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The field of neuroimaging has reached a watershed and critiques and emerging trends are raising foundational issues of methodology, measurement, and theory. Here, scholars reexamine these issues and explore controversies that have arisen in cognitive science, cognitive neuroscience, computer science, and signal processing.

Brain -- Magnetic resonance imaging. --- Brain mapping. --- Electronic books. -- local. --- Brain mapping --- Brain --- Data Interpretation, Statistical --- Brain Mapping --- Magnetic Resonance Imaging --- Physiology --- Research Design --- Central Nervous System --- Statistics as Topic --- Diagnostic Imaging --- Research --- Decision Support Techniques --- Investigative Techniques --- Tomography --- Biological Science Disciplines --- Methods --- Diagnostic Techniques, Neurological --- Natural Science Disciplines --- Health Care Evaluation Mechanisms --- Diagnostic Techniques and Procedures --- Science --- Analytical, Diagnostic and Therapeutic Techniques and Equipment --- Epidemiologic Methods --- Medical Informatics Applications --- Nervous System --- Medical Informatics --- Diagnosis --- Public Health --- Disciplines and Occupations --- Anatomy --- Quality of Health Care --- Health Care Quality, Access, and Evaluation --- Environment and Public Health --- Information Science --- Health Care --- Neuroscience --- Human Anatomy & Physiology --- Health & Biological Sciences --- Magnetic resonance imaging --- Magnetic resonance imaging. --- Connectome mapping --- Mapping of the brain --- Topographic brain mapping --- Localization of functions --- NEUROSCIENCE/General --- COGNITIVE SCIENCES/General

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Brain --- Cognitive science --- Connectionism --- Neural circuitry --- Neural computers --- Sciences cognitives --- Ordinateurs neuronaux --- Mathematical models --- Congresses --- Congrès --- Hydrocephalus --- Brain - Mathematical models - Congresses. --- Connectionism - Congresses. --- Cognitive science - Congresses. --- Neural circuitry - Congresses. --- Neural computers - Congresses.

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This volume includes some of the key research papers in the area of machine learning produced at MIT and Siemens during a three-year joint research effort. It includes papers on many different styles of machine learning, organized into three parts. Part I, theory, includes three papers on theoretical aspects of machine learning. The first two use the theory of computational complexity to derive some fundamental limits on what isefficiently learnable. The third provides an efficient algorithm for identifying finite automata. Part II, artificial intelligence and symbolic learning methods, includes five papers giving an overview of the state of the art and future developments in the field of machine learning, a subfield of artificial intelligence dealing with automated knowledge acquisition and knowledge revision. Part III, neural and collective computation, includes five papers sampling the theoretical diversity and trends in the vigorous new research field of neural networks: massively parallel symbolic induction, task decomposition through competition, phoneme discrimination, behavior-based learning, and self-repairing neural networks.

Artificial intelligence. Robotics. Simulation. Graphics --- Machine learning --- Congresses --- Artificial intelligence --- Neural networks (Computer science) --- Machine learning - Congresses. --- Artificial intelligence - Congresses. --- Neural networks (Computer science) - Congresses. --- Artificial intelligence. --- Computer science. --- Artificial Intelligence. --- Computation by Abstract Devices. --- Processor Architectures. --- Informatics --- Science --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers