Increasing Accessibility to the Blind of Virtual Environments,Using a Virtual Mobility Aid Based On the "EyeCane": Feasibility Study

Virtual worlds and environments are becoming an increasingly central part of our lives, yet they are still far from accessible to the blind. This is especially unfortunate as such environments hold great potential for them for uses such as social interaction, online education and especially for use with familiarizing the visually impaired user with a real environment virtually from the comfort and safety of his own home before visiting it in the real world. We have implemented a simple algorithm to improve this situation using single-point depth information, enabling the blind to use a virtual cane, modeled on the “EyeCane” electronic travel aid, within any virtual environment with minimal pre-processing. Use of the Virtual-EyeCane, enables this experience to potentially be later used in real world environments with identical stimuli to those from the virtual environment. We show the fast-learned practical use of this algorithm for navigation in simple environments.     

Autonomous Navigation for Unmanned Aerial Vehicles Based on Chaotic Bionics Theory

In this paper a new reactive mechanism based on perception-action bionics for multi-sensory integration applied to Un-manned Aerial Vehicles (UAVs) navigation is proposed.The strategy is inspired by the olfactory bulb neural activity observed inrabbits subject to external stimuli.The new UAV navigation technique exploits the use of a multiscroll chaotic system which isable to be controlled in real-time towards less complex orbits,like periodic orbits or equilibrium points,considered as perceptiveorbits.These are subject to real-time modifications on the basis of environment changes acquired through a Synthetic ApertureRadar (SAR) sensory system.The mathematical details of the approach are given including simulation results in a virtual en-vironment.The results demonstrate the capability of autonomous navigation for UAV based on chaotic bionics theory in com-plex spatial environments.     

Sleep modulates the neural substrates of both spatial and contextual memory consolidation

It is known that sleep reshapes the neural representations that subtend the memories acquired while navigating in a virtual environment. However, navigation is not process-pure, as manifold learning components contribute to performance, notably the spatial and contextual memory constituents. In this context, it remains unclear whether post-training sleep globally promotes consolidation of all of the memory components embedded in virtual navigation, or rather favors the development of specific representations. Here, we investigated the effect of post-training sleep on the neural substrates of the consolidation of spatial and contextual memories acquired while navigating in a complex 3D, naturalistic virtual town. Using fMRI, we mapped regional cerebral activity during various tasks designed to tap either the spatial or the contextual memory component, or both, 72 h after encoding with or without sleep deprivation during the first post-training night. Behavioral performance was not dependent upon post-training sleep deprivation, neither in a natural setting that engages both spatial and contextual memory processes nor when looking more specifically at each of these memory representations. At the neuronal level however, analyses that focused on contextual memory revealed distinct correlations between performance and neuronal activity in frontal areas associated with recollection processes after post-training sleep, and in the parahippocampal gyrus associated with familiarity processes in sleep-deprived participants. Likewise, efficient spatial memory was associated with posterior cortical activity after sleep whereas it correlated with parahippocampal/medial temporal activity after sleep deprivation. Finally, variations in place-finding efficiency in a natural setting encompassing spatial and contextual elements were associated with caudate activity after post-training sleep, suggesting the automation of navigation. These data indicate that post-training sleep modulates the neural substrates of the consolidation of both the spatial and contextual memories acquired during virtual navigation.     

Easy Rider: Monkeys Learn to Drive a Wheelchair to Navigate through a Complex Maze

The neurological bases of spatial navigation are mainly investigated in rodents and seldom in primates. The few studies led on spatial navigation in both human and non-human primates are performed in virtual, not in real environments. This is mostly because of methodological difficulties inherent in conducting research on freely-moving monkeys in real world environments. There is some incertitude, however, regarding the extrapolation of rodent spatial navigation strategies to primates. Here we present an entirely new platform for investigating real spatial navigation in rhesus monkeys. We showed that monkeys can learn a pathway by using different strategies. In these experiments three monkeys learned to drive the wheelchair and to follow a specified route through a real maze. After learning the route, probe tests revealed that animals successively use three distinct navigation strategies based on i) the place of the reward, ii) the direction taken to obtain reward or iii) a cue indicating reward location. The strategy used depended of the options proposed and the duration of learning. This study reveals that monkeys, like rodents and humans, switch between different spatial navigation strategies with extended practice, implying well-conserved brain learning systems across different species. This new task with freely driving monkeys provides a good support for the electrophysiological and pharmacological investigation of spatial navigation in the real world by making possible electrophysiological and pharmacological investigations.     

Perception, representation and recognition: a holistic view of recognition

It is clear that humans have mental representations of their spatial environments and that these representations are useful, if not essential, in a wide variety of cognitive tasks such as identification of landmarks and objects, guiding actions and navigation and in directing spatial awareness and attention. Determining the properties of mental representation has long been a contentious issue (see Pinker, 1984). One method of probing the nature of human representation is by studying the extent to which representation can surpass or go beyond the visual (or sensory) experience from which it derives. From a strictly empiricist standpoint what is not sensed cannot be represented; except as a combination of things that have been experienced. But perceptual experience is always limited by our view of the world and the properties of our visual system. It is therefore not surprising when human representation is found to be highly dependent on the initial viewpoint of the observer and on any shortcomings thereof. However, representation is not a static entity; it evolves with experience. The debate as to whether human representation of objects is view-dependent or view-invariant that has dominated research journals recently may simply be a discussion concerning how much information is available in the retinal image during experimental tests and whether this information is sufficient for the task at hand. Here we review an approach to the study of the development of human spatial representation under realistic problem solving scenarios. This is facilitated by the use of realistic virtual environments, exploratory learning and redundancy in visual detail.     

Aging and Sensory Substitution in a Virtual Navigation Task

Virtual environments are becoming ubiquitous, and used in a variety of contexts–from entertainment to training and rehabilitation. Recently, technology for making them more accessible to blind or visually impaired users has been developed, by using sound to represent visual information. The ability of older individuals to interpret these cues has not yet been studied. In this experiment, we studied the effects of age and sensory modality (visual or auditory) on navigation through a virtual maze. We added a layer of complexity by conducting the experiment in a rotating room, in order to test the effect of the spatial bias induced by the rotation on performance. Results from 29 participants showed that with the auditory cues, it took participants a longer time to complete the mazes, they took a longer path length through the maze, they paused more, and had more collisions with the walls, compared to navigation with the visual cues. The older group took a longer time to complete the mazes, they paused more, and had more collisions with the walls, compared to the younger group. There was no effect of room rotation on the performance, nor were there any significant interactions among age, feedback modality and room rotation. We conclude that there is a decline in performance with age, and that while navigation with auditory cues is possible even at an old age, it presents more challenges than visual navigation.     

Environmental enrichment promotes neural plasticity and cognitive ability in fish

Different kinds of experience during early life can play a significant role in the development of an animal''s behavioural phenotype. In natural contexts, this influences behaviours from anti-predator responses to navigation abilities. By contrast, for animals reared in captive environments, the homogeneous nature of their experience tends to reduce behavioural flexibility. Studies with cage-reared rodents indicate that captivity often compromises neural development and neural plasticity. Such neural and behavioural deficits can be problematic if captive-bred animals are being reared with the intention of releasing them as part of a conservation strategy. Over the last decade, there has been growing interest in the use of environmental enrichment to promote behavioural flexibility in animals that are bred for release. Here, we describe the positive effects of environmental enrichment on neural plasticity and cognition in juvenile Atlantic salmon (Salmo salar). Exposing fish to enriched conditions upregulated the forebrain expression of NeuroD1 mRNA and improved learning ability assessed in a spatial task. The addition of enrichment to the captive environment thus promotes neural and behavioural changes that are likely to promote behavioural flexibility and improve post-release survival.     

Multifunctional Setup for Studying Human Motor Control Using Transcranial Magnetic Stimulation,Electromyography, Motion Capture,and Virtual Reality

The study of neuromuscular control of movement in humans is accomplished with numerous technologies. Non-invasive methods for investigating neuromuscular function include transcranial magnetic stimulation, electromyography, and three-dimensional motion capture. The advent of readily available and cost-effective virtual reality solutions has expanded the capabilities of researchers in recreating “real-world” environments and movements in a laboratory setting. Naturalistic movement analysis will not only garner a greater understanding of motor control in healthy individuals, but also permit the design of experiments and rehabilitation strategies that target specific motor impairments (e.g. stroke). The combined use of these tools will lead to increasingly deeper understanding of neural mechanisms of motor control. A key requirement when combining these data acquisition systems is fine temporal correspondence between the various data streams. This protocol describes a multifunctional system’s overall connectivity, intersystem signaling, and the temporal synchronization of recorded data. Synchronization of the component systems is primarily accomplished through the use of a customizable circuit, readily made with off the shelf components and minimal electronics assembly skills.     

Virtual World Currency Value Fluctuation Prediction System Based on User Sentiment Analysis

In this paper, we present a method for predicting the value of virtual currencies used in virtual gaming environments that support multiple users, such as massively multiplayer online role-playing games (MMORPGs). Predicting virtual currency values in a virtual gaming environment has rarely been explored; it is difficult to apply real-world methods for predicting fluctuating currency values or shares to the virtual gaming world on account of differences in domains between the two worlds. To address this issue, we herein predict virtual currency value fluctuations by collecting user opinion data from a virtual community and analyzing user sentiments or emotions from the opinion data. The proposed method is straightforward and applicable to predicting virtual currencies as well as to gaming environments, including MMORPGs. We test the proposed method using large-scale MMORPGs and demonstrate that virtual currencies can be effectively and efficiently predicted with it.     

Resolution of uncertainty in prefrontal cortex

Making optimal decisions in the face of uncertain or incomplete information arises as a common problem in everyday behavior, but the neural processes underlying this ability remain poorly understood. A typical case is navigation, in which a subject has to search for a known goal from an unknown location. Navigating under uncertain conditions requires making decisions on the basis of the current belief about location and updating that belief based on incoming information. Here, we use functional magnetic resonance imaging during a maze navigation task to study neural activity relating to the resolution of uncertainty as subjects make sequential decisions to reach a goal. We show that distinct regions of prefrontal cortex are engaged in specific computational functions that are well described by a Bayesian model of decision making. This permits efficient goal-oriented navigation and provides new insights into decision making by humans.     

Prominent Inhibitory Projections Guide Sensorimotor Computation: An Invertebrate Perspective

From single‐cell organisms to complex neural networks, all evolved to provide control solutions to generate context‐ and goal‐specific actions. Neural circuits performing sensorimotor computation to drive navigation employ inhibitory control as a gating mechanism as they hierarchically transform (multi)sensory information into motor actions. Here, the focus is on this literature to critically discuss the proposition that prominent inhibitory projections form sensorimotor circuits. After reviewing the neural circuits of navigation across various invertebrate species, it is argued that with increased neural circuit complexity and the emergence of parallel computations, inhibitory circuits acquire new functions. The contribution of inhibitory neurotransmission for navigation goes beyond shaping the communication that drives motor neurons, and instead includes encoding of emergent sensorimotor representations. A mechanistic understanding of the neural circuits performing sensorimotor computations in invertebrates will unravel the minimum circuit requirements driving adaptive navigation.     

Neural network committees for finger joint angle estimation from surface EMG signals

Background  

In virtual reality (VR) systems, the user's finger and hand positions are sensed and used to control the virtual environments. Direct biocontrol of VR environments using surface electromyography (SEMG) signals may be more synergistic and unconstraining to the user. The purpose of the present investigation was to develop a technique to predict the finger joint angle from the surface EMG measurements of the extensor muscle using neural network models.     

Principles of goal-directed spatial robot navigation in biomimetic models

Mobile robots and animals alike must effectively navigate their environments in order to achieve their goals. For animals goal-directed navigation facilitates finding food, seeking shelter or migration; similarly robots perform goal-directed navigation to find a charging station, get out of the rain or guide a person to a destination. This similarity in tasks extends to the environment as well; increasingly, mobile robots are operating in the same underwater, ground and aerial environments that animals do. Yet despite these similarities, goal-directed navigation research in robotics and biology has proceeded largely in parallel, linked only by a small amount of interdisciplinary research spanning both areas. Most state-of-the-art robotic navigation systems employ a range of sensors, world representations and navigation algorithms that seem far removed from what we know of how animals navigate; their navigation systems are shaped by key principles of navigation in ‘real-world’ environments including dealing with uncertainty in sensing, landmark observation and world modelling. By contrast, biomimetic animal navigation models produce plausible animal navigation behaviour in a range of laboratory experimental navigation paradigms, typically without addressing many of these robotic navigation principles. In this paper, we attempt to link robotics and biology by reviewing the current state of the art in conventional and biomimetic goal-directed navigation models, focusing on the key principles of goal-oriented robotic navigation and the extent to which these principles have been adapted by biomimetic navigation models and why.     

A neural systems analysis of adaptive navigation

In the field of the neurobiology of learning, significant emphasis has been placed on understanding neural plasticity within a single structure (or synapse type) as it relates to a particular type of learning mediated by a particular brain area. To appreciate fully the breadth of the plasticity responsible for complex learning phenomena, it is imperative that we also examine the neural mechanisms of the behavioral instantiation of learned information, how motivational systems interact, and how past memories affect the learning process. To address this issue, we describe a model of complex learning (rodent adaptive navigation) that could be used to study dynamically interactive neural systems. Adaptive navigation depends on the efficient integration of external and internal sensory information with motivational systems to arrive at the most effective cognitive and/or behavioral strategies. We present evidence consistent with the view that during navigation: 1) the limbic thalamus and limbic cortex is primarily responsible for the integration of current and expected sensory information, 2) the hippocampal-septal-hypothalamic system provides a mechanism whereby motivational perspectives bias sensory processing, and 3) the amygdala-prefrontal-striatal circuit allows animals to evaluate the expected reinforcement consequences of context-dependent behavioral responses. Although much remains to be determined regarding the nature of the interactions among neural systems, new insights have emerged regarding the mechanisms that underlie flexible and adaptive behavioral responses.     

Environment Recognition System Based on Multiple Classification Analyses for Mobile Robots

Various mechanisms have recently been developed that combine linkage mechanisms and wheels. In particular, the combination of passive linkage mechanisms and small wheels is a main research trend because standard wheeled mobile mechanisms find it difficult to move on rough terrain. In our previous research, a six-wheel mobile robot employing a passive linkage mechanism has been developed to enhance maneuverability and was able to climb over a 0.20 m bump and stairs. We designed a hybrid velocity and torque controller using a neural network since simple velocity controllers fail to climb up. In this paper, we propose an environment recognition system for a wheeled mobile robot that consists of multiple classification analyses to make the robot more adaptive to various environments by selecting a suitable system such as decision making, navigation and controller using the result of the environment recognition system. We evaluate the recognition performance in operation environments; slopes, bumps and stairs by comparing principle component, k-means and self-organizing map analyses.     

Dohko: an autonomic system for provision,configuration, and management of inter-cloud environments based on a software product line engineering method

Configuring and executing applications across multiple clouds is a challenging task due to the various terminologies used by the cloud providers. Therefore, we advocate the use of autonomic systems to do this work automatically. Thus, in this paper, we propose and evaluate Dohko, an autonomic and goal-oriented system for inter-cloud environments. Dohko implements self-configuration, self-healing, and context-awareness properties. Likewise, it relies on a hierarchical P2P overlay (a) to manage the  virtual machines running on the clouds and (b) to deal with inter-cloud communication. Furthermore, it depends on a software product line engineering method to enable applications’ deployment and reconfiguration, without requiring pre-configured virtual machine images. Experimental results show that Dohko can free the users from the duty of executing non-native cloud application on single and over many clouds. In particular, it tackles the lack of middleware prototypes that can support different scenarios when using simultaneous services from multiple clouds.     

Flexible navigational computations in the Drosophila central complex

Insects can perform impressive feats of navigation, suggesting a sophisticated sense of direction and an ability to choose appropriate trajectories toward ethological goals. The hypothesized substrate for these navigational abilities is the central complex (CX), a midline brain structure with orderly topology. The circuit transformations performed by the CX are now being concretely described by recent advances in the study of fruit fly neural circuits. An emerging theme is dynamic representation of navigational variables (e.g. heading or travel direction) computed in a manner distributed across specific neuronal populations. These representations are shaped by multimodal inputs whose weights evolve rapidly as surroundings change. Investigation of CX circuits is revealing with precise detail how structured wiring and synaptic plasticity enable neural circuits to flexibly subsample from the currently available sensory and motor cues to build a stable and accurate map of space. Given the sensory richness of natural environments, these findings are encouraging insect neuroscientists to no longer ask which cues insects use to navigate, but instead which cues can insects use, and under which contexts.     

Ageing effects on path integration and landmark navigation

Navigation abilities show marked decline in both normal ageing and dementia. Path integration may be particularly affected, as it is supported by the hippocampus and entorhinal cortex, both of which show severe degeneration with ageing. Age differences in path integration based on kinaesthetic and vestibular cues have been clearly demonstrated, but very little research has focused on visual path integration, based only on optic flow. Path integration is complemented by landmark navigation, which may also show age differences, but has not been well studied either. Here we present a study using several simple virtual navigation tasks to explore age differences in path integration both with and without landmark information. We report that, within a virtual environment that provided only optic flow information, older participants exhibited deficits in path integration in terms of distance reproduction, rotation reproduction, and triangle completion. We also report age differences in triangle completion within an environment that provided landmark information. In all tasks, we observed a more restricted range of responses in the older participants, which we discuss in terms of a leaky integrator model, as older participants showed greater leak than younger participants. Our findings begin to explain the mechanisms underlying age differences in path integration, and thus contribute to an understanding of the substantial decline in navigation abilities observed in ageing.