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

What has been missed for predicting human attention in viewing driving clips?

Recent research progress on the topic of human visual attention allocation in scene perception and its simulation is based mainly on studies with static images. However, natural vision requires us to extract visual information that constantly changes due to egocentric movements or dynamics of the world. It is unclear to what extent spatio-temporal regularity, an inherent regularity in dynamic vision, affects human gaze distribution and saliency computation in visual attention models. In this free-viewing eye-tracking study we manipulated the spatio-temporal regularity of traffic videos by presenting them in normal video sequence, reversed video sequence, normal frame sequence, and randomised frame sequence. The recorded human gaze allocation was then used as the ‘ground truth’ to examine the predictive ability of a number of state-of-the-art visual attention models. The analysis revealed high inter-observer agreement across individual human observers, but all the tested attention models performed significantly worse than humans. The inferior predictability of the models was evident from indistinguishable gaze prediction irrespective of stimuli presentation sequence, and weak central fixation bias. Our findings suggest that a realistic visual attention model for the processing of dynamic scenes should incorporate human visual sensitivity with spatio-temporal regularity and central fixation bias

Improved Collision Perception Neuronal System Model with Adaptive Inhibition Mechanism and Evolutionary Learning

Accurate and timely perception of collision in highly variable environments is still a challenging problem for artificial visual systems. As a source of inspiration, the lobula giant movement detectors (LGMDs) in locust’s visual pathways have been studied intensively, and modelled as quick collision detectors against challenges from various scenarios including vehicles and robots. However, the state-of-the-art LGMD models have not achieved acceptable robustness to deal with more challenging scenarios like the various vehicle driving scenes, due to the lack of adaptive signal processing mechanisms. To address this problem, we propose an improved neuronal system model, called LGMD+, that is featured by novel modelling of spatiotemporal inhibition dynamics with biological plausibilities including 1) lateral inhibitionswithglobalbiasesdefinedbyavariantofGaussiandistribution,spatially,and2)anadaptivefeedforward inhibition mediation pathway, temporally. Accordingly, the LGMD+ performs more effectively to detect merely approaching objects threatening head-on collision risks by appropriately suppressing motion distractors caused by vibrations, near-miss or approaching stimuli with deviations from the centre view. Through evolutionary learning with a systematic dataset of various crash and non-collision driving scenarios, the LGMD+ shows improved robustness outperforming the previous related methods. After evolution, its computational simplicity, flexibility and robustness have also been well demonstrated by real-time experiments of autonomous micro-mobile robots

PCLIPS

CLIPS is an expert system, created specifically to allow rapid implementation of an expert system. CLIPS is written in C, and thus needs a very small amount of memory to run. Parallel CLIPS (PCLIPS) is an extension to CLIPS which is intended to be used in situations where a group of expert systems are expected to run simultaneously and occasionally communicate with each other on an integrated network. PCLIPS is a coarse-grained data distribution system. Its main goal is to take information in one knowledge base and distribute it to other knowledge bases so that all the executing expert systems are able to use that knowledge to solve their disparate problems

The Musashi RNA Binding Proteins Are Regulators of Alternative Splicing and Protein Expression in Photoreceptor Cells

The Musashi (Msi) family of RNA binding proteins consists of two paralogs, Msi1 and Msi2, that are highly conserved across species. The two paralogs have emerged as factors that promote stem cell proliferation by post-transcriptionally regulating translation. In addition to their expression in stem cells, the Musashi proteins are also expressed in postmitotic neurons, including the photoreceptor cells. The Musashi proteins have been observed to maintain high expression levels in the postmitotic photoreceptors within the eye of both invertebrates and vertebrates. These observations suggest an additional role in the maintenance of terminally differentiated neurons. Building upon these observations, we investigated the role of Musashi individually and in combination in mature photoreceptors. Using a tamoxifen-inducible mouse model, I generated single and combined deletion of Msi1 and Msi2 in mature photoreceptor cells. Our results show that the Musashi proteins are required for the function and viability of mature photoreceptors. Global analysis of the Msi1 targets in the retina showed binding to UAG motifs predominantly located in introns and 3’-UTRs. Using RNA-sequencing and proteomics analysis, with the incorporation of the publicly available single-cell RNA seq, we found that in mature photoreceptors, the Musashi enhance the expression of proteins in high demand. Among these targets are proteins needed for the daily regeneration of the light sensory organelle of the photoreceptors. Collectively, the data provide new insights on the targets, possible molecular mechanisms, and function of the Musashi in mature photoreceptors. The results support a model of the Musashi proteins acting as a posttranscriptional activator for protein expression in mature photoreceptors. In the course of our work, an unusual behavior of the 13A4 antibody to prominin-1 (Prom1) prompted us to analyze its epitope. Prom1 is a transmembrane protein with a role in the morphogenesis of photoreceptor outer segment disk membranes. Mutations in the Prom1 gene have resulted in various forms of retinal degeneration affecting rods and cones. Scanning deletion mutagenesis and structural modeling demonstrated that mAB 13A4 recognizes a structural epitope that is affected by the inclusion of the alternative exon 19 during photoreceptor maturation. Consequently, the reactivity of mAB 13A4 towards the photoreceptor specific isoform of PROM1 is significantly reduced on a Western blot leading to gross underestimation of PROM1 protein levels in the retina

Neural coding in the visual system of Drosophila melanogaster: how do small neural populations support visually guided behaviours?

All organisms wishing to survive and reproduce must be able to respond adaptively to a complex, changing world. Yet the computational power available is constrained by biology and evolution, favouring mechanisms that are parsimonious yet robust. Here we investigate the information carried in small populations of visually responsive neurons in Drosophila melanogaster. These so-called ‘ring neurons’, projecting to the ellipsoid body of the central complex, are reported to be necessary for complex visual tasks such as pattern recognition and visual navigation. Recently the receptive fields of these neurons have been mapped, allowing us to investigate how well they can support such behaviours. For instance, in a simulation of classic pattern discrimination experiments, we show that the pattern of output from the ring neurons matches observed fly behaviour. However, performance of the neurons (as with flies) is not perfect and can be easily improved with the addition of extra neurons, suggesting the neurons’ receptive fields are not optimised for recognising abstract shapes, a conclusion which casts doubt on cognitive explanations of fly behaviour in pattern recognition assays. Using artificial neural networks, we then assess how easy it is to decode more general information about stimulus shape from the ring neuron population codes. We show that these neurons are well suited for encoding information about size, position and orientation, which are more relevant behavioural parameters for a fly than abstract pattern properties. This leads us to suggest that in order to understand the properties of neural systems, one must consider how perceptual circuits put information at the service of behaviour

LGMD and DSNs neural networks integration for collision predication

An ability to predict collisions is essential for current vehicles and autonomous robots. In this paper, an integrated collision predication system is proposed based on neural subsystems inspired from Lobula giant movement detector (LGMD) and directional selective neurons (DSNs) which focus on different part of the visual field separately. The two type of neurons found in the visual pathways of insects respond most strongly to moving objects with preferred motion patterns, i.e., the LGMD prefers looming stimuli and DSNs prefer specific lateral movements. We fuse the extracted information by each type of neurons to make final decision. By dividing the whole field of view into four regions for each subsystem to process, the proposed approaches can detect hazardous situations that had been difficult for single subsystem only. Our experiments show that the integrated system works in most of the hazardous scenarios

Proceedings of the NASA Conference on Space Telerobotics, volume 1

The theme of the Conference was man-machine collaboration in space. Topics addressed include: redundant manipulators; man-machine systems; telerobot architecture; remote sensing and planning; navigation; neural networks; fundamental AI research; and reasoning under uncertainty