Near range path navigation using LGMD visual neural networks

In this paper, we proposed a method for near range path navigation for a mobile robot by using a pair of biologically inspired visual neural network – lobula giant movement detector (LGMD). In the proposed binocular style visual system, each LGMD processes images covering a part of the wide field of view and extracts relevant visual cues as its output. The outputs from the two LGMDs are compared and translated into executable motor commands to control the wheels of the robot in real time. Stronger signal from the LGMD in one side pushes the robot away from this side step by step; therefore, the robot can navigate in a visual environment naturally with the proposed vision system. Our experiments showed that this bio-inspired system worked well in different scenarios

Redundant neural vision systems: competing for collision recognition roles

Ability to detect collisions is vital for future robots that interact with humans in complex visual environments. Lobula giant movement detectors (LGMD) and directional selective neurons (DSNs) are two types of identified neurons found in the visual pathways of insects such as locusts. Recent modelling studies showed that the LGMD or grouped DSNs could each be tuned for collision recognition. In both biological and artificial vision systems, however, which one should play the collision recognition role and the way the two types of specialized visual neurons could be functioning together are not clear. In this modeling study, we compared the competence of the LGMD and the DSNs, and also investigate the cooperation of the two neural vision systems for collision recognition via artificial evolution. We implemented three types of collision recognition neural subsystems – the LGMD, the DSNs and a hybrid system which combines the LGMD and the DSNs subsystems together, in each individual agent. A switch gene determines which of the three redundant neural subsystems plays the collision recognition role. We found that, in both robotics and driving environments, the LGMD was able to build up its ability for collision recognition quickly and robustly therefore reducing the chance of other types of neural networks to play the same role. The results suggest that the LGMD neural network could be the ideal model to be realized in hardware for collision recognition

Looming detection by identified visual interneurons during larval development of the locust Locusta migratoria

Insect larvae clearly react to visual stimuli, but the ability of any visual neuron in a newly hatched insect to respond selectively to particular stimuli has not been directly tested. We characterised a pair of neurons in locust larvae that have been extensively studied in adults, where they are known to respond selectively to objects approaching on a collision course: the lobula giant motion detector (LGMD) and its postsynaptic partner, the descending contralateral motion detector (DCMD). Our physiological recordings of DCMD axon spikes reveal that at the time of hatching, the neurons already respond selectively to objects approaching the locust and they discriminate between stimulus approach speeds with differences in spike frequency. For a particular approaching stimulus, both the number and peak frequency of spikes increase with instar. In contrast, the number of spikes in responses to receding stimuli decreases with instar, so performance in discriminating approaching from receding stimuli improves as the locust goes through successive moults. In all instars, visual movement over one part of the visual field suppresses a response to movement over another part. Electron microscopy demonstrates that the anatomical substrate for the selective response to approaching stimuli is present in all larval instars: small neuronal processes carrying information from the eye make synapses both onto LGMD dendrites and with each other, providing pathways for lateral inhibition that shape selectivity for approaching objects.Fil: Simmons, Peter J.. University of Newcastle; Reino UnidoFil: Sztarker, Julieta. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Fisiología, Biología Molecular y Neurociencias. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Fisiología, Biología Molecular y Neurociencias; ArgentinaFil: Rind, F. Claire. University of Newcastle; Reino Unid

Predator versus Prey:Locust Looming-Detector Neuron and Behavioural Responses to Stimuli Representing Attacking Bird Predators

Many arthropods possess escape-triggering neural mechanisms that help them evade predators. These mechanisms are important neuroethological models, but they are rarely investigated using predator-like stimuli because there is often insufficient information on real predator attacks. Locusts possess uniquely identifiable visual neurons (the descending contralateral movement detectors, DCMDs) that are well-studied looming motion detectors. The DCMDs trigger ‘glides’ in flying locusts, which are hypothesised to be appropriate last-ditch responses to the looms of avian predators. To date it has not been possible to study glides in response to stimuli simulating bird attacks because such attacks have not been characterised. We analyse video of wild black kites attacking flying locusts, and estimate kite attack speeds of 10.8±1.4 m/s. We estimate that the loom of a kite’s thorax towards a locust at these speeds should be characterised by a relatively low ratio of half size to speed (l/|v|) in the range 4–17 ms. Peak DCMD spike rate and gliding response occurrence are known to increase as l/|v| decreases for simple looming shapes. Using simulated looming discs, we investigate these trends and show that both DCMD and behavioural responses are strong to stimuli with kite-like l/|v| ratios. Adding wings to looming discs to produce a more realistic stimulus shape did not disrupt the overall relationships of DCMD and gliding occurrence to stimulus l/|v|. However, adding wings to looming discs did slightly reduce high frequency DCMD spike rates in the final stages of object approach, and slightly delay glide initiation. Looming discs with or without wings triggered glides closer to the time of collision as l/|v| declined, and relatively infrequently before collision at very low l/|v|. However, the performance of this system is in line with expectations for a last-ditch escape response

Reactive direction control for a mobile robot: A locust-like control of escape direction emerges when a bilateral pair of model locust visual neurons are integrated

Locusts possess a bilateral pair of uniquely identifiable visual neurons that respond vigorously to the image of an approaching object. These neurons are called the lobula giant movement detectors (LGMDs). The locust LGMDs have been extensively studied and this has lead to the development of an LGMD model for use as an artificial collision detector in robotic applications. To date, robots have been equipped with only a single, central artificial LGMD sensor, and this triggers a non-directional stop or rotation when a potentially colliding object is detected. Clearly, for a robot to behave autonomously, it must react differently to stimuli approaching from different directions. In this study, we implement a bilateral pair of LGMD models in Khepera robots equipped with normal and panoramic cameras. We integrate the responses of these LGMD models using methodologies inspired by research on escape direction control in cockroaches. Using ‘randomised winner-take-all’ or ‘steering wheel’ algorithms for LGMD model integration, the khepera robots could escape an approaching threat in real time and with a similar distribution of escape directions as real locusts. We also found that by optimising these algorithms, we could use them to integrate the left and right DCMD responses of real jumping locusts offline and reproduce the actual escape directions that the locusts took in a particular trial. Our results significantly advance the development of an artificial collision detection and evasion system based on the locust LGMD by allowing it reactive control over robot behaviour. The success of this approach may also indicate some important areas to be pursued in future biological research

Structural Organization of the Presynaptic Density at Identified Synapses in the Locust Central Nervous System

In a synaptic active zone, vesicles aggregate around a densely staining structure called the presynaptic density. We focus on its three-dimensional architecture and a major molecular component in the locust. We used electron tomography to study the presynaptic density in synapses made in the brain by identified second-order neuron of the ocelli. Here, vesicles close to the active zone are organized in two rows on either side of the presynaptic density, a level of organization not previously reported in insect central synapses. The row of vesicles that is closest to the density's base includes vesicles docked with the presynaptic membrane and thus presumably ready for release, whereas the outer row of vesicles does not include any that are docked. We show that a locust ortholog of the Drosophila protein Bruchpilot is localized to the presynaptic density, both in the ocellar pathway and compound eye visual neurons. An antibody recognizing the C-terminus of the Bruchpilot ortholog selectively labels filamentous extensions of the presynaptic density that reach out toward vesicles. Previous studies on Bruchpilot have focused on its role in neuromuscular junctions in Drosophila, and our study shows it is also a major functional component of presynaptic densities in the central nervous system of an evolutionarily distant insect. Our study thus reveals Bruchpilot executes similar functions in synapses that can sustain transmission of small graded potentials as well as those relaying large, spike-evoked signals. J. Comp. Neurol. 520:384–400, 2012. © 2011 Wiley Periodicals, Inc

A look into the cockpit of the developing locust: Looming detectors and predator avoidance

For many animals, the visual detection of looming stimuli is crucial at any stage of their lives. For example, human babies of only 6 days old display evasive responses to looming stimuli (Bower et al. [1971]: Percept Psychophys 9: 193-196). This means the neuronal pathways involved in looming detection should mature early in life. Locusts have been used extensively to examine the neural circuits and mechanisms involved in sensing looming stimuli and triggering visually evoked evasive actions, making them ideal subjects in which to investigate the development of looming sensitivity. Two lobula giant movement detectors (LGMD) neurons have been identified in the lobula region of the locust visual system: the LGMD1 neuron responds selectively to looming stimuli and provides information that contributes to evasive responses such as jumping and emergency glides. The LGMD2 responds to looming stimuli and shares many response properties with the LGMD1. Both neurons have only been described in the adult. In this study, we describe a practical method combining classical staining techniques and 3D neuronal reconstructions that can be used, even in small insects, to reveal detailed anatomy of individual neurons. We have used it to analyze the anatomy of the fan-shaped dendritic tree of the LGMD1 and the LGMD2 neurons in all stages of the post-embryonic development of Locusta migratoria. We also analyze changes seen during the ontogeny of escape behaviors triggered by looming stimuli, specially the hiding response.Fil: Sztarker, Julieta. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Fisiología, Biología Molecular y Neurociencias. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Fisiología, Biología Molecular y Neurociencias; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Fisiología, Biología Molecular y Celular; Argentina. University of Newcastle; Reino UnidoFil: Rind, F. Claire. University of Newcastle; Reino Unid

Postsynaptic organizations of directional selective visual neural networks for collision detection

In this paper, we studied the postsynaptic organizations of directional selective visual neurons for collision detection. Directional selective neurons can extract different directional visual motion cues fast and reliably by allowing inhibition spreads to further layers in specific directions with one or several time steps delay. Whether these directional selective neurons can be easily organised for other specific visual tasks is not known. Taking collision detection as the primary visual task, we investigated the postsynaptic organizations of these directional selective neurons through evolutionary processes. The evolved postsynaptic organizations demonstrated robust properties in detecting imminent collisions in complex visual environments with many of which achieved 94% success rate after evolution suggesting active roles in collision detection directional selective neurons and its postsynaptic organizations can play

Visual motion pattern extraction and fusion for collision detection in complex dynamic scenes

Detecting colliding objects in complex dynamic scenes is a difficult task for conventional computer vision techniques. However, visual processing mechanisms in animals such as insects may provide very simple and effective solutions for detecting colliding objects in complex dynamic scenes. In this paper, we propose a robust collision detecting system, which consists of a lobula giant movement detector (LGMD) based neural network and a translating sensitive neural network (TSNN), to recognise objects on a direct collision course in complex dynamic scenes. The LGMD based neural network is specialized for recognizing looming objects that are on a direct collision course. The TSNN, which fuses the extracted visual motion cues from several whole field direction selective neural networks, is only sensitive to translating movements in the dynamic scenes. The looming cue and translating cue revealed by the two specialized visual motion detectors are fused in the present system via a decision making mechanism. In the system, the LGMD plays a key role in detecting imminent collision; the decision from TSNN becomes useful only when a collision alarm has been issued by the LGMD network. Using driving scenarios as an example, we showed that the bio-inspired system can reliably detect imminent colliding objects in complex driving scenes