artificial intelligence, but also benefiting the understanding of complicated biological visual systems. These biological mechanisms through millions of years of evolutionary development will have formed solid modules for constructing dynamic vision systems for future intelligent machines. This article reviews the computational motion perception models originating from biological research on insects' visual systems in the literature. These motion perception models or neural networks consist of the looming-sensitive neuronal models of lobula giant movement detectors (LGMDs) in locusts, the translation-sensitive neural systems of direction-selective neurons (DSNs) in fruit flies, bees, and locusts, and the small-target motion detectors (STMDs) in dragonflies and hoverflies. We also review the applications of these models to robots and vehicles. Through these modeling studies, we summarize the methodologies that generate different direction and size selectivity in motion perception. Finally, we discuss multiple systems integration and hardware realization of these bio-inspired motion perception models.

Sensorimotor Transformations Underlying Variability in Song Intensity during Drosophila Courtship

Diverse animal species, from insects to humans, utilize acoustic signals for communication. Studies of the neural basis for song or speech production have focused almost exclusively on the generation of spectral and temporal patterns, but animals can also adjust acoustic signal intensity when communicating. For example, humans naturally regulate the loudness of speech in accord with a visual estimate of receiver distance. The underlying mechanisms for this ability remain uncharacterized in any system. Here, we show that Drosophila males modulate courtship song amplitude with female distance, and we investigate each stage of the sensorimotor transformation underlying this behavior, from the detection of particular visual stimulus features and the timescales of sensory processing to the modulation of neural and muscle activity that generates song. Our results demonstrate an unanticipated level of control in insect acoustic communication and uncover novel computations and mechanisms underlying the regulation of acoustic signal intensity.

Properties of neuronal facilitation that improve target tracking in natural pursuit simulations

Although flying insects have limited visual acuity (approx. 1°) and relatively small brains, many species pursue tiny targets against cluttered backgrounds with high success. Our previous computational model, inspired by electrophysiological recordings from insect 'small target motion detector' (STMD) neurons, did not account for several key properties described from the biological system. These include the recent observations of response 'facilitation' (a slow build-up of response to targets that move on long, continuous trajectories) and 'selective attention', a competitive mechanism that selects one target from alternatives. Here, we present an elaborated STMD-inspired model, implemented in a closed loop target-tracking system that uses an active saccadic gaze fixation strategy inspired by insect pursuit. We test this system against heavily cluttered natural scenes. Inclusion of facilitation not only substantially improves success for even short-duration pursuits, but it also enhances the ability to 'attend' to one target in the presence of distracters. Our model predicts optimal facilitation parameters that are static in space and dynamic in time, changing with respect to the amount of background clutter and the intended purpose of the pursuit. Our results provide insights into insect neurophysiology and show the potential of this algorithm for implementation in artificial visual systems and robotic applications.

Contrast sensitivity and the detection of moving patterns and features

Theories based on optimal sampling by the retina have been widely applied to visual ecology at the level of the optics of the eye, supported by visual behaviour. This leads to speculation about the additional processing that must lie in between-in the brain itself. But fewer studies have adopted a quantitative approach to evaluating the detectability of specific features in these neural pathways. We briefly review this approach with a focus on contrast sensitivity of two parallel pathways for motion processing in insects, one used for analysis of wide-field optic flow, the other for detection of small features. We further use a combination of optical modelling of image blur and physiological recording from both photoreceptors and higher-order small target motion detector neurons sensitive to small targets to show that such neurons operate right at the limits imposed by the optics of the eye and the noise level of single photoreceptors. Despite this, and the limitation of only being able to use information from adjacent receptors to detect target motion, they achieve a contrast sensitivity that rivals that of wide-field motion sensitive pathways in either insects or vertebrates-among the highest in absolute terms seen in any animal.

Correlation between OFF and ON channels underlies dark target selectivity in an insect visual system

In both vertebrates and invertebrates, evidence supports separation of luminance increments and decrements (ON and OFF channels) in early stages of visual processing (Hartline, 1938; Joesch et al., 2010); however, less is known about how these parallel pathways are recombined to encode form and motion. In Drosophila, genetic knockdown of inputs to putative ON and OFF pathways and direct recording from downstream neurons in the wide-field motion pathway reveal that local elementary motion detectors exist in pairs that separately correlate contrast polarity channels, ON with ON and OFF with OFF (Joesch et al., 2013). However, behavioral responses to reverse-phi motion of discrete features reveal additional correlations of the opposite signs (Clark et al., 2011). We here present intracellular recordings from feature detecting neurons in the dragonfly that provide direct physiological evidence for the correlation of OFF and ON pathways. These neurons show clear polarity selectivity for feature contrast, responding strongly to targets that are darker than the background and only weakly to dark contrasting edges. These dark target responses are much stronger than the linear combination of responses to ON and OFF edges. We compare these data with output from elementary motion detector-based models (Eichner et al., 2011; Clark et al., 2011), with and without stages of strong center-surround antagonism. Our data support an alternative elementary small target motion detector model, which derives dark target selectivity from the correlation of a delayed OFF with an un-delayed ON signal at each individual visual processing unit (Wiederman et al., 2008, 2009).

Invertebrate vision: peripheral adaptation to repeated object motion

Visual systems adapt rapidly to objects moving repeatedly within the visual field, because such objects are likely irrelevant. In the crab, the neural switch for such adaptation has been found to take place at a surprisingly early stage of the visual processing pathway.

Flying fruit flies correct for visual sideslip depending on relative speed of forward optic flow

As a fly flies through its environment, static objects produce moving images on its retina, and this optic flow is essential for steering and course corrections. Different types of rotation and translation produce unique flow fields, which fly brains are wired to identify. However, a feature of optic flow unique to translational motion is that adjacent images may move across the retina at different speeds, depending on their distance from the observer. Many insects take advantage of this depth cue, called motion parallax, to determine the distance to objects. We wanted to know if differential object speeds affect the corrective responses of fruit flies when they experience unplanned course deviations. We presented tethered flying flies with optic flow and measured their corrective responses to sideways perturbations of images with different relative forward speeds. We found that flying flies attend to the relative speed of dots during forward motion, and adjust their corrective responses to sideslip deviations depending on this cue. With no other distinguishing features (such as brightness or size), flies mounted a greater response to sideways deviations that were signaled by faster moving dots in the forward flow field, those that appeared radially closer by their speeds. This is consistent with the interpretation that fruit flies attend to seemingly nearer objects, and correct more strongly when they indicate a perturbation.

Spatial vision in insects is facilitated by shaping the dynamics of visual input through behavioral action

Insects such as flies or bees, with their miniature brains, are able to control highly aerobatic flight maneuvres and to solve spatial vision tasks, such as avoiding collisions with obstacles, landing on objects, or even localizing a previously learnt inconspicuous goal on the basis of environmental cues. With regard to solving such spatial tasks, these insects still outperform man-made autonomous flying systems. To accomplish their extraordinary performance, flies and bees have been shown by their characteristic behavioral actions to actively shape the dynamics of the image flow on their eyes ("optic flow"). The neural processing of information about the spatial layout of the environment is greatly facilitated by segregating the rotational from the translational optic flow component through a saccadic flight and gaze strategy. This active vision strategy thus enables the nervous system to solve apparently complex spatial vision tasks in a particularly efficient and parsimonious way. The key idea of this review is that biological agents, such as flies or bees, acquire at least part of their strength as autonomous systems through active interactions with their environment and not by simply processing passively gained information about the world. These agent-environment interactions lead to adaptive behavior in surroundings of a wide range of complexity. Animals with even tiny brains, such as insects, are capable of performing extraordinarily well in their behavioral contexts by making optimal use of the closed action-perception loop. Model simulations and robotic implementations show that the smart biological mechanisms of motion computation and visually-guided flight control might be helpful to find technical solutions, for example, when designing micro air vehicles carrying a miniaturized, low-weight on-board processor.

Facilitation of dragonfly target-detecting neurons by slow moving features on continuous paths

Dragonflies detect and pursue targets such as other insects for feeding and conspecific interaction. They have a class of neurons highly specialized for this task in their lobula, the “small target motion detecting” (STMD) neurons. One such neuron, CSTMD1, reaches maximum response slowly over hundreds of milliseconds of target motion. Recording the intracellular response from CSTMD1 and a second neuron in this system, BSTMD1, we determined that for the neurons to reach maximum response levels, target motion must produce sequential local activation of elementary motion detecting elements. This facilitation effect is most pronounced when targets move at velocities slower than what was previously thought to be optimal. It is completely disrupted if targets are instantaneously displaced a few degrees from their current location. Additionally, we utilize a simple computational model to discount the parsimonious hypothesis that CSTMD1's slow build-up to maximum response is due to it incorporating a sluggish neural delay filter. Whilst the observed facilitation may be too slow to play a role in prey pursuit flights, which are typically rapidly resolved, we hypothesize that it helps maintain elevated sensitivity during prolonged, aerobatically intricate conspecific pursuits. Since the effect seems to be localized, it most likely enhances the relative salience of the most recently “seen” locations during such pursuit flights.

Neuronal encoding of object and distance information: a model simulation study on naturalistic optic flow processing

We developed a model of the input circuitry of the FD1 cell, an identified motion-sensitive interneuron in the blowfly's visual system. The model circuit successfully reproduces the FD1 cell's most conspicuous property: its larger responses to objects than to spatially extended patterns. The model circuit also mimics the time-dependent responses of FD1 to dynamically complex naturalistic stimuli, shaped by the blowfly's saccadic flight and gaze strategy: the FD1 responses are enhanced when, as a consequence of self-motion, a nearby object crosses the receptive field during intersaccadic intervals. Moreover, the model predicts that these object-induced responses are superimposed by pronounced pattern-dependent fluctuations during movements on virtual test flights in a three-dimensional environment with systematic modifications of the environmental patterns. Hence, the FD1 cell is predicted to detect not unambiguously objects defined by the spatial layout of the environment, but to be also sensitive to objects distinguished by textural features. These ambiguous detection abilities suggest an encoding of information about objects-irrespective of the features by which the objects are defined-by a population of cells, with the FD1 cell presumably playing a prominent role in such an ensemble.

Visual control of navigation in insects and its relevance for robotics

Flying insects display remarkable agility, despite their diminutive eyes and brains. This review describes our growing understanding of how these creatures use visual information to stabilize flight, avoid collisions with objects, regulate flight speed, detect and intercept other flying insects such as mates or prey, navigate to a distant food source, and orchestrate flawless landings. It also outlines the ways in which these insights are now being used to develop novel, biologically inspired strategies for the guidance of autonomous, airborne vehicles.

Neural specializations for small target detection in insects

Despite being equipped with low-resolution eyes and tiny brains, many insects show exquisite abilities to detect and pursue targets even in highly textured surrounds. Target tracking behavior is subserved by neurons that are sharply tuned to the motion of small high-contrast targets. These neurons respond robustly to target motion, even against self-generated optic flow. A recent model, supported by neurophysiology, generates target selectivity by being sharply tuned to the unique spatiotemporal profile associated with target motion. Target neurons are likely connected in a complex network where some provide more direct output to behavior, whereas others serve an inter-regulatory role. These interactions may regulate attention and aid in the robust detection of targets in clutter observed in behavior.

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.

Discrimination of features in natural scenes by a dragonfly neuron

Flying insects engage in spectacular high-speed pursuit of targets, requiring visual discrimination of moving objects against cluttered backgrounds. As a first step toward understanding the neural basis for this complex task, we used computational modeling of insect small target motion detector (STMD) neurons to predict responses to features within natural scenes and then compared this with responses recorded from an identified STMD neuron in the dragonfly brain (Hemicordulia tau). A surprising model prediction confirmed by our electrophysiological recordings is that even heavily cluttered scenes contain very few features that excite these neurons, due largely to their exquisite tuning for small features. We also show that very subtle manipulations of the image cause dramatic changes in the response of this neuron, because of the complex inhibitory and facilitatory interactions within the receptive field.

Spatial facilitation by a high-performance dragonfly target-detecting neuron

Many animals visualize and track small moving targets at long distances—be they prey, approaching predators or conspecifics. Insects are an excellent model system for investigating the neural mechanisms that have evolved for this challenging task. Specialized small target motion detector (STMD) neurons in the optic lobes of the insect brain respond strongly even when the target size is below the resolution limit of the eye. Many STMDs also respond robustly to small targets against complex stationary or moving backgrounds. We hypothesized that this requires a complex mechanism to avoid breakthrough responses by background features, and yet to adequately amplify the weak signal of tiny targets. We compared responses of dragonfly STMD neurons to small targets that begin moving within the receptive field with responses to targets that approach the same location along longer trajectories. We find that responses along longer trajectories are strongly facilitated by a mechanism that builds up slowly over several hundred milliseconds. This allows the neurons to give sustained responses to continuous target motion, thus providing a possible explanation for their extraordinary sensitivity.

Local and large-range inhibition in feature detection

Lateral inhibition is perhaps the most ubiquitous of neuronal mechanisms, having been demonstrated in early stages of processing in many different sensory pathways of both mammals and invertebrates. Recent work challenges the long-standing view that assumes that similar mechanisms operate to tune neuronal responses to higher order properties. Scant evidence for lateral inhibition exists beyond the level of the most peripheral stages of visual processing, leading to suggestions that many features of the tuning of higher order visual neurons can be accounted for by the receptive field and other intrinsic coding properties of visual neurons. Using insect target neurons as a model, we present unequivocal evidence that feature tuning is shaped not by intrinsic properties but by potent spatial lateral inhibition operating well beyond the first stages of visual processing. In addition, we present evidence for a second form of higher-order spatial inhibition--a long-range interocular transfer of information that we argue serves a role in establishing interocular rivalry and thus potentially a neural substrate for directing attention to single targets in the presence of distracters. In so doing, we demonstrate not just one, but two levels of spatial inhibition acting beyond the level of peripheral processing.

Feature detection and the hypercomplex property in insects

Discerning a target amongst visual 'clutter' is a complicated task that has been elegantly solved by flying insects, as evidenced by their mid-air interactions with conspecifics and prey. The neurophysiology of small-target motion detectors (STMDs) underlying these complex behaviors has recently been described and suggests that insects use mechanisms similar to those of hypercomplex cells of the mammalian visual cortex to achieve target-specific tuning. Cortical hypercomplex cells are end-stopped, which means that they respond optimally to small moving targets, with responses to extended bars attenuated. We review not only the underlying mechanisms involved in this tuning but also how recently proposed models provide a possible explanation for another remarkable property of these neurons - their ability to respond robustly to the motion of targets even against moving backgrounds.

Distributed dendritic processing facilitates object detection: a computational analysis on the visual system of the fly

Background

Detecting objects is an important task when moving through a natural environment. Flies, for example, may land on salient objects or may avoid collisions with them. The neuronal ensemble of Figure Detection cells (FD-cells) in the visual system of the fly is likely to be involved in controlling these behaviours, as these cells are more sensitive to objects than to extended background structures. Until now the computations in the presynaptic neuronal network of FD-cells and, in particular, the functional significance of the experimentally established distributed dendritic processing of excitatory and inhibitory inputs is not understood.

Methodology/Principal Findings

We use model simulations to analyse the neuronal computations responsible for the preference of FD-cells for small objects. We employed a new modelling approach which allowed us to account for the spatial spread of electrical signals in the dendrites while avoiding detailed compartmental modelling. The models are based on available physiological and anatomical data. Three models were tested each implementing an inhibitory neural circuit, but differing by the spatial arrangement of the inhibitory interaction. Parameter optimisation with an evolutionary algorithm revealed that only distributed dendritic processing satisfies the constraints arising from electrophysiological experiments. In contrast to a direct dendro-dendritic inhibition of the FD-cell (Direct Distributed Inhibition model), an inhibition of its presynaptic retinotopic elements (Indirect Distributed Inhibition model) requires smaller changes in input resistance in the inhibited neurons during visual stimulation.

Conclusions/Significance

Distributed dendritic inhibition of retinotopic elements as implemented in our Indirect Distributed Inhibition model is the most plausible wiring scheme for the neuronal circuit of FD-cells. This microcircuit is computationally similar to lateral inhibition between the retinotopic elements. Hence, distributed inhibition might be an alternative explanation of perceptual phenomena currently explained by lateral inhibition networks.