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What neurobiology and artificial intelligence tell us about how the brain builds itself How does a neural network become a brain? While neurobiologists investigate how nature accomplishes this feat, computer scientists interested in artificial intelligence strive to achieve this through technology. The Self-Assembling Brain tells the stories of both fields, exploring the historical and modern approaches taken by the scientists pursuing answers to the quandary: What information is necessary to make an intelligent neural network?As Peter Robin Hiesinger argues, “the information problem” underlies both fields, motivating the questions driving forward the frontiers of research. How does genetic information unfold during the years-long process of human brain development—and is there a quicker path to creating human-level artificial intelligence? Is the biological brain just messy hardware, which scientists can improve upon by running learning algorithms on computers? Can AI bypass the evolutionary programming of “grown” networks? Through a series of fictional discussions between researchers across disciplines, complemented by in-depth seminars, Hiesinger explores these tightly linked questions, highlighting the challenges facing scientists, their different disciplinary perspectives and approaches, as well as the common ground shared by those interested in the development of biological brains and AI systems. In the end, Hiesinger contends that the information content of biological and artificial neural networks must unfold in an algorithmic process requiring time and energy. There is no genome and no blueprint that depicts the final product. The self-assembling brain knows no shortcuts.Written for readers interested in advances in neuroscience and artificial intelligence, The Self-Assembling Brain looks at how neural networks grow smarter.

Neural networks (Computer science) --- Learning --- Physiological aspects. --- Gary Macus. --- How to Create a Mind. --- Peter Sterling. --- Principles of Neural Design. --- Ray Kurzweil. --- Roger Sperry. --- Seymour Benzer. --- Simon Laughlin. --- Sydney Brenner. --- The Birth of the Mind. --- algorithm. --- algorithmic growth. --- artificial life. --- artificial neural network. --- axon guidance. --- behavior. --- brain development. --- brain wiring. --- cellular automaton. --- cognitive bias. --- complexity. --- computer intelligence. --- connectome. --- cybernetics. --- deep learning. --- evolution. --- filopodia. --- gene. --- guidance cue. --- information theory. --- machine learning. --- memory. --- neural circuit. --- neurogenetics. --- self-organization. --- synapse.

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Due to the increasing demand for security and reliability in manufacturing and mechatronic systems, early detection and diagnosis of faults are key points to reduce economic losses caused by unscheduled maintenance and downtimes, to increase safety, to prevent the endangerment of human beings involved in the process operations and to improve reliability and availability of autonomous systems. The development of algorithms for health monitoring and fault and anomaly detection, capable of the early detection, isolation, or even prediction of technical component malfunctioning, is becoming more and more crucial in this context. This Special Issue is devoted to new research efforts and results concerning recent advances and challenges in the application of “Algorithms for Fault Detection and Diagnosis”, articulated over a wide range of sectors. The aim is to provide a collection of some of the current state-of-the-art algorithms within this context, together with new advanced theoretical solutions.

structural health monitoring --- digital image processing --- damage --- gray level co-occurrence matrix --- self-organization map --- rolling bearings --- fault diagnosis --- multiscale entropy --- amplitude-aware permutation entropy --- random forest --- reusable launch vehicle --- thruster valve failure --- thruster fault detection --- Kalman filter --- machine vision --- machine diagnostics --- instantaneous angular speed --- SURVISHNO 2019 challenge --- video tachometer --- motion tracking --- edge detection --- parametric template modeling --- adaptive template matching --- genetic algorithm --- misalignment --- fault prediction --- combined prediction --- multivariate grey model --- quantum genetic algorithm --- least squares support vector machine --- lithium-ion battery --- battery faults --- battery safety --- battery management system --- fault diagnostic algorithms

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“Symmetry Breaking in Cells and Tissues” presents a collection of seventeen reviews, opinions and original research papers contributed by theoreticians, physicists and mathematicians, as well as experimental biologists, united by a common interest in biological pattern formation and morphogenesis. The contributors discuss diverse manifestations of symmetry breaking in biology and showcase recent developments in experimental and theoretical approaches to biological morphogenesis and pattern formation on multiple scales.

Research & information: general --- Biology, life sciences --- actin waves --- curved proteins --- dynamic instability --- podosomes --- diffusion --- cell polarity --- Cdc42 --- stress --- cellular memory --- phase separation --- prions --- apoptotic extrusion --- oncogenic extrusion --- contractility --- actomyosin --- bottom-up synthetic biology --- motor proteins --- pattern formation --- self-organization --- cell motility --- signal transduction --- actin dynamics --- intracellular waves --- polarization --- direction sensing --- symmetry-breaking --- biphasic responses --- reaction-diffusion --- membrane and cortical tension --- cell fusion --- cortexillin --- cytokinesis --- Dictyostelium --- myosin --- symmetry breaking --- cytoplasmic flow --- phase-space analysis --- nonlinear waves --- actin polymerization --- bifurcation theory --- mass conservation --- spatial localization --- activator–inhibitor models --- developmental transitions --- cell polarization --- mathematical model --- fission yeast --- reaction–diffusion model --- small GTPases --- Cdc42 oscillations --- pseudopod --- Ras activation --- cytoskeleton --- chemotaxis --- neutrophils --- natural variation --- modelling --- activator-substrate mechanism --- mass-conserved models --- intracellular polarization --- partial differential equations --- sensitivity analysis --- GTPase activating protein (GAP) --- fission yeast Schizosaccharomyces pombe --- CRY2-CIBN --- optogenetics --- clustering --- positive feedback --- network evolution --- Saccharomyces cerevisiae --- polarity --- modularity --- neutrality --- n/a

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“Symmetry Breaking in Cells and Tissues” presents a collection of seventeen reviews, opinions and original research papers contributed by theoreticians, physicists and mathematicians, as well as experimental biologists, united by a common interest in biological pattern formation and morphogenesis. The contributors discuss diverse manifestations of symmetry breaking in biology and showcase recent developments in experimental and theoretical approaches to biological morphogenesis and pattern formation on multiple scales.

Research & information: general --- Biology, life sciences --- actin waves --- curved proteins --- dynamic instability --- podosomes --- diffusion --- cell polarity --- Cdc42 --- stress --- cellular memory --- phase separation --- prions --- apoptotic extrusion --- oncogenic extrusion --- contractility --- actomyosin --- bottom-up synthetic biology --- motor proteins --- pattern formation --- self-organization --- cell motility --- signal transduction --- actin dynamics --- intracellular waves --- polarization --- direction sensing --- symmetry-breaking --- biphasic responses --- reaction-diffusion --- membrane and cortical tension --- cell fusion --- cortexillin --- cytokinesis --- Dictyostelium --- myosin --- symmetry breaking --- cytoplasmic flow --- phase-space analysis --- nonlinear waves --- actin polymerization --- bifurcation theory --- mass conservation --- spatial localization --- activator–inhibitor models --- developmental transitions --- cell polarization --- mathematical model --- fission yeast --- reaction–diffusion model --- small GTPases --- Cdc42 oscillations --- pseudopod --- Ras activation --- cytoskeleton --- chemotaxis --- neutrophils --- natural variation --- modelling --- activator-substrate mechanism --- mass-conserved models --- intracellular polarization --- partial differential equations --- sensitivity analysis --- GTPase activating protein (GAP) --- fission yeast Schizosaccharomyces pombe --- CRY2-CIBN --- optogenetics --- clustering --- positive feedback --- network evolution --- Saccharomyces cerevisiae --- polarity --- modularity --- neutrality --- actin waves --- curved proteins --- dynamic instability --- podosomes --- diffusion --- cell polarity --- Cdc42 --- stress --- cellular memory --- phase separation --- prions --- apoptotic extrusion --- oncogenic extrusion --- contractility --- actomyosin --- bottom-up synthetic biology --- motor proteins --- pattern formation --- self-organization --- cell motility --- signal transduction --- actin dynamics --- intracellular waves --- polarization --- direction sensing --- symmetry-breaking --- biphasic responses --- reaction-diffusion --- membrane and cortical tension --- cell fusion --- cortexillin --- cytokinesis --- Dictyostelium --- myosin --- symmetry breaking --- cytoplasmic flow --- phase-space analysis --- nonlinear waves --- actin polymerization --- bifurcation theory --- mass conservation --- spatial localization --- activator–inhibitor models --- developmental transitions --- cell polarization --- mathematical model --- fission yeast --- reaction–diffusion model --- small GTPases --- Cdc42 oscillations --- pseudopod --- Ras activation --- cytoskeleton --- chemotaxis --- neutrophils --- natural variation --- modelling --- activator-substrate mechanism --- mass-conserved models --- intracellular polarization --- partial differential equations --- sensitivity analysis --- GTPase activating protein (GAP) --- fission yeast Schizosaccharomyces pombe --- CRY2-CIBN --- optogenetics --- clustering --- positive feedback --- network evolution --- Saccharomyces cerevisiae --- polarity --- modularity --- neutrality