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This eBook is a collection of articles from a Frontiers Research Topic. Frontiers Research Topics are very popular trademarks of the Frontiers Journals Series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author by contacting the Frontiers Editorial Office: frontiersin.org/about/contact

collective behavior --- self-organization --- emergence --- collective dynamics --- tools and methods

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Biophysical modelling of brain activity has had a long and illustrious history and has during the last few years profited from technological advances that allow obtaining neuroimaging data at an unprecedented spatiotemporal resolution. It is a very active area of research with applications ranging from the characterization of neurobiological and cognitive processes to constructing artificial brains in silico and building brain-machine interface and neuroprosthetic devices. The relevant community has always benefited from interdisciplinary interactions between different and seemingly distant fields ranging from mathematics and engineering to linguistics and psychology. This Research Topic aims to promote such interactions and we welcome all works related or that can contribute to an understanding of and construction of models for neural activity. The focus will be on biophysical models describing brain activity usually measured by fMRI or electrophysiology. Such models can be divided into two large classes: neural mass and neural field models. The main difference between these two classes is that field models prescribe how a quantity characterizing neural activity (such as average depolarization of a neural population) evolves over both space and time as opposed to mass models which characterize the evolution of this quantity over time only and assume that all neurons of a population are located at (approximately) the same point. This Research Topic will focus on both classes of such models and discuss several of their aspects and relative merits focusing on the main ideas of neural field and mass theories that span from synapses to the whole brain, comparisons of their predictions with EEG and MEG spectra of spontaneous brain activity, evoked responses, seizures, and fitting to data to infer brain states and map physiological parameters. We welcome submissions shedding light on the underlying dynamics within the neural tissue that can yield explanations of disorders such as epilepsy and migraine as well as normal functions such as attention, working memory and decision making and encourage papers reporting new theoretical and/or modelling work as well as advances in experimental methods that can benefit modelling endeavours. The aim of this Research Topic is to provide a forum for state-of-the-art research in the field and foster new theoretical advances.

Calculus --- Mathematics --- Physical Sciences & Mathematics --- neural disorders --- self-organization --- Electroencephalogram --- neural networks --- Electrophysiology --- Integro-differential equations --- neural field theory --- neural masses --- oscillations --- anaesthesia

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Dieser Band beschäftigt sich mit dem maschinellen Lernen - der Autoadaption von algorithmischen Artefakten - als Thema interdisziplinärer Diskurse zu beispielsweise Selbstorganisation oder schwacher künstlicher Intelligenz. Anknüpfend an Heidegger, Goodman und Hubig ermöglicht die Studie einen systematischen interdisziplinären Zugang zu maschinellem Lernen, indem seine Charakteristika - etwa künstliche neuronale Netze oder evolutionäres Lernen - präzise, aber dennoch interdisziplinär verständlich beschrieben werden. Darauf aufbauend nimmt Sebastian Harrach eine technikphilosophische Verortung des maschinellen Lernens vor und geht exemplarisch den Denkrichtungen einer interdisziplinären Diskussion nach.

Technology --- Machine learning. --- Machinery --- Technology and civilization --- Machines --- Manufactures --- Power (Mechanics) --- Mechanical engineering --- Motors --- Power transmission --- Learning, Machine --- Artificial intelligence --- Machine theory --- Philosophy. --- Curious devices --- Maschinelles Lernen; Mensch-Computer-Interaktion; Big Data; Selbstorganisation; Technikphilosophie; Philosophie; Technik; Medienphilosophie; Informatik; Machine Learning; Human-computer Interaction; Self-organization; Philosophy of Technology; Philosophy; Technology; Media Philosophy; Computer Sciences --- Big Data. --- Computer Sciences. --- Human-computer Interaction. --- Media Philosophy. --- Philosophy of Technology. --- Self-organization. --- Technology.

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This book, by a leading expert in urban agriculture, offers a genuine solution to today’s global food crisis. By contributing more to feeding themselves, cities can allow breathing space for the rural sector to convert to more organic sustainable approaches. Biel’s approach connects with current debates about agroecology and food sovereignty, asks key questions, and proposes lines of future research. He suggests that today’s food insecurity – manifested in a regime of wildly fluctuating prices – reflects not just temporary stresses in the existing mode of production, but more profoundly the troubled process of generating a new one. He argues that the solution cannot be implemented at a merely technical or political level: the force of change can only be driven by the kind of social movements which are now daring to challenge the existing unsustainable order. Drawing on both his academic research and teaching, and 15 years’ experience as a practising urban farmer, Biel brings a unique interdisciplinary approach to this key global issue, creating a dialogue between the physical and social sciences

Food supply. --- Food control --- Produce trade --- Agriculture --- Food security --- Single cell proteins --- Development studies --- Urban communities --- Sociology & anthropology --- Sociology --- Urban & municipal planning --- Sustainable agriculture --- Food security. --- Urban agriculture. --- Sustainable agriculture. --- Low-input agriculture --- Low-input sustainable agriculture --- Lower input agriculture --- Resource-efficient agriculture --- Sustainable farming --- Alternative agriculture --- Urban farming --- Land use, Urban --- Food deserts --- Food insecurity --- Insecurity, Food --- Security, Food --- Human security --- Food supply --- urban agriculture --- co-operatives --- sustainability --- food security --- Capitalism --- Paradigm --- Self-organization

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This book is published open access under a CC BY 4.0 license. It covers aspects of unsupervised machine learning used for knowledge discovery in data science and introduces a data-driven approach to cluster analysis, the Databionic swarm(DBS). DBS consists of the 3D landscape visualization and clustering of data. The 3D landscape enables 3D printing of high-dimensional data structures.The clustering and number of clusters or an absence of cluster structure are verified by the 3D landscape at a glance. DBS is the first swarm-based technique that shows emergent properties while exploiting concepts of swarm intelligence, self-organization and the Nash equilibrium concept from game theory. It results in the elimination of a global objective function and the setting of parameters. By downloading the R package DBS can be applied to data drawn from diverse research fields and used even by non-professionals in the field of data mining. Contents Approaches to Unsupervised Machine Learning Methods of Visualization of High-Dimensional Data Quality Assessments of Visualizations Behavior-Based Systems in Data Science Databionic Swarm (DBS) Target Groups Lecturers, students as well as non-professional users of data science, statistics, computer science, business mathematics, medicine, biology The Author Michael C. Thrun, Dipl.-Phys., successfully defended his Ph.D. in 2017 at the Philipps University of Marburg. Thrun’s advisor was the Chair of Neuroinformatics, Prof. Dr. rer. nat. Alfred G. H. Ultsch.

Computer science. --- Data structures (Computer science). --- Pattern recognition. --- Computer Science. --- Pattern Recognition. --- Data Structures. --- Design perception --- Pattern recognition --- Form perception --- Perception --- Figure-ground perception --- Information structures (Computer science) --- Structures, Data (Computer science) --- Structures, Information (Computer science) --- Electronic data processing --- File organization (Computer science) --- Abstract data types (Computer science) --- Informatics --- Science --- Optical pattern recognition. --- Data structures (Computer scienc. --- Optical data processing --- Pattern perception --- Perceptrons --- Visual discrimination --- Data structures (Computer science) --- Cluster Analysis --- Dimensionality Reduction --- Swarm Intelligence --- Visualization --- Unsupervised Machine Learning --- Data Science --- Knowledge Discovery --- 3D Printing --- Self-Organization --- Emergence --- Game Theory --- Advanced Analytics --- High-Dimensional Data --- Multivariate Data --- Analysis of Structured Data