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

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

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

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 modelling 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 of insects' visual systems in the literature. These motion perception models or neural networks comprise 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, as well as the small target motion detectors (STMDs) in dragonflies and hover flies. We also review the applications of these models to robots and vehicles. Through these modelling studies, we summarise the methodologies that generate different direction and size selectivity in motion perception. At last, we discuss about multiple systems integration and hardware realisation of these bio-inspired motion perception models

Fast Atmosphere-Ocean Model Runs with Large Changes in CO2

How does climate sensitivity vary with the magnitude of climate forcing? This question was investigated with the use of a modified coupled atmosphere-ocean model, whose stability was improved so that the model would accommodate large radiative forcings yet be fast enough to reach rapid equilibrium. Experiments were performed in which atmospheric CO2 was multiplied by powers of 2, from 1/64 to 256 times the 1950 value. From 8 to 32 times, the 1950 CO2, climate sensitivity for doubling CO2 reaches 8 C due to increases in water vapor absorption and cloud top height and to reductions in low level cloud cover. As CO2 amount increases further, sensitivity drops as cloud cover and planetary albedo stabilize. No water vapor-induced runaway greenhouse caused by increased CO2 was found for the range of CO2 examined. With CO2 at or below 1/8 of the 1950 value, runaway sea ice does occur as the planet cascades to a snowball Earth climate with fully ice covered oceans and global mean surface temperatures near 30 C

Trajectory mapping: A tool for validation of trace gas observations

We investigate the effectiveness of trajectory mapping(TM) as a data validation tool. TM combines a dynamical model of the atmosphere with trace gas observations to provide more statistically robust estimates of instrument performance over much broader geographic areas than traditional techniques are able to provide. We present four detailed case studies selected so that the traditional techniques are expected to work well. In each case the TM results are equivalent to or improve upon the measurement comparisons performed with traditional approaches. The TM results are statistically more robust than those achieved using traditional approaches since the TM comparisons occur over a much larger range of geophysical variability. In the first case study we compare ozone data from the Halogen Occultation Experiment (HALOE) with Microwave Limb Sounder(MLS). TM comparisons appear to introduce little to no error as compared to the traditional approach. In the second case study we compare ozone data from HALOE with that from the Stratospheric Aerosol and Gas Experiment TT(SAGE TT). TM results in differences of less than 5% as compared to the traditional approach at altitudes between 18 and 25 km and less than 10% at altitudes between 25 and 40 km.In the third case study we show that ozone profiles generated from HALOE data using TM compare well with profiles from five European ozonesondes. In the fourth case study we evaluate the precision of MLS H20 using TM and find typical precision uncertainties of 3-7% at most latitudes and altitudes. The TM results agree well with previous estimates but are the result of a global analysis of the data rather than an analysis in the limited latitude bands in which traditional approaches work. Finally, sensitivity studies using the MLS H20 data show the following: (1) a combination of forward and backward trajectory calculations minimize uncertainties in isentropic TM; (2) although the uncertainty of the technique increases with trajectory duration,TM calculations of up to 14 days can provide reliable information for use in data validation studies; (3) a correlation coincidence criterion of 400 km produces the best TM results under most circumstances; (4) TM performs well compared to (and sometimes better than) traditional approaches at all latitudes and in most seasons and; (5) TM introduces no statistically significant biases at altitudes between 22 and 40 km

Non-Linear Neuronal Responses as an Emergent Property of Afferent Networks: A Case Study of the Locust Lobula Giant Movement Detector

In principle it appears advantageous for single neurons to perform non-linear operations. Indeed it has been reported that some neurons show signatures of such operations in their electrophysiological response. A particular case in point is the Lobula Giant Movement Detector (LGMD) neuron of the locust, which is reported to locally perform a functional multiplication. Given the wide ramifications of this suggestion with respect to our understanding of neuronal computations, it is essential that this interpretation of the LGMD as a local multiplication unit is thoroughly tested. Here we evaluate an alternative model that tests the hypothesis that the non-linear responses of the LGMD neuron emerge from the interactions of many neurons in the opto-motor processing structure of the locust. We show, by exposing our model to standard LGMD stimulation protocols, that the properties of the LGMD that were seen as a hallmark of local non-linear operations can be explained as emerging from the dynamics of the pre-synaptic network. Moreover, we demonstrate that these properties strongly depend on the details of the synaptic projections from the medulla to the LGMD. From these observations we deduce a number of testable predictions. To assess the real-time properties of our model we applied it to a high-speed robot. These robot results show that our model of the locust opto-motor system is able to reliably stabilize the movement trajectory of the robot and can robustly support collision avoidance. In addition, these behavioural experiments suggest that the emergent non-linear responses of the LGMD neuron enhance the system's collision detection acuity. We show how all reported properties of this neuron are consistently reproduced by this alternative model, and how they emerge from the overall opto-motor processing structure of the locust. Hence, our results propose an alternative view on neuronal computation that emphasizes the network properties as opposed to the local transformations that can be performed by single neurons

An initial intercomparison of atmospheric and oceanic climatology for the ICE-5G and ICE-4G models of LGM paleotopography

This paper investigates the impact of the new ICE-5G paleotopography dataset for Last Glacial Maximum (LGM) conditions on a coupled model simulation of the thermal and dynamical state of the glacial atmosphere and on both land surface and sea surface conditions. The study is based upon coupled climate simulations performed with the ocean–atmosphere–sea ice model of intermediate-complexity Climate de Bilt-coupled large-scale ice–ocean (ECBilt-Clio) model. Four simulations focusing on the Last Glacial Maximum [21 000 calendar years before present (BP)] have been analyzed: a first simulation (LGM-4G) that employed the original ICE-4G ice sheet topography and albedo, and a second simulation (LGM-5G) that employed the newly constructed ice sheet topography, denoted ICE-5G, and its respective albedo. Intercomparison of the results obtained in these experiments demonstrates that the LGM-5G simulation delivers significantly enhanced cooling over Canada compared to the LGM-4G simulation whereas positive temperature anomalies are simulated over southern North America and the northern Atlantic. Moreover, introduction of the ICE-5G topography is shown to lead to a deceleration of the subtropical westerlies and to the development of an intensified ridge over North America, which has a profound effect upon the hydrological cycle. Additionally, two flat ice sheet experiments were carried out to investigate the impact of the ice sheet albedo on global climate. By comparing these experiments with the full LGM simulations, it becomes evident that the climate anomalies between LGM-5G and LGM-4G are mainly driven by changes of the earth’s topography