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Author: A Browne
Publisher: CRC Press
ISBN: 9780750304993
Category : Mathematics
Languages : en
Pages : 294
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Book Description
Neural Network Analysis, Architectures and Applications discusses the main areas of neural networks, with each authoritative chapter covering the latest information from different perspectives. Divided into three parts, the book first lays the groundwork for understanding and simplifying networks. It then describes novel architectures and algorithms, including pulse-stream techniques, cellular neural networks, and multiversion neural computing. The book concludes by examining various neural network applications, such as neuron-fuzzy control systems and image compression. This final part of the book also provides a case study involving oil spill detection. This book is invaluable for students and practitioners who have a basic understanding of neural computing yet want to broaden and deepen their knowledge of the field.
Author: A Browne
Publisher: CRC Press
ISBN: 9780750304993
Category : Mathematics
Languages : en
Pages : 294
Get Book
Book Description
Neural Network Analysis, Architectures and Applications discusses the main areas of neural networks, with each authoritative chapter covering the latest information from different perspectives. Divided into three parts, the book first lays the groundwork for understanding and simplifying networks. It then describes novel architectures and algorithms, including pulse-stream techniques, cellular neural networks, and multiversion neural computing. The book concludes by examining various neural network applications, such as neuron-fuzzy control systems and image compression. This final part of the book also provides a case study involving oil spill detection. This book is invaluable for students and practitioners who have a basic understanding of neural computing yet want to broaden and deepen their knowledge of the field.
Author: Richard M. Golden
Publisher: MIT Press
ISBN: 9780262071741
Category : Computers
Languages : en
Pages : 452
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Book Description
For convenience, many of the proofs of the key theorems have been rewritten so that the entire book uses a relatively uniform notion.
Author: Alex Fornito
Publisher: Academic Press
ISBN: 0124081185
Category : Medical
Languages : en
Pages : 494
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Book Description
Fundamentals of Brain Network Analysis is a comprehensive and accessible introduction to methods for unraveling the extraordinary complexity of neuronal connectivity. From the perspective of graph theory and network science, this book introduces, motivates and explains techniques for modeling brain networks as graphs of nodes connected by edges, and covers a diverse array of measures for quantifying their topological and spatial organization. It builds intuition for key concepts and methods by illustrating how they can be practically applied in diverse areas of neuroscience, ranging from the analysis of synaptic networks in the nematode worm to the characterization of large-scale human brain networks constructed with magnetic resonance imaging. This text is ideally suited to neuroscientists wanting to develop expertise in the rapidly developing field of neural connectomics, and to physical and computational scientists wanting to understand how these quantitative methods can be used to understand brain organization. Extensively illustrated throughout by graphical representations of key mathematical concepts and their practical applications to analyses of nervous systems Comprehensively covers graph theoretical analyses of structural and functional brain networks, from microscopic to macroscopic scales, using examples based on a wide variety of experimental methods in neuroscience Designed to inform and empower scientists at all levels of experience, and from any specialist background, wanting to use modern methods of network science to understand the organization of the brain
Author: Edward J. Rzempoluck
Publisher: Springer Science & Business Media
ISBN: 1461217466
Category : Computers
Languages : en
Pages : 233
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Book Description
This book and software package complements the traditional data analysis tools already widely available. It presents an introduction to the analysis of data using neural network functions such as multilayer feed-forward networks using error back propagation, genetic algorithm-neural network hybrids, generalised regression neural networks, learning quantizer networks, and self-organising feature maps. In an easy-to-use, Windows-based environment it offers a wide range of data analytic tools which are not usually found together: genetic algorithms, probabilistic networks, as well as a number of related techniques that support these. Readers are assumed to have a basic understanding of computers and elementary mathematics, allowing them to quickly conduct sophisticated hands-on analyses of data sets.
Author: Daniel S. Yeung
Publisher: Springer Science & Business Media
ISBN: 3642025323
Category : Computers
Languages : en
Pages : 89
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Book Description
Artificial neural networks are used to model systems that receive inputs and produce outputs. The relationships between the inputs and outputs and the representation parameters are critical issues in the design of related engineering systems, and sensitivity analysis concerns methods for analyzing these relationships. Perturbations of neural networks are caused by machine imprecision, and they can be simulated by embedding disturbances in the original inputs or connection weights, allowing us to study the characteristics of a function under small perturbations of its parameters. This is the first book to present a systematic description of sensitivity analysis methods for artificial neural networks. It covers sensitivity analysis of multilayer perceptron neural networks and radial basis function neural networks, two widely used models in the machine learning field. The authors examine the applications of such analysis in tasks such as feature selection, sample reduction, and network optimization. The book will be useful for engineers applying neural network sensitivity analysis to solve practical problems, and for researchers interested in foundational problems in neural networks.
Author: Tommaso Fellin
Publisher: Humana Press
ISBN: 9781617796340
Category : Medical
Languages : en
Pages : 490
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Book Description
In the last few years, technologies that allow for monitoring and manipulating neuronal networks at a single cell resolution have been rapidly expanding. In Neuronal Network Analysis: Concepts and Experimental Approaches, experts in the field provide an overview of some of the most advanced techniques for the study of neuronal circuits, highlighting current technical limitations and providing a vision of what is yet to come. Broken into six convenient, interdependent section, this volume covers optical approaches, electrophysiological approaches, both in vitro and in vivo, optogenetics, morpho-functional approaches, and neuronal network analysis in humans. As a volume in the Neuromethods series, this work provides the kind of detailed description and implementation advice that is crucial for getting optimal results. Authoritative and cutting-edge, Neuronal Network Analysis: Concepts and Experimental Approaches will be useful for early career neuroscientists, as well as for experienced neuroscientists, by providing a practical framework for studying the brain as an organ while maintaining an appreciation for its cellular diversity.
Author: Zhang Yi
Publisher: Springer Science & Business Media
ISBN: 1475738196
Category : Computers
Languages : en
Pages : 244
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Book Description
Since the outstanding and pioneering research work of Hopfield on recurrent neural networks (RNNs) in the early 80s of the last century, neural networks have rekindled strong interests in scientists and researchers. Recent years have recorded a remarkable advance in research and development work on RNNs, both in theoretical research as weIl as actual applications. The field of RNNs is now transforming into a complete and independent subject. From theory to application, from software to hardware, new and exciting results are emerging day after day, reflecting the keen interest RNNs have instilled in everyone, from researchers to practitioners. RNNs contain feedback connections among the neurons, a phenomenon which has led rather naturally to RNNs being regarded as dynamical systems. RNNs can be described by continuous time differential systems, discrete time systems, or functional differential systems, and more generally, in terms of non linear systems. Thus, RNNs have to their disposal, a huge set of mathematical tools relating to dynamical system theory which has tumed out to be very useful in enabling a rigorous analysis of RNNs.
Author: Olaf Sporns
Publisher: MIT Press
ISBN: 0262528983
Category : Medical
Languages : en
Pages : 433
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Book Description
An integrative overview of network approaches to neuroscience explores the origins of brain complexity and the link between brain structure and function. Over the last decade, the study of complex networks has expanded across diverse scientific fields. Increasingly, science is concerned with the structure, behavior, and evolution of complex systems ranging from cells to ecosystems. In Networks of the Brain, Olaf Sporns describes how the integrative nature of brain function can be illuminated from a complex network perspective. Highlighting the many emerging points of contact between neuroscience and network science, the book serves to introduce network theory to neuroscientists and neuroscience to those working on theoretical network models. Sporns emphasizes how networks connect levels of organization in the brain and how they link structure to function, offering an informal and nonmathematical treatment of the subject. Networks of the Brain provides a synthesis of the sciences of complex networks and the brain that will be an essential foundation for future research.
Author: Moo K. Chung
Publisher: Cambridge University Press
ISBN: 110718486X
Category : Computers
Languages : en
Pages : 343
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Book Description
This coherent mathematical and statistical approach aimed at graduate students incorporates regression and topology as well as graph theory.
Author: Grady Hanrahan
Publisher: CRC Press
ISBN: 1439812594
Category : Mathematics
Languages : en
Pages : 206
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Book Description
Originating from models of biological neural systems, artificial neural networks (ANN) are the cornerstones of artificial intelligence research. Catalyzed by the upsurge in computational power and availability, and made widely accessible with the co-evolution of software, algorithms, and methodologies, artificial neural networks have had a profound
