Search results
Found 12130 matches for
A new computational model illuminates the extraordinary eyes of Phronima.
Vision in the midwater of the open ocean requires animals to perform visual tasks quite unlike those of any other environment. These tasks consist of detecting small, low contrast objects and point sources against a relatively dim and uniform background. Deep-sea animals have evolved many extraordinary visual adaptations to perform these tasks. Linking eye anatomy to specific selective pressures, however, is challenging, not least because of the many difficulties of studying deep-sea animals. Computational modelling of vision, based on detailed morphological reconstructions of animal eyes, along with underwater optics, offers a chance to understand the specific visual capabilities of individual visual systems. Prior to the work presented here, comprehensive models for apposition compound eyes in the mesopelagic, the dominant eye form of crustaceans, were lacking. We adapted a model developed for single-lens eyes and used it to examine how different parameters affect the model's ability to detect point sources and extended objects. This new model also allowed us to examine spatial summation as a means to improve visual performance. Our results identify a trade-off between increased depth range over which eyes function effectively and increased distance at which extended objects can be detected. This trade-off is driven by the size of the ommatidial acceptance angle. We also show that if neighbouring ommatidia have overlapping receptive fields, spatial summation helps with all detection tasks, including the detection of bioluminescent point sources. By applying our model to the apposition compound eyes of Phronima, a mesopelagic hyperiid amphipod, we show that the specialisations of the large medial eyes of Phronima improve both the detection of point sources and of extended objects. The medial eyes outperformed the lateral eyes at every modelled detection task. We suggest that the small visual field size of Phronima's medial eyes and the strong asymmetry between the medial and lateral eyes reflect Phronima's need for effective vision across a large depth range and its habit of living inside a barrel. The barrel's narrow aperture limits the usefulness of a large visual field and has allowed a strong asymmetry between the medial and lateral eyes. The model provides a useful tool for future investigations into the visual abilities of apposition compound eyes in the deep sea.
An autonomous robot inspired by insect neurophysiology pursues moving features in natural environments.
OBJECTIVE: Many computer vision and robotic applications require the implementation of robust and efficient target-tracking algorithms on a moving platform. However, deployment of a real-time system is challenging, even with the computational power of modern hardware. Lightweight and low-powered flying insects, such as dragonflies, track prey or conspecifics within cluttered natural environments, illustrating an efficient biological solution to the target-tracking problem. APPROACH: We used our recent recordings from 'small target motion detector' neurons in the dragonfly brain to inspire the development of a closed-loop target detection and tracking algorithm. This model exploits facilitation, a slow build-up of response to targets which move along long, continuous trajectories, as seen in our electrophysiological data. To test performance in real-world conditions, we implemented this model on a robotic platform that uses active pursuit strategies based on insect behaviour. MAIN RESULTS: Our robot performs robustly in closed-loop pursuit of targets, despite a range of challenging conditions used in our experiments; low contrast targets, heavily cluttered environments and the presence of distracters. We show that the facilitation stage boosts responses to targets moving along continuous trajectories, improving contrast sensitivity and detection of small moving targets against textured backgrounds. Moreover, the temporal properties of facilitation play a useful role in handling vibration of the robotic platform. We also show that the adoption of feed-forward models which predict the sensory consequences of self-movement can significantly improve target detection during saccadic movements. SIGNIFICANCE: Our results provide insight into the neuronal mechanisms that underlie biological target detection and selection (from a moving platform), as well as highlight the effectiveness of our bio-inspired algorithm in an artificial visual system.
Performance of an insect-inspired target tracker in natural conditions
Robust and efficient target-tracking algorithms embedded on moving platforms, are a requirement for many computer vision and robotic applications. However, deployment of a real-time system is challenging, even with the computational power of modern hardware. As inspiration, we look to biological lightweight solutions-lightweight and low-powered flying insects. For example, dragonflies pursue prey and mates within cluttered, natural environments, deftly selecting their target amidst swarms. In our laboratory, we study the physiology and morphology of dragonfly 'small target motion detector' neurons likely to underlie this pursuit behaviour. Here we describe our insect-inspired tracking model derived from these data and compare its efficacy and efficiency with state-of-the-art engineering models. For model inputs, we use both publicly available video sequences, as well as our own task-specific dataset (small targets embedded within natural scenes). In the context of the tracking problem, we describe differences in object statistics within the video sequences. For the general dataset, our model often locks on to small components of larger objects, tracking these moving features. When input imagery includes small moving targets, for which our highly nonlinear filtering is matched, the robustness outperforms state-of-the-art trackers. In all scenarios, our insect-inspired tracker runs at least twice the speed of the comparison algorithms.
Contrast sensitivity, visual acuity and the effect of behavioural state on optokinetic gain in fiddler crabs.
Most animals rely on visual information for a variety of everyday tasks. The information available to a visual system depends in part on its spatial resolving power and contrast sensitivity. Because of their competing demands for physical space within an eye, these traits cannot simultaneously be improved without increasing overall eye size. The contrast sensitivity function is an integrated measure of visual performance that measures both resolution and contrast sensitivity. Its measurement helps us identify how different species have made a trade-off between contrast sensitivity and spatial resolution. It further allows us to identify the evolutionary drivers of sensory processing and visually mediated behaviour. Here, we measured the contrast sensitivity function of the fiddler crab Gelasimus dampieri using its optokinetic responses to wide-field moving sinusoidal intensity gratings of different orientations, spatial frequencies, contrasts and speeds. We further tested whether the behavioural state of the crabs (i.e. whether crabs are actively walking or not) affects their optokinetic gain and contrast sensitivity. Our results from a group of five crabs suggest a minimum perceived contrast of 6% and a horizontal and vertical visual acuity of 0.4 cyc deg-1 and 0.28 cyc deg-1, respectively, in the crabs' region of maximum optomotor sensitivity. Optokinetic gain increased in moving crabs compared with restrained crabs, adding another example of the importance of naturalistic approaches when studying the performance of animals.
Behavioural and neural responses of crabs show evidence for selective attention in predator avoidance.
Selective attention, the ability to focus on a specific stimulus and suppress distractions, plays a fundamental role for animals in many contexts, such as mating, feeding, and predation. Within natural environments, animals are often confronted with multiple stimuli of potential importance. Such a situation significantly complicates the decision-making process and imposes conflicting information on neural systems. In the context of predation, selectively attending to one of multiple threats is one possible solution. However, how animals make such escape decisions is rarely studied. A previous field study on the fiddler crab, Gelasimus dampieri, provided evidence of selective attention in the context of escape decisions. To identify the underlying mechanisms that guide their escape decisions, we measured the crabs' behavioural and neural responses to either a single, or two simultaneously approaching looming stimuli. The two stimuli were either identical or differed in contrast to represent different levels of threat certainty. Although our behavioural data provides some evidence that crabs perceive signals from both stimuli, we show that both the crabs and their looming-sensitive neurons almost exclusively respond to only one of two simultaneous threats. The crabs' body orientation played an important role in their decision about which stimulus to run away from. When faced with two stimuli of differing contrasts, both neurons and crabs were much more likely to respond to the stimulus with the higher contrast. Our data provides evidence that the crabs' looming-sensitive neurons play an important part in the mechanism that drives their selective attention in the context of predation. Our results support previous suggestions that the crabs' escape direction is calculated downstream of their looming-sensitive neurons by means of a population vector of the looming sensitive neuronal ensemble.
Evidence of predictive selective attention in fiddler crabs during escape in the natural environment.
Selective attention is of fundamental relevance to animals for performing a diversity of tasks such as mating, feeding, predation and avoiding predators. Within natural environments, prey animals are often exposed to multiple, simultaneous threats, which significantly complicates the decision-making process. However, selective attention is rarely studied in complex, natural environments or in the context of escape responses. We therefore asked how relatively simple animals integrate the information from multiple, concurrent threatening events. Do they identify and respond only to what they perceive as the most dangerous threat, or do they respond to multiple stimuli at the same time? Do simultaneous threats evoke an earlier or stronger response than single threats? We investigated these questions by conducting field experiments and compared escape responses of the fiddler crab Gelasimus dampieri when faced with either a single or two simultaneously approaching dummy predators. We used the dummies' approach trajectories to manipulate the threat level; a directly approaching dummy indicated higher risk while a tangentially approaching dummy that passed the crabs at a distance represented a lower risk. The crabs responded later, but on average more often, when approached more directly. However, when confronted with the two dummies simultaneously, the crabs responded as if approached only by the directly approaching dummy. This suggests that the crabs are able to predict how close the dummy's trajectory is to a collision course and selectively suppress their normally earlier response to the less dangerous dummy. We thus provide evidence of predictive selective attention within a natural environment.
A new method for mapping spatial resolution in compound eyes suggests two visual streaks in fiddler crabs.
Visual systems play a vital role in guiding the behaviour of animals. Understanding the visual information animals are able to acquire is therefore key to understanding their visually mediated decision making. Compound eyes, the dominant eye type in arthropods, are inherently low-resolution structures. Their ability to resolve spatial detail depends on sampling resolution (interommatidial angle) and the quality of ommatidial optics. Current techniques for estimating interommatidial angles are difficult, and generally require in vivo measurements. Here, we present a new method for estimating interommatidial angles based on the detailed analysis of 3D micro-computed tomography images of fixed samples. Using custom-made MATLAB software, we determined the optical axes of individual ommatidia and projected these axes into the 3D space around the animal. The combined viewing directions of all ommatidia, estimated from geometrical optics, allowed us to estimate interommatidial angles and map the animal's sampling resolution across its entire visual field. The resulting topographic representations of visual acuity match very closely the previously published data obtained from both fiddler and grapsid crabs. However, the new method provides additional detail that was not previously detectable and reveals that fiddler crabs, rather than having a single horizontal visual streak as is common in flat-world inhabitants, probably have two parallel streaks located just above and below the visual horizon. A key advantage of our approach is that it can be used on appropriately preserved specimens, allowing the technique to be applied to animals such as deep-sea crustaceans that are inaccessible or unsuitable for in vivo approaches.
Properties of neuronal facilitation that improve target tracking in natural pursuit simulations
Although flying insects have limited visual acuity (approx. 18) 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.
Fiddler crabs are unique in timing their escape responses based on speed-dependent visual cues.
Predation risk imposes strong selection pressures on visual systems to quickly and accurately identify the position and movement of potential predators.1,2 Many invertebrates and other small animals, however, have limited capacity for distance perception due to their low spatial resolution and closely situated eyes.3,4 Consequently, they often rely on simplified decision criteria, essentially heuristics or "rules of thumb", to make decisions. The visual cues animals use to make escape decisions are surprisingly consistent, especially among arthropods, with the timing of escape commonly triggered by size-dependent visual cues such as angular size or angular size increment.5,6,7,8,9,10 Angular size, however, confuses predator size and distance and provides no information about the speed of the attack. Here, we show that fiddler crabs (Gelasimus dampieri) are unique among the arthropods studied to date as they timed their escape response based on the speed of an object's angular expansion. The crabs responded reliably by running away from visual stimuli that expanded at approximately 1.7 degrees/s, irrespective of stimulus size, speed, or its initial distance from the crabs. Though the threshold expansion speed was consistent across different stimulus conditions, we found that the escape timing was modulated by the elevation at which the stimulus approached, suggesting that other risk factors can bias the expansion speed threshold. The results suggest that the visual escape cues used by arthropods are less conserved than previously thought and that lifestyle and environment are significant drivers determining the escape cues used by different species.
Photoreceptors and diurnal variation in spectral sensitivity in the fiddler crab Gelasimus dampieri.
Colour signals, and the ability to detect them, are important for many animals and can be vital to their survival and fitness. Fiddler crabs use colour information to detect and recognise conspecifics, but their colour vision capabilities remain unclear. Many studies have attempted to measure their spectral sensitivity and identify contributing retinular cells, but the existing evidence is inconclusive. We used electroretinogram (ERG) measurements and intracellular recordings from retinular cells to estimate the spectral sensitivity of Gelasimus dampieri and to track diurnal changes in spectral sensitivity. G. dampieri has a broad spectral sensitivity and is most sensitive to wavelengths between 420 and 460 nm. Selective adaptation experiments uncovered an ultraviolet (UV) retinular cell with a peak sensitivity shorter than 360 nm. The species' spectral sensitivity above 400 nm is too broad to be fitted by a single visual pigment and using optical modelling, we provide evidence that at least two medium-wavelength sensitive (MWS) visual pigments are contained within a second blue-green sensitive retinular cell. We also found a ∼25 nm diurnal shift in spectral sensitivity towards longer wavelengths in the evening in both ERG and intracellular recordings. Whether the shift is caused by screening pigment migration or changes in opsin expression remains unclear, but the observation shows the diel dynamism of colour vision in this species. Together, these findings support the notion that G. dampieri possesses the minimum requirement for colour vision, with UV and blue/green receptors, and help to explain some of the inconsistent results of previous research.
A biologically inspired facilitation mechanism enhances the detection and pursuit of targets of varying contrast
Many species of flying insects detect and chase prey or conspecifics within a visually cluttered surround, e.g. for predation, territorial or mating behavior. We modeled such detection and pursuit for small moving targets, and tested it within a closed-loop, virtual reality flight arena. Our model is inspired directly by electrophysiological recordings from 'small target motion detector' (STMD) neurons in the insect brain that are likely to underlie this behavioral task. The front-end uses a variant of a biologically inspired 'elementary' small target motion detector (ESTMD), elaborated to detect targets in natural scenes of both contrast polarities (i.e. both dark and light targets). We also include an additional model for the recently identified physiological 'facilitation' mechanism believed to form the basis for selective attention in insect STMDs, and quantify the improvement this provides for pursuit success and target discriminability over a range of target contrasts.
Robustness and real-time performance of an insect inspired target tracking algorithm under natural conditions
Many computer vision tasks require the implementation of robust and efficient target tracking algorithms. Furthermore, in robotic applications these algorithms must perform whilst on a moving platform (ego motion). Despite the increase in computational processing power, many engineering algorithms are still challenged by real-Time applications. In contrast, lightweight and low-power flying insects, such as dragonflies, can readily chase prey and mates within cluttered natural environments, deftly selecting their target amidst distractors (swarms). In our laboratory, we record from 'target-detecting' neurons in the dragonfly brain that underlie this pursuit behavior. We recently developed a closed-loop target detection and tracking algorithm based on key properties of these neurons. Here we test our insect-inspired tracking model in open-loop against a set of naturalistic sequences and compare its efficacy and efficiency with other state-of-The-Art engineering models. In terms of tracking robustness, our model performs similarly to many of these trackers, yet is at least 3 times more efficient in terms of processing speed.
Performance assessment of an insect-inspired target tracking model in background clutter
Biological visual systems provide excellent examples of robust target detection and tracking mechanisms capable of performing in a wide range of environments. Consequently, they have been sources of inspiration for many artificial vision algorithms. However, testing the robustness of target detection and tracking algorithms is a challenging task due to the diversity of environments for applications of these algorithms. Correlation between image quality metrics and model performance is one way to deal with this problem. Previously we developed a target detection model inspired by physiology of insects and implemented it in a closed loop target tracking algorithm. In the current paper we vary the kinetics of a salience-enhancing element of our algorithm and test its effect on the robustness of our model against different natural images to find the relationship between model performance and background clutter.
Erratum to: Stage 1 registered report: metacognitive asymmetries in visual perception and Stage 2 registered report: metacognitive asymmetries in visual perception.
[This corrects the article DOI: 10.1093/nc/niab005.][This corrects the article DOI: 10.1093/nc/niab025.].
Paradoxical evidence weighting in confidence judgments for detection and discrimination.
When making discrimination decisions between two stimulus categories, subjective confidence judgments are more positively affected by evidence in support of a decision than negatively affected by evidence against it. Recent theoretical proposals suggest that this "positive evidence bias" may be due to observers adopting a detection-like strategy when rating their confidence-one that has functional benefits for metacognition in real-world settings where detectability and discriminability often go hand in hand. However, it is unknown whether, or how, this evidence-weighting asymmetry affects detection decisions about the presence or absence of a stimulus. In four experiments, we first successfully replicate a positive evidence bias in discrimination confidence. We then show that detection decisions and confidence ratings paradoxically suffer from an opposite "negative evidence bias" to negatively weigh evidence even when it is optimal to assign it a positive weight. We show that the two effects are uncorrelated and discuss our findings in relation to models that account for a positive evidence bias as emerging from a confidence-specific heuristic, and alternative models where decision and confidence are generated by the same, Bayes-rational process.
Dissociating the Neural Correlates of Subjective Visibility from Those of Decision Confidence.
A key goal of consciousness science is identifying neural signatures of being aware versus unaware of simple stimuli. This is often investigated in the context of near-threshold detection, with reports of stimulus awareness being linked to heightened activation in a frontoparietal network. However, because of reports of stimulus presence typically being associated with higher confidence than reports of stimulus absence, these results could be explained by frontoparietal regions encoding stimulus visibility, decision confidence, or both. In an exploratory analysis, we leverage fMRI data from 35 human participants (20 females) to disentangle these possibilities. We first show that, whereas stimulus identity was best decoded from the visual cortex, stimulus visibility (presence vs absence) was best decoded from prefrontal regions. To control for effects of confidence, we then selectively sampled trials before decoding to equalize confidence distributions between absence and presence responses. This analysis revealed striking differences in the neural correlates of subjective visibility in PFC ROIs, depending on whether or not differences in confidence were controlled for. We interpret our findings as highlighting the importance of controlling for metacognitive aspects of the decision process in the search for neural correlates of visual awareness.SIGNIFICANCE STATEMENT While much has been learned over the past two decades about the neural basis of visual awareness, the role of the PFC remains a topic of debate. By applying decoding analyses to functional brain imaging data, we show that prefrontal representations of subjective visibility are contaminated by neural correlates of decision confidence. We propose a new analysis method to control for these metacognitive aspects of awareness reports, and use it to reveal confidence-independent correlates of perceptual judgments in a subset of prefrontal areas.
A microfabricated fluorescence-activated cell sorter.
We have demonstrated a disposable microfabricated fluorescence-activated cell sorter (microFACS) for sorting various biological entities. Compared with conventional FACS machines, the microFACS provides higher sensitivity, no cross-contamination, and lower cost. We have used microFACS chips to obtain substantial enrichment of micron-sized fluorescent bead populations of differing colors. Furthermore, we have separated Escherichia coli cells expressing green fluorescent protein from a background of nonfluorescent E. coli cells and shown that the bacteria are viable after extraction from the sorting device. These sorters can function as stand-alone devices or as components of an integrated microanalytical chip.