Neural Control of Actions Involving Different Coordinate Systems

di Parm

A dynamic neural field approach to the covert and overt deployment of spatial attention

International audienceAbstract The visual exploration of a scene involves the in- terplay of several competing processes (for example to se- lect the next saccade or to keep fixation) and the integration of bottom-up (e.g. contrast) and top-down information (the target of a visual search task). Identifying the neural mech- anisms involved in these processes and in the integration of these information remains a challenging question. Visual attention refers to all these processes, both when the eyes remain fixed (covert attention) and when they are moving (overt attention). Popular computational models of visual attention consider that the visual information remains fixed when attention is deployed while the primates are executing around three saccadic eye movements per second, changing abruptly this information. We present in this paper a model relying on neural fields, a paradigm for distributed, asyn- chronous and numerical computations and show that covert and overt attention can emerge from such a substratum. We identify and propose a possible interaction of four elemen- tary mechanisms for selecting the next locus of attention, memorizing the previously attended locations, anticipating the consequences of eye movements and integrating bottom- up and top-down information in order to perform a visual search task with saccadic eye movements

Engineering derivatives from biological systems for advanced aerospace applications

The present study consisted of a literature survey, a survey of researchers, and a workshop on bionics. These tasks produced an extensive annotated bibliography of bionics research (282 citations), a directory of bionics researchers, and a workshop report on specific bionics research topics applicable to space technology. These deliverables are included as Appendix A, Appendix B, and Section 5.0, respectively. To provide organization to this highly interdisciplinary field and to serve as a guide for interested researchers, we have also prepared a taxonomy or classification of the various subelements of natural engineering systems. Finally, we have synthesized the results of the various components of this study into a discussion of the most promising opportunities for accelerated research, seeking solutions which apply engineering principles from natural systems to advanced aerospace problems. A discussion of opportunities within the areas of materials, structures, sensors, information processing, robotics, autonomous systems, life support systems, and aeronautics is given. Following the conclusions are six discipline summaries that highlight the potential benefits of research in these areas for NASA's space technology programs

Mediated Cognition: Information Technologies and the Sciences of Mind

This dissertation investigates the interconnections between minds, media, and the cognitive sciences. It asks what it means for media to have effects upon the mind: do our tools influence the ways that we think? It considers what scientific evidence can be brought to bear on the question: how can we know and measure these effects? Ultimately, it looks to the looping pathways by which science employs technological media in understanding the mind, and the public comes to understand and respond to these scientific discourses. I contend that like human cognition itself, the enterprise of cognitive science is a deeply and distinctively mediated phenomenon. This casts a different light on contemporary debates about whether television, computers, or the Internet are changing our brains, for better or for worse. Rather than imagining media effects as befalling a fictive natural mind, I draw on multiple disciplines to situate mind and the sciences thereof as shaped from their origins through interaction with technology. Our task is then to interrogate the forms of cognition and attention fostered by different media, alongside their attendant costs and benefits. The first chapter positions this dissertation between the fields of media studies and STS, developing a case for the reality of media effects without the implication of technological determinism. The second considers the history of technological metaphor in scientific characterizations of the mind. The third section consists of three separate chapters on the history of cognitive science, presenting the core of my case for its uniquely mediated character. Across three distinct eras, what unifies cognitive science is the quest to understand the mind using computational systems, operating by turns as generative metaphors and tangible models. I then evaluate the contemporary cognitive-scientific research on the question of media effects, and the growing role of electronic media in science. My fifth and final section develops a content analysis: what is said in the media about the popular theory that media themselves, in one way or another, are causing attention deficit disorders? The work concludes with a summary and some reflections on mind, culture, technoscience and markets as recursively interwoven causal systems

Promoting Cognitive and Fine Motor Development in the Art Room for Young Students with Exposure to Trauma: an Exploratory Case Study.

Despite the overwhelming research that suggests visual artmaking experiences can produce positive outcomes for individuals, many state art education policies fail to implement and integrate the arts into core academic curriculums. Specifically in the state of West Virginia, kindergarten through twelfth grade students often lack the access to a quality and consistent art education. With high teacher-pupil ratios and inclusive learning environments, educators face the difficult task of meeting all the individual needs of their students’ in the classroom. This not only affects whole student populations, but also poses risks for children who have been exposed to trauma. Traumatic experiences in early childhood can cause a range of severe negative outcomes. West Virginia has seen a rise in child victims and the occurrence of traumatic childhood e since 2015, making it more apparent than ever that children need resources to help them heal and mitigate the impact trauma can have on their lives. School communities are increasingly implementing trauma-informed programs that provide students with the proper interventions they need. Although these programs already have invaluable benefits, there seems to be a gap in research between the interventions of currently established programs and the possibilities artmaking experiences can provide for trauma exposed students. This exploratory case study investigates how to design art lessons that promote the development of students in the kindergarten through second grade classroom who have been identified with exposure to trauma. This research particularly focuses on understanding how art lessons can promote cognitive and fine motor development for these students in the art room. Finally, this research examines the role an art education can play in supporting these students within trauma-informed school communities

Consciousness in Artificial Intelligence: Insights from the Science of Consciousness

Whether current or near-term AI systems could be conscious is a topic of scientific interest and increasing public concern. This report argues for, and exemplifies, a rigorous and empirically grounded approach to AI consciousness: assessing existing AI systems in detail, in light of our best-supported neuroscientific theories of consciousness. We survey several prominent scientific theories of consciousness, including recurrent processing theory, global workspace theory, higher-order theories, predictive processing, and attention schema theory. From these theories we derive "indicator properties" of consciousness, elucidated in computational terms that allow us to assess AI systems for these properties. We use these indicator properties to assess several recent AI systems, and we discuss how future systems might implement them. Our analysis suggests that no current AI systems are conscious, but also suggests that there are no obvious technical barriers to building AI systems which satisfy these indicators

A Model of the Network Architecture of the Brain that Supports Natural Language Processing

For centuries, neuroscience has proposed models of the neurobiology of language processing that are static and localised to few temporal and inferior frontal regions. Although existing models have offered some insight into the processes underlying lower-level language features, they have largely overlooked how language operates in the real world. Here, we aimed at investigating the network organisation of the brain and how it supports language processing in a naturalistic setting. We hypothesised that the brain is organised in a multiple core-periphery and dynamic modular architecture, with canonical language regions forming high-connectivity hubs. Moreover, we predicted that language processing would be distributed to much of the rest of the brain, allowing it to perform more complex tasks and to share information with other cognitive domains. To test these hypotheses, we collected the Naturalistic Neuroimaging Database of people watching full length movies during functional magnetic resonance imaging. We computed network algorithms to capture the voxel-wise architecture of the brain in individual participants and inspected variations in activity distribution over different stimuli and over more complex language features. Our results confirmed the hypothesis that the brain is organised in a flexible multiple core-periphery architecture with large dynamic communities. Here, language processing was distributed to much of the rest of the brain, together forming multiple communities. Canonical language regions constituted hubs, explaining why they consistently appear in various other neurobiology of language models. Moreover, language processing was supported by other regions such as visual cortex and episodic memory regions, when processing more complex context-specific language features. Overall, our flexible and distributed model of language comprehension and the brain points to additional brain regions and pathways that could be exploited for novel and more individualised therapies for patients suffering from speech impairments

High Efficiency Real-Time Sensor and Actuator Control and Data Processing

The advances in sensor and actuator technology foster the use of large multitransducer networks in many different fields. The increasing complexity of such networks poses problems in data processing, especially when high-efficiency is required for real-time applications. In fact, multi-transducer data processing usually consists of interconnection and co-operation of several modules devoted to process different tasks. Multi-transducer network modules often include tasks such as control, data acquisition, data filtering interfaces, feature selection and pattern analysis. Heterogeneous techniques derived from chemometrics, neural networks, fuzzy-rules used to implement such tasks may introduce module interconnection and co-operation issues. To help dealing with these problems the author here presents a software library architecture for a dynamic and efficient management of multi-transducer data processing and control techniques. The framework’s base architecture and the implementation details of several extensions are described. Starting from the base models available in the framework core dedicated models for control processes and neural network tools have been derived. The Facial Automaton for Conveying Emotion (FACE) has been used as a test field for the control architecture