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What do yin-yang and the Lorenzian butterfly in chaos have in common? The outside perspective. Only by going very far outside — beyond the end of the world — do certain aspects of the world become intelligible. The computer makes it possible today to go after the interface. What does the world look like if you are an internally chaotic part? Is the world just a difference, an interface, a forcing function? Is it possible to identify those features which exist only from the inside? How far does the meta-unmaskability go? Is quantum mechanics a virtual reality? Can the micro-interface be manipulated? Such questions are tackled in this fascinating book.
This book presents thoroughly revised tutorial papers based on lectures given by leading researchers at the 8th International Summer School on Neural Networks in Erice, Italy, in October/November 2003. The eight tutorial papers presented provide competent coverage of the field of cortical dynamics, consolidating recent theoretical and experimental results on the processing, transmission, and imprinting of information in the brain as well as on important functions of the cortical area, such as cortical rhythms, cortical neural plasticity, and their structural basis and functional significance. The book is divided in two topical sections on fundamentals of cortical dynamics and mathematical models of cortical dynamics.
Table of contents: 1. Introduction. 2. Mappings. 3. Flows. 1. Two-variable systems. 4. Flows II. Three-vairable systems. 5. Forced systems. 6. Coupled systems. 7.Experimental methods. 8. The Belousov-Zhabotinskii reaction and other solution-phase reactions. 9. Gas-phase reactions. 10. Heterogeneous catalysis. 11. Electrodissolution reactions. 12. Biochemical systems. Index.
In recent years, scientists have applied the principles of complex systems science to increasingly diverse fields. The results have been nothing short of remarkable: their novel approaches have provided answers to long-standing questions in biology, ecology, physics, engineering, computer science, economics, psychology and sociology. "Unifying Themes in Complex Systems" is a well established series of carefully edited conference proceedings that serve the purpose of documenting and archiving the progress of cross-fertilization in this field. About NECSI: For over 10 years, The New England Complex Systems Institute (NECSI) has been instrumental in the development of complex systems science and its applications. NECSI conducts research, education, knowledge dissemination, and community development around the world for the promotion of the study of complex systems and its application for the betterment of society. NECSI hosts the International Conference on Complex Systems and publishes the NECSI Book Series in conjunction with Springer Publishers.
This book presents an original thesis about the notion of sensory experience and of the mind’s architecture, which is grounded in current trends in cognitive science and philosophy of mind. Presented in the form of a dialogue, the book explores some of the psychological and philosophical consequences that the author derives from his proposal.
This book gives a wide-ranging description of the many facets of complex dynamic networks and systems within an infrastructure provided by integrated control and supervision: envisioning, design, experimental exploration, and implementation. The theoretical contributions and the case studies presented can reach control goals beyond those of stabilization and output regulation or even of adaptive control. Reporting on work of the Control of Complex Systems (COSY) research program, Complex Systems follows from and expands upon an earlier collection: Control of Complex Systems by introducing novel theoretical techniques for hard-to-control networks and systems. The major common feature of all t...
This book examines how national, regional and international patent law can better respond to the interests of a diverse set of non-profit and public interest entities, and be of more benefit to developing countries. The book sets out a "tool-box" of participatory mechanisms which would foster third party participation in the patent process.
The five volume set LNCS 7663, LNCS 7664, LNCS 7665, LNCS 7666 and LNCS 7667 constitutes the proceedings of the 19th International Conference on Neural Information Processing, ICONIP 2012, held in Doha, Qatar, in November 2012. The 423 regular session papers presented were carefully reviewed and selected from numerous submissions. These papers cover all major topics of theoretical research, empirical study and applications of neural information processing research. The 5 volumes represent 5 topical sections containing articles on theoretical analysis, neural modeling, algorithms, applications, as well as simulation and synthesis.
The seven-volume set of LNCS 11301-11307, constitutes the proceedings of the 25th International Conference on Neural Information Processing, ICONIP 2018, held in Siem Reap, Cambodia, in December 2018. The 401 full papers presented were carefully reviewed and selected from 575 submissions. The papers address the emerging topics of theoretical research, empirical studies, and applications of neural information processing techniques across different domains. The first volume, LNCS 11301, is organized in topical sections on deep neural networks, convolutional neural networks, recurrent neural networks, and spiking neural networks.
The two-volume set LNAI 8397 and LNAI 8398 constitutes the refereed proceedings of the 6th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2014, held in Bangkok, Thailand, in April 2014. The 125 revised papers presented were carefully reviewed and selected from 300 submissions. The papers address the following topics: natural language and text processing, intelligent information retrieval, semantic Web, social networks and recommendation systems, intelligent database systems, decision support systems, computer vision techniques, and machine learning and data mining. The papers are organized in topical sections on multiple model approach to machine learning, MMAML 201...