Read Books Online and Download eBooks, EPub, PDF, Mobi, Kindle, Text Full Free.
Neural Network Models For Spatial Data Mining Map Production And Cortical Direction Selectivity
Download Neural Network Models For Spatial Data Mining Map Production And Cortical Direction Selectivity full books in PDF, epub, and Kindle. Read online Neural Network Models For Spatial Data Mining Map Production And Cortical Direction Selectivity ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!
Book Synopsis Neural Network Models for Spatial Data Mining, Map Production, and Cortical Direction Selectivity by : Olga Parsons
Download or read book Neural Network Models for Spatial Data Mining, Map Production, and Cortical Direction Selectivity written by Olga Parsons and published by . This book was released on 2003 with total page 312 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Application of Artificial Neural Networks in Geoinformatics by : Saro Lee
Download or read book Application of Artificial Neural Networks in Geoinformatics written by Saro Lee and published by MDPI. This book was released on 2018-04-09 with total page 229 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a printed edition of the Special Issue "Application of Artificial Neural Networks in Geoinformatics" that was published in Applied Sciences
Book Synopsis Dissertation Abstracts International by :
Download or read book Dissertation Abstracts International written by and published by . This book was released on 2003 with total page 776 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Self-organizing Map Formation by : Klaus Obermayer
Download or read book Self-organizing Map Formation written by Klaus Obermayer and published by MIT Press. This book was released on 2001 with total page 472 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides an overview of self-organizing map formation, including recent developments. Self-organizing maps form a branch of unsupervised learning, which is the study of what can be determined about the statistical properties of input data without explicit feedback from a teacher. The articles are drawn from the journal Neural Computation.The book consists of five sections. The first section looks at attempts to model the organization of cortical maps and at the theory and applications of the related artificial neural network algorithms. The second section analyzes topographic maps and their formation via objective functions. The third section discusses cortical maps of stimulus features. The fourth section discusses self-organizing maps for unsupervised data analysis. The fifth section discusses extensions of self-organizing maps, including two surprising applications of mapping algorithms to standard computer science problems: combinatorial optimization and sorting. Contributors J. J. Atick, H. G. Barrow, H. U. Bauer, C. M. Bishop, H. J. Bray, J. Bruske, J. M. L. Budd, M. Budinich, V. Cherkassky, J. Cowan, R. Durbin, E. Erwin, G. J. Goodhill, T. Graepel, D. Grier, S. Kaski, T. Kohonen, H. Lappalainen, Z. Li, J. Lin, R. Linsker, S. P. Luttrell, D. J. C. MacKay, K. D. Miller, G. Mitchison, F. Mulier, K. Obermayer, C. Piepenbrock, H. Ritter, K. Schulten, T. J. Sejnowski, S. Smirnakis, G. Sommer, M. Svensen, R. Szeliski, A. Utsugi, C. K. I. Williams, L. Wiskott, L. Xu, A. Yuille, J. Zhang
Book Synopsis The Relevance of the Time Domain to Neural Network Models by : A. Ravishankar Rao
Download or read book The Relevance of the Time Domain to Neural Network Models written by A. Ravishankar Rao and published by Springer Science & Business Media. This book was released on 2011-09-18 with total page 234 pages. Available in PDF, EPUB and Kindle. Book excerpt: A significant amount of effort in neural modeling is directed towards understanding the representation of information in various parts of the brain, such as cortical maps [6], and the paths along which sensory information is processed. Though the time domain is integral an integral aspect of the functioning of biological systems, it has proven very challenging to incorporate the time domain effectively in neural network models. A promising path that is being explored is to study the importance of synchronization in biological systems. Synchronization plays a critical role in the interactions between neurons in the brain, giving rise to perceptual phenomena, and explaining multiple effects such as visual contour integration, and the separation of superposed inputs. The purpose of this book is to provide a unified view of how the time domain can be effectively employed in neural network models. A first direction to consider is to deploy oscillators that model temporal firing patterns of a neuron or a group of neurons. There is a growing body of research on the use of oscillatory neural networks, and their ability to synchronize under the right conditions. Such networks of synchronizing elements have been shown to be effective in image processing and segmentation tasks, and also in solving the binding problem, which is of great significance in the field of neuroscience. The oscillatory neural models can be employed at multiple scales of abstraction, ranging from individual neurons, to groups of neurons using Wilson-Cowan modeling techniques and eventually to the behavior of entire brain regions as revealed in oscillations observed in EEG recordings. A second interesting direction to consider is to understand the effect of different neural network topologies on their ability to create the desired synchronization. A third direction of interest is the extraction of temporal signaling patterns from brain imaging data such as EEG and fMRI. Hence this Special Session is of emerging interest in the brain sciences, as imaging techniques are able to resolve sufficient temporal detail to provide an insight into how the time domain is deployed in cognitive function. The following broad topics will be covered in the book: Synchronization, phase-locking behavior, image processing, image segmentation, temporal pattern analysis, EEG analysis, fMRI analyis, network topology and synchronizability, cortical interactions involving synchronization, and oscillatory neural networks. This book will benefit readers interested in the topics of computational neuroscience, applying neural network models to understand brain function, extracting temporal information from brain imaging data, and emerging techniques for image segmentation using oscillatory networks
Book Synopsis ARTMAP Neural Networks for Information Fusion and Data Mining by : Olga Parsons
Download or read book ARTMAP Neural Networks for Information Fusion and Data Mining written by Olga Parsons and published by . This book was released on 2002 with total page 74 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Theoretical Neuroscience by : Peter Dayan
Download or read book Theoretical Neuroscience written by Peter Dayan and published by MIT Press. This book was released on 2005-08-12 with total page 477 pages. Available in PDF, EPUB and Kindle. Book excerpt: Theoretical neuroscience provides a quantitative basis for describing what nervous systems do, determining how they function, and uncovering the general principles by which they operate. This text introduces the basic mathematical and computational methods of theoretical neuroscience and presents applications in a variety of areas including vision, sensory-motor integration, development, learning, and memory. The book is divided into three parts. Part I discusses the relationship between sensory stimuli and neural responses, focusing on the representation of information by the spiking activity of neurons. Part II discusses the modeling of neurons and neural circuits on the basis of cellular and synaptic biophysics. Part III analyzes the role of plasticity in development and learning. An appendix covers the mathematical methods used, and exercises are available on the book's Web site.
Book Synopsis Modern Techniques in Neuroscience Research by : Uwe Windhorst
Download or read book Modern Techniques in Neuroscience Research written by Uwe Windhorst and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 1336 pages. Available in PDF, EPUB and Kindle. Book excerpt: An overview of the techniques used in modern neuroscience research with the emphasis on showing how different techniques can optimally be combined in the study of problems that arise at some levels of nervous system organization. This is essentially a working tool for the scientist in the laboratory and clinic, providing detailed step-by-step protocols with tips and recommendations. Most chapters and protocols are organized such that they can be used independently, while cross-references between the chapters, a glossary, a list of suppliers and appendices provide further help.
Book Synopsis Neural Networks and Statistical Learning by : Ke-Lin Du
Download or read book Neural Networks and Statistical Learning written by Ke-Lin Du and published by Springer Science & Business Media. This book was released on 2013-12-09 with total page 834 pages. Available in PDF, EPUB and Kindle. Book excerpt: Providing a broad but in-depth introduction to neural network and machine learning in a statistical framework, this book provides a single, comprehensive resource for study and further research. All the major popular neural network models and statistical learning approaches are covered with examples and exercises in every chapter to develop a practical working understanding of the content. Each of the twenty-five chapters includes state-of-the-art descriptions and important research results on the respective topics. The broad coverage includes the multilayer perceptron, the Hopfield network, associative memory models, clustering models and algorithms, the radial basis function network, recurrent neural networks, principal component analysis, nonnegative matrix factorization, independent component analysis, discriminant analysis, support vector machines, kernel methods, reinforcement learning, probabilistic and Bayesian networks, data fusion and ensemble learning, fuzzy sets and logic, neurofuzzy models, hardware implementations, and some machine learning topics. Applications to biometric/bioinformatics and data mining are also included. Focusing on the prominent accomplishments and their practical aspects, academic and technical staff, graduate students and researchers will find that this provides a solid foundation and encompassing reference for the fields of neural networks, pattern recognition, signal processing, machine learning, computational intelligence, and data mining.
Book Synopsis Fundamentals of Computerized Tomography by : Gabor T. Herman
Download or read book Fundamentals of Computerized Tomography written by Gabor T. Herman and published by Springer Science & Business Media. This book was released on 2009-07-14 with total page 302 pages. Available in PDF, EPUB and Kindle. Book excerpt: This revised and updated second edition – now with two new chapters - is the only book to give a comprehensive overview of computer algorithms for image reconstruction. It covers the fundamentals of computerized tomography, including all the computational and mathematical procedures underlying data collection, image reconstruction and image display. Among the new topics covered are: spiral CT, fully 3D positron emission tomography, the linogram mode of backprojection, and state of the art 3D imaging results. It also includes two new chapters on comparative statistical evaluation of the 2D reconstruction algorithms and alternative approaches to image reconstruction.
Book Synopsis Neural Networks in a Softcomputing Framework by : Ke-Lin Du
Download or read book Neural Networks in a Softcomputing Framework written by Ke-Lin Du and published by Springer Science & Business Media. This book was released on 2006-08-02 with total page 610 pages. Available in PDF, EPUB and Kindle. Book excerpt: This concise but comprehensive textbook reviews the most popular neural-network methods and their associated techniques. Each chapter provides state-of-the-art descriptions of important major research results of the respective neural-network methods. A range of relevant computational intelligence topics, such as fuzzy logic and evolutionary algorithms – powerful tools for neural-network learning – are introduced. The systematic survey of neural-network models and exhaustive references list will point readers toward topics for future research. The algorithms outlined also make this textbook a valuable reference for scientists and practitioners working in pattern recognition, signal processing, speech and image processing, data analysis and artificial intelligence.
Book Synopsis Neural Networks for Pattern Recognition by : Christopher M. Bishop
Download or read book Neural Networks for Pattern Recognition written by Christopher M. Bishop and published by Oxford University Press. This book was released on 1995-11-23 with total page 501 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical pattern recognition; Probability density estimation; Single-layer networks; The multi-layer perceptron; Radial basis functions; Error functions; Parameter optimization algorithms; Pre-processing and feature extraction; Learning and generalization; Bayesian techniques; Appendix; References; Index.
Download or read book Dendrites written by Greg Stuart and published by Oxford University Press, USA. This book was released on 2007 with total page 578 pages. Available in PDF, EPUB and Kindle. Book excerpt: Dendrites form the major receiving part of neurons. This text presents a survey of knowledge on dendrites, from their morphology and development, through to their electrical chemical, and computational properties.
Book Synopsis Mathematical Foundations of Neuroscience by : G. Bard Ermentrout
Download or read book Mathematical Foundations of Neuroscience written by G. Bard Ermentrout and published by Springer Science & Business Media. This book was released on 2010-07-01 with total page 434 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book applies methods from nonlinear dynamics to problems in neuroscience. It uses modern mathematical approaches to understand patterns of neuronal activity seen in experiments and models of neuronal behavior. The intended audience is researchers interested in applying mathematics to important problems in neuroscience, and neuroscientists who would like to understand how to create models, as well as the mathematical and computational methods for analyzing them. The authors take a very broad approach and use many different methods to solve and understand complex models of neurons and circuits. They explain and combine numerical, analytical, dynamical systems and perturbation methods to produce a modern approach to the types of model equations that arise in neuroscience. There are extensive chapters on the role of noise, multiple time scales and spatial interactions in generating complex activity patterns found in experiments. The early chapters require little more than basic calculus and some elementary differential equations and can form the core of a computational neuroscience course. Later chapters can be used as a basis for a graduate class and as a source for current research in mathematical neuroscience. The book contains a large number of illustrations, chapter summaries and hundreds of exercises which are motivated by issues that arise in biology, and involve both computation and analysis. Bard Ermentrout is Professor of Computational Biology and Professor of Mathematics at the University of Pittsburgh. David Terman is Professor of Mathematics at the Ohio State University.
Book Synopsis The Cognitive Neurosciences by : Michael S. Gazzaniga
Download or read book The Cognitive Neurosciences written by Michael S. Gazzaniga and published by MIT Press. This book was released on 2009-09-18 with total page 1377 pages. Available in PDF, EPUB and Kindle. Book excerpt: "The fourth edition of The Cognitive Neurosciences continues to chart new directions in the study of the biologic underpinnings of complex cognition - the relationship between the structural and physiological mechanisms of the nervous system and the psychological reality of the mind. The material in this edition is entirely new, with all chapters written specifically for it." --Book Jacket.
Book Synopsis Mathematics for Neuroscientists by : Fabrizio Gabbiani
Download or read book Mathematics for Neuroscientists written by Fabrizio Gabbiani and published by Academic Press. This book was released on 2017-02-04 with total page 630 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mathematics for Neuroscientists, Second Edition, presents a comprehensive introduction to mathematical and computational methods used in neuroscience to describe and model neural components of the brain from ion channels to single neurons, neural networks and their relation to behavior. The book contains more than 200 figures generated using Matlab code available to the student and scholar. Mathematical concepts are introduced hand in hand with neuroscience, emphasizing the connection between experimental results and theory. Fully revised material and corrected text Additional chapters on extracellular potentials, motion detection and neurovascular coupling Revised selection of exercises with solutions More than 200 Matlab scripts reproducing the figures as well as a selection of equivalent Python scripts
Book Synopsis Learning to Think Spatially by : National Research Council
Download or read book Learning to Think Spatially written by National Research Council and published by National Academies Press. This book was released on 2005-02-03 with total page 333 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learning to Think Spatially examines how spatial thinking might be incorporated into existing standards-based instruction across the school curriculum. Spatial thinking must be recognized as a fundamental part of Kâ€"12 education and as an integrator and a facilitator for problem solving across the curriculum. With advances in computing technologies and the increasing availability of geospatial data, spatial thinking will play a significant role in the information-based economy of the twenty-first century. Using appropriately designed support systems tailored to the Kâ€"12 context, spatial thinking can be taught formally to all students. A geographic information system (GIS) offers one example of a high-technology support system that can enable students and teachers to practice and apply spatial thinking in many areas of the curriculum.
