Neural Modeling And Neural Networks

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Neural Modeling and Neural Networks

Author : F. Ventriglia
Publisher : Elsevier
ISBN 13 : 1483287904
Total Pages : 363 pages
Book Rating : 4.4/5 (832 download)

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Book Synopsis Neural Modeling and Neural Networks by : F. Ventriglia

Download or read book Neural Modeling and Neural Networks written by F. Ventriglia and published by Elsevier. This book was released on 2013-10-22 with total page 363 pages. Available in PDF, EPUB and Kindle. Book excerpt: Research in neural modeling and neural networks has escalated dramatically in the last decade, acquiring along the way terms and concepts, such as learning, memory, perception, recognition, which are the basis of neuropsychology. Nevertheless, for many, neural modeling remains controversial in its purported ability to describe brain activity. The difficulties in "modeling" are various, but arise principally in identifying those elements that are fundamental for the expression (and description) of superior neural activity. This is complicated by our incomplete knowledge of neural structures and functions, at the cellular and population levels. The first step towards enhanced appreciation of the value of neural modeling and neural networks is to be aware of what has been achieved in this multidisciplinary field of research. This book sets out to create such awareness. Leading experts develop in twelve chapters the key topics of neural structures and functions, dynamics of single neurons, oscillations in groups of neurons, randomness and chaos in neural activity, (statistical) dynamics of neural networks, learning, memory and pattern recognition.

Neural Modeling

Author : Ronald MacGregor
Publisher : Springer Science & Business Media
ISBN 13 : 1468421905
Total Pages : 413 pages
Book Rating : 4.4/5 (684 download)

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Book Synopsis Neural Modeling by : Ronald MacGregor

Download or read book Neural Modeling written by Ronald MacGregor and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 413 pages. Available in PDF, EPUB and Kindle. Book excerpt: The purpose of this book is to introduce and survey the various quantitative methods which have been proposed for describing, simulating, embodying, or characterizing the processing of electrical signals in nervous systems. We believe that electrical signal processing is a vital determinant of the functional organization of the brain, and that in unraveling the inherent complexities of this processing it will be essential to utilize the methods of quantification and modeling which have led to crowning successes in the physical and engineering sciences. In comprehensive terms, we conceive neural modeling to be the attempt to relate, in nervous systems, function to structure on the basis of operation. Sufficient knowledge and appropriate tools are at hand to maintain a serious and thorough study in the area. However, work in the area has yet to be satisfactorily integrated within contemporary brain research. Moreover, there exists a good deal of inefficiency within the area resulting from an overall lack of direction, critical self-evaluation, and cohesion. Such theoretical and modeling studies as have appeared exist largely as fragmented islands in the literature or as sparsely attended sessions at neuroscience conferences. In writing this book, we were guided by three main immediate objectives. Our first objective is to introduce the area to the upcoming generation of students of both the hard sciences and psychological and biological sciences in the hope that they might eventually help bring about the contributions it promises.

Neural Networks for Statistical Modeling

Author : Murray Smith
Publisher : Van Nostrand Reinhold Company
ISBN 13 :
Total Pages : 268 pages
Book Rating : 4.F/5 ( download)

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Book Synopsis Neural Networks for Statistical Modeling by : Murray Smith

Download or read book Neural Networks for Statistical Modeling written by Murray Smith and published by Van Nostrand Reinhold Company. This book was released on 1993 with total page 268 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Artificial Neural Network Modelling

Author : Subana Shanmuganathan
Publisher : Springer
ISBN 13 : 3319284959
Total Pages : 472 pages
Book Rating : 4.3/5 (192 download)

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Book Synopsis Artificial Neural Network Modelling by : Subana Shanmuganathan

Download or read book Artificial Neural Network Modelling written by Subana Shanmuganathan and published by Springer. This book was released on 2016-02-03 with total page 472 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers theoretical aspects as well as recent innovative applications of Artificial Neural networks (ANNs) in natural, environmental, biological, social, industrial and automated systems. It presents recent results of ANNs in modelling small, large and complex systems under three categories, namely, 1) Networks, Structure Optimisation, Robustness and Stochasticity 2) Advances in Modelling Biological and Environmental Systems and 3) Advances in Modelling Social and Economic Systems. The book aims at serving undergraduates, postgraduates and researchers in ANN computational modelling.

Artificial Higher Order Neural Networks for Modeling and Simulation

Author : Zhang, Ming
Publisher : IGI Global
ISBN 13 : 1466621761
Total Pages : 455 pages
Book Rating : 4.4/5 (666 download)

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Book Synopsis Artificial Higher Order Neural Networks for Modeling and Simulation by : Zhang, Ming

Download or read book Artificial Higher Order Neural Networks for Modeling and Simulation written by Zhang, Ming and published by IGI Global. This book was released on 2012-10-31 with total page 455 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book introduces Higher Order Neural Networks (HONNs) to computer scientists and computer engineers as an open box neural networks tool when compared to traditional artificial neural networks"--Provided by publisher.

Advanced Models of Neural Networks

Author : Gerasimos G. Rigatos
Publisher : Springer
ISBN 13 : 3662437643
Total Pages : 296 pages
Book Rating : 4.6/5 (624 download)

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Book Synopsis Advanced Models of Neural Networks by : Gerasimos G. Rigatos

Download or read book Advanced Models of Neural Networks written by Gerasimos G. Rigatos and published by Springer. This book was released on 2014-08-27 with total page 296 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a complete study on neural structures exhibiting nonlinear and stochastic dynamics, elaborating on neural dynamics by introducing advanced models of neural networks. It overviews the main findings in the modelling of neural dynamics in terms of electrical circuits and examines their stability properties with the use of dynamical systems theory. It is suitable for researchers and postgraduate students engaged with neural networks and dynamical systems theory.

Modeling Brain Function

Author : D. J. Amit
Publisher : Cambridge University Press
ISBN 13 : 9780521421249
Total Pages : 528 pages
Book Rating : 4.4/5 (212 download)

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Book Synopsis Modeling Brain Function by : D. J. Amit

Download or read book Modeling Brain Function written by D. J. Amit and published by Cambridge University Press. This book was released on 1989 with total page 528 pages. Available in PDF, EPUB and Kindle. Book excerpt: One of the most exciting and potentially rewarding areas of scientific research is the study of the principles and mechanisms underlying brain function. It is also of great promise to future generations of computers. A growing group of researchers, adapting knowledge and techniques from a wide range of scientific disciplines, have made substantial progress understanding memory, the learning process, and self organization by studying the properties of models of neural networks - idealized systems containing very large numbers of connected neurons, whose interactions give rise to the special qualities of the brain. This book introduces and explains the techniques brought from physics to the study of neural networks and the insights they have stimulated. It is written at a level accessible to the wide range of researchers working on these problems - statistical physicists, biologists, computer scientists, computer technologists and cognitive psychologists. The author presents a coherent and clear nonmechanical presentation of all the basic ideas and results. More technical aspects are restricted, wherever possible, to special sections and appendices in each chapter. The book is suitable as a text for graduate courses in physics, electrical engineering, computer science and biology.

Neural Networks: Computational Models and Applications

Author : Huajin Tang
Publisher : Springer Science & Business Media
ISBN 13 : 3540692258
Total Pages : 310 pages
Book Rating : 4.5/5 (46 download)

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Book Synopsis Neural Networks: Computational Models and Applications by : Huajin Tang

Download or read book Neural Networks: Computational Models and Applications written by Huajin Tang and published by Springer Science & Business Media. This book was released on 2007-03-12 with total page 310 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neural Networks: Computational Models and Applications presents important theoretical and practical issues in neural networks, including the learning algorithms of feed-forward neural networks, various dynamical properties of recurrent neural networks, winner-take-all networks and their applications in broad manifolds of computational intelligence: pattern recognition, uniform approximation, constrained optimization, NP-hard problems, and image segmentation. The book offers a compact, insightful understanding of the broad and rapidly growing neural networks domain.

Introduction to Neural and Cognitive Modeling

Author : Daniel S. Levine
Publisher : Psychology Press
ISBN 13 : 1135692246
Total Pages : 573 pages
Book Rating : 4.1/5 (356 download)

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Book Synopsis Introduction to Neural and Cognitive Modeling by : Daniel S. Levine

Download or read book Introduction to Neural and Cognitive Modeling written by Daniel S. Levine and published by Psychology Press. This book was released on 2000-02-01 with total page 573 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thoroughly, thoughtfully revised edition of a very successful textbook makes the principles and the details of neural network modeling accessible to cognitive scientists of all varieties as well as to others interested in these models. Research since the publication of the first edition has been systematically incorporated into a framework of proven pedagogical value. Features of the second edition include: * A new section on spatiotemporal pattern processing * Coverage of ARTMAP networks (the supervised version of adaptive resonance networks) and recurrent back-propagation networks * A vastly expanded section on models of specific brain areas, such as the cerebellum, hippocampus, basal ganglia, and visual and motor cortex * Up-to-date coverage of applications of neural networks in areas such as combinatorial optimization and knowledge representation As in the first edition, the text includes extensive introductions to neuroscience and to differential and difference equations as appendices for students without the requisite background in these areas. As graphically revealed in the flowchart in the front of the book, the text begins with simpler processes and builds up to more complex multilevel functional systems. For more information visit the author's personal Web site at www.uta.edu/psychology/faculty/levine/

Model Neural Networks and Behavior

Author : Allen Selverston
Publisher : Springer Science & Business Media
ISBN 13 : 1475758588
Total Pages : 549 pages
Book Rating : 4.4/5 (757 download)

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Book Synopsis Model Neural Networks and Behavior by : Allen Selverston

Download or read book Model Neural Networks and Behavior written by Allen Selverston and published by Springer Science & Business Media. This book was released on 2013-06-29 with total page 549 pages. Available in PDF, EPUB and Kindle. Book excerpt: The most conspicuous function of the nervous system is to control animal behav ior. From the complex operations of learning and mentation to the molecular con figuration of ionic channels, the nervous system serves as the interface between an animal and its environment. To study and understand the fundamental mecha nisms underlying the control of behavior, it is often both necessary and desirable to employ biological systems with characteristics especially suitable for answering specific questions. In neurobiology, many invertebrates have become established as model systems for investigations at both the systems and the cellular level. Large, readily identifiable neurons have made invertebrates especially useful for cellular studies. The fact that these neurons occur in much smaller numbers than those in higher animals also makes them important for circuit analysis. Although important differences exist, some of the questions that would be tech nically impossible to answer with vertebrates can become experimentally tractable with invertebrates.

Forecasting: principles and practice

Author : Rob J Hyndman
Publisher : OTexts
ISBN 13 : 0987507117
Total Pages : 380 pages
Book Rating : 4.9/5 (875 download)

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Book Synopsis Forecasting: principles and practice by : Rob J Hyndman

Download or read book Forecasting: principles and practice written by Rob J Hyndman and published by OTexts. This book was released on 2018-05-08 with total page 380 pages. Available in PDF, EPUB and Kindle. Book excerpt: Forecasting is required in many situations. Stocking an inventory may require forecasts of demand months in advance. Telecommunication routing requires traffic forecasts a few minutes ahead. Whatever the circumstances or time horizons involved, forecasting is an important aid in effective and efficient planning. This textbook provides a comprehensive introduction to forecasting methods and presents enough information about each method for readers to use them sensibly.

Artificial Neural Networks for Modelling and Control of Non-Linear Systems

Author : Johan A.K. Suykens
Publisher : Springer Science & Business Media
ISBN 13 : 1475724934
Total Pages : 242 pages
Book Rating : 4.4/5 (757 download)

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Book Synopsis Artificial Neural Networks for Modelling and Control of Non-Linear Systems by : Johan A.K. Suykens

Download or read book Artificial Neural Networks for Modelling and Control of Non-Linear Systems written by Johan A.K. Suykens and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 242 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial neural networks possess several properties that make them particularly attractive for applications to modelling and control of complex non-linear systems. Among these properties are their universal approximation ability, their parallel network structure and the availability of on- and off-line learning methods for the interconnection weights. However, dynamic models that contain neural network architectures might be highly non-linear and difficult to analyse as a result. Artificial Neural Networks for Modelling and Control of Non-Linear Systems investigates the subject from a system theoretical point of view. However the mathematical theory that is required from the reader is limited to matrix calculus, basic analysis, differential equations and basic linear system theory. No preliminary knowledge of neural networks is explicitly required. The book presents both classical and novel network architectures and learning algorithms for modelling and control. Topics include non-linear system identification, neural optimal control, top-down model based neural control design and stability analysis of neural control systems. A major contribution of this book is to introduce NLq Theory as an extension towards modern control theory, in order to analyze and synthesize non-linear systems that contain linear together with static non-linear operators that satisfy a sector condition: neural state space control systems are an example. Moreover, it turns out that NLq Theory is unifying with respect to many problems arising in neural networks, systems and control. Examples show that complex non-linear systems can be modelled and controlled within NLq theory, including mastering chaos. The didactic flavor of this book makes it suitable for use as a text for a course on Neural Networks. In addition, researchers and designers will find many important new techniques, in particular NLq emTheory, that have applications in control theory, system theory, circuit theory and Time Series Analysis.

Neural Network Modeling

Author : P. S. Neelakanta
Publisher : CRC Press
ISBN 13 : 1351428950
Total Pages : 194 pages
Book Rating : 4.3/5 (514 download)

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Book Synopsis Neural Network Modeling by : P. S. Neelakanta

Download or read book Neural Network Modeling written by P. S. Neelakanta and published by CRC Press. This book was released on 2018-02-06 with total page 194 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neural Network Modeling offers a cohesive approach to the statistical mechanics and principles of cybernetics as a basis for neural network modeling. It brings together neurobiologists and the engineers who design intelligent automata to understand the physics of collective behavior pertinent to neural elements and the self-control aspects of neurocybernetics. The theoretical perspectives and explanatory projections portray the most current information in the field, some of which counters certain conventional concepts in the visualization of neuronal interactions.

Fundamentals of Neural Network Modeling

Author : Randolph W. Parks
Publisher : MIT Press
ISBN 13 : 9780262161756
Total Pages : 450 pages
Book Rating : 4.1/5 (617 download)

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Book Synopsis Fundamentals of Neural Network Modeling by : Randolph W. Parks

Download or read book Fundamentals of Neural Network Modeling written by Randolph W. Parks and published by MIT Press. This book was released on 1998 with total page 450 pages. Available in PDF, EPUB and Kindle. Book excerpt: Provides an introduction to the neural network modeling of complex cognitive and neuropsychological processes. Over the past few years, computer modeling has become more prevalent in the clinical sciences as an alternative to traditional symbol-processing models. This book provides an introduction to the neural network modeling of complex cognitive and neuropsychological processes. It is intended to make the neural network approach accessible to practicing neuropsychologists, psychologists, neurologists, and psychiatrists. It will also be a useful resource for computer scientists, mathematicians, and interdisciplinary cognitive neuroscientists. The editors (in their introduction) and contributors explain the basic concepts behind modeling and avoid the use of high-level mathematics. The book is divided into four parts. Part I provides an extensive but basic overview of neural network modeling, including its history, present, and future trends. It also includes chapters on attention, memory, and primate studies. Part II discusses neural network models of behavioral states such as alcohol dependence, learned helplessness, depression, and waking and sleeping. Part III presents neural network models of neuropsychological tests such as the Wisconsin Card Sorting Task, the Tower of Hanoi, and the Stroop Test. Finally, part IV describes the application of neural network models to dementia: models of acetycholine and memory, verbal fluency, Parkinsons disease, and Alzheimer's disease. Contributors J. Wesson Ashford, Rajendra D. Badgaiyan, Jean P. Banquet, Yves Burnod, Nelson Butters, John Cardoso, Agnes S. Chan, Jean-Pierre Changeux, Kerry L. Coburn, Jonathan D. Cohen, Laurent Cohen, Jose L. Contreras-Vidal, Antonio R. Damasio, Hanna Damasio, Stanislas Dehaene, Martha J. Farah, Joaquin M. Fuster, Philippe Gaussier, Angelika Gissler, Dylan G. Harwood, Michael E. Hasselmo, J, Allan Hobson, Sam Leven, Daniel S. Levine, Debra L. Long, Roderick K. Mahurin, Raymond L. Ownby, Randolph W. Parks, Michael I. Posner, David P. Salmon, David Servan-Schreiber, Chantal E. Stern, Jeffrey P. Sutton, Lynette J. Tippett, Daniel Tranel, Bradley Wyble

The Neural Simulation Language

Author : Alfredo Weitzenfeld
Publisher : MIT Press
ISBN 13 : 9780262731492
Total Pages : 466 pages
Book Rating : 4.7/5 (314 download)

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Book Synopsis The Neural Simulation Language by : Alfredo Weitzenfeld

Download or read book The Neural Simulation Language written by Alfredo Weitzenfeld and published by MIT Press. This book was released on 2002 with total page 466 pages. Available in PDF, EPUB and Kindle. Book excerpt: Simulation in NSL - Modeling in NSL - Schematic Capture System - User Interface and Graphical Windows - The Modeling Language NSLM - The Scripting Language NSLS - Adaptive Resonance Theory - Depth Perception - Retina - Receptive Fields - The Associative Search Network: Landmark Learning and Hill Climbing - A Model of Primate Visual-Motor Conditional Learning - The Modular Design of the Oculomotor System in Monkeys - Crowley-Arbib Saccade Model - A Cerebellar Model of Sensorimotor Adaptation - Learning to Detour - Face Recognition by Dynamic Link Matching - Appendix I : NSLM Methods - NSLJ Extensions - NSLC Extensions - NSLJ and NSLC Differences - NSLJ and NSLC Installation Instructions.

An Introduction to the Modeling of Neural Networks

Author : Pierre Peretto
Publisher : Cambridge University Press
ISBN 13 : 9780521424875
Total Pages : 496 pages
Book Rating : 4.4/5 (248 download)

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Book Synopsis An Introduction to the Modeling of Neural Networks by : Pierre Peretto

Download or read book An Introduction to the Modeling of Neural Networks written by Pierre Peretto and published by Cambridge University Press. This book was released on 1992-10-29 with total page 496 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a beginning graduate-level introduction to neural networks which is divided into four parts.

Neural Networks

Author : Berndt Müller
Publisher : Springer Science & Business Media
ISBN 13 : 3642577601
Total Pages : 340 pages
Book Rating : 4.6/5 (425 download)

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Book Synopsis Neural Networks by : Berndt Müller

Download or read book Neural Networks written by Berndt Müller and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 340 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neural Networks presents concepts of neural-network models and techniques of parallel distributed processing in a three-step approach: - A brief overview of the neural structure of the brain and the history of neural-network modeling introduces to associative memory, preceptrons, feature-sensitive networks, learning strategies, and practical applications. - The second part covers subjects like statistical physics of spin glasses, the mean-field theory of the Hopfield model, and the "space of interactions" approach to the storage capacity of neural networks. - The final part discusses nine programs with practical demonstrations of neural-network models. The software and source code in C are on a 3 1/2" MS-DOS diskette can be run with Microsoft, Borland, Turbo-C, or compatible compilers.