Neural Modeling Fields

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Neural Fields

Author : Stephen Coombes
Publisher : Springer
ISBN 13 : 3642545939
Total Pages : 488 pages
Book Rating : 4.6/5 (425 download)

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Book Synopsis Neural Fields by : Stephen Coombes

Download or read book Neural Fields written by Stephen Coombes and published by Springer. This book was released on 2014-06-17 with total page 488 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neural field theory has a long-standing tradition in the mathematical and computational neurosciences. Beginning almost 50 years ago with seminal work by Griffiths and culminating in the 1970ties with the models of Wilson and Cowan, Nunez and Amari, this important research area experienced a renaissance during the 1990ties by the groups of Ermentrout, Robinson, Bressloff, Wright and Haken. Since then, much progress has been made in both, the development of mathematical and numerical techniques and in physiological refinement und understanding. In contrast to large-scale neural network models described by huge connectivity matrices that are computationally expensive in numerical simulations, neural field models described by connectivity kernels allow for analytical treatment by means of methods from functional analysis. Thus, a number of rigorous results on the existence of bump and wave solutions or on inverse kernel construction problems are nowadays available. Moreover, neural fields provide an important interface for the coupling of neural activity to experimentally observable data, such as the electroencephalogram (EEG) or functional magnetic resonance imaging (fMRI). And finally, neural fields over rather abstract feature spaces, also called dynamic fields, found successful applications in the cognitive sciences and in robotics. Up to now, research results in neural field theory have been disseminated across a number of distinct journals from mathematics, computational neuroscience, biophysics, cognitive science and others. There is no comprehensive collection of results or reviews available yet. With our proposed book Neural Field Theory, we aim at filling this gap in the market. We received consent from some of the leading scientists in the field, who are willing to write contributions for the book, among them are two of the founding-fathers of neural field theory: Shun-ichi Amari and Jack Cowan.

Neural Modeling Fields

Author : Fouad Sabry
Publisher : One Billion Knowledgeable
ISBN 13 :
Total Pages : 189 pages
Book Rating : 4.:/5 (661 download)

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Book Synopsis Neural Modeling Fields by : Fouad Sabry

Download or read book Neural Modeling Fields written by Fouad Sabry and published by One Billion Knowledgeable. This book was released on 2023-07-04 with total page 189 pages. Available in PDF, EPUB and Kindle. Book excerpt: What Is Neural Modeling Fields Neural modeling field (NMF) is a mathematical framework for machine learning that integrates ideas from neural networks, fuzzy logic, and model based recognition. Its acronym stands for "Neural Modeling Field." Modeling fields, modeling fields theory (MFT), and Maximum likelihood artificial neural networks (MLANS) are some of the other names that have been used to refer to this concept.At the AFRL, Leonid Perlovsky is the one responsible for developing this framework. The NMF can be understood as a mathematical description of the machinery that make up the mind. These mechanisms include ideas, feelings, instincts, imagination, reasoning, and comprehension. The NMF is organized in a hetero-hierarchical structure that contains many levels. There are concept-models that encapsulate the knowledge at each level of the NMF. These concept-models generate so-called top-down signals, which interact with input signals that come from lower levels. These interactions are governed by dynamic equations, which are responsible for driving concept-model learning, adaptation, and the development of new concept-models for better correspondence to the input, bottom-up signals. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Neural modeling fields Chapter 2: Machine learning Chapter 3: Supervised learning Chapter 4: Unsupervised learning Chapter 5: Weak supervision Chapter 6: Reinforcement learning Chapter 7: Neural network Chapter 8: Artificial neural network Chapter 9: Fuzzy logic Chapter 10: Adaptive neuro fuzzy inference system (II) Answering the public top questions about neural modeling fields. (III) Real world examples for the usage of neural modeling fields in many fields. (IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of neural modeling fields' technologies. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of neural modeling fields.

Neural Masses and Fields: Modelling the Dynamics of Brain Activity

Author : Karl Friston
Publisher : Frontiers Media SA
ISBN 13 : 2889194272
Total Pages : 238 pages
Book Rating : 4.8/5 (891 download)

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Book Synopsis Neural Masses and Fields: Modelling the Dynamics of Brain Activity by : Karl Friston

Download or read book Neural Masses and Fields: Modelling the Dynamics of Brain Activity written by Karl Friston and published by Frontiers Media SA. This book was released on 2015-05-25 with total page 238 pages. Available in PDF, EPUB and Kindle. Book excerpt: Biophysical modelling of brain activity has a long and illustrious history and has recently profited from technological advances that furnish neuroimaging data at an unprecedented spatiotemporal resolution. Neuronal modelling 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. Biophysical modelling 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 by promoting papers that contribute to a deeper understanding of neural activity as measured by fMRI or electrophysiology. In general, mean field models of neural activity can be divided into two classes: neural mass and neural field models. The main difference between these 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 activity over time only; by assuming that all neurons in a population are located at (approximately) the same point. This Research Topic focuses on both classes of models and considers several aspects and their relative merits 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 data - to infer brain states and map physiological parameters.

Neuronal Dynamics

Author : Wulfram Gerstner
Publisher : Cambridge University Press
ISBN 13 : 1107060834
Total Pages : 591 pages
Book Rating : 4.1/5 (7 download)

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Book Synopsis Neuronal Dynamics by : Wulfram Gerstner

Download or read book Neuronal Dynamics written by Wulfram Gerstner and published by Cambridge University Press. This book was released on 2014-07-24 with total page 591 pages. Available in PDF, EPUB and Kindle. Book excerpt: This solid introduction uses the principles of physics and the tools of mathematics to approach fundamental questions of neuroscience.

Statistical Field Theory for Neural Networks

Author : Moritz Helias
Publisher : Springer Nature
ISBN 13 : 303046444X
Total Pages : 203 pages
Book Rating : 4.0/5 (34 download)

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Book Synopsis Statistical Field Theory for Neural Networks by : Moritz Helias

Download or read book Statistical Field Theory for Neural Networks written by Moritz Helias and published by Springer Nature. This book was released on 2020-08-20 with total page 203 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a self-contained introduction to techniques from field theory applied to stochastic and collective dynamics in neuronal networks. These powerful analytical techniques, which are well established in other fields of physics, are the basis of current developments and offer solutions to pressing open problems in theoretical neuroscience and also machine learning. They enable a systematic and quantitative understanding of the dynamics in recurrent and stochastic neuronal networks. This book is intended for physicists, mathematicians, and computer scientists and it is designed for self-study by researchers who want to enter the field or as the main text for a one semester course at advanced undergraduate or graduate level. The theoretical concepts presented in this book are systematically developed from the very beginning, which only requires basic knowledge of analysis and linear algebra.

Dynamic Thinking

Author : Gregor Schöner
Publisher : Oxford University Press
ISBN 13 : 0199300569
Total Pages : 421 pages
Book Rating : 4.1/5 (993 download)

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Book Synopsis Dynamic Thinking by : Gregor Schöner

Download or read book Dynamic Thinking written by Gregor Schöner and published by Oxford University Press. This book was released on 2016 with total page 421 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book describes a new theoretical approach--Dynamic Field Theory (DFT)--that explains how people think and act"--

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.

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.

Tutorial on Neural Systems Modeling

Author : Thomas J. Anastasio
Publisher : Sinauer
ISBN 13 : 9780878933396
Total Pages : 0 pages
Book Rating : 4.9/5 (333 download)

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Book Synopsis Tutorial on Neural Systems Modeling by : Thomas J. Anastasio

Download or read book Tutorial on Neural Systems Modeling written by Thomas J. Anastasio and published by Sinauer. This book was released on 2010-03-01 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: For students of neuroscience and cognitive science who wish to explore the functioning of the brain further, but lack an extensive background in computer programming or maths, this new book makes neural systems modelling truly accessible. Short, simple MATLAB computer programs give readers all the experience necessary to run their own simulations.

Neural Networks and Intellect

Author : Leonid I. Perlovsky
Publisher : Oxford University Press, USA
ISBN 13 : 9780195111620
Total Pages : 469 pages
Book Rating : 4.1/5 (116 download)

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Book Synopsis Neural Networks and Intellect by : Leonid I. Perlovsky

Download or read book Neural Networks and Intellect written by Leonid I. Perlovsky and published by Oxford University Press, USA. This book was released on 2001 with total page 469 pages. Available in PDF, EPUB and Kindle. Book excerpt: This work describes a mathematical concept of modelling field theory and its applications to a variety of problems, while offering a view of the relationships among mathematics, computational concepts in neural networks, semiotics, and concepts of mind in psychology and philosophy.

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.

Semi-empirical Neural Network Modeling and Digital Twins Development

Author : Dmitriy Tarkhov
Publisher : Academic Press
ISBN 13 : 012815652X
Total Pages : 290 pages
Book Rating : 4.1/5 (281 download)

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Book Synopsis Semi-empirical Neural Network Modeling and Digital Twins Development by : Dmitriy Tarkhov

Download or read book Semi-empirical Neural Network Modeling and Digital Twins Development written by Dmitriy Tarkhov and published by Academic Press. This book was released on 2019-11-23 with total page 290 pages. Available in PDF, EPUB and Kindle. Book excerpt: Semi-empirical Neural Network Modeling presents a new approach on how to quickly construct an accurate, multilayered neural network solution of differential equations. Current neural network methods have significant disadvantages, including a lengthy learning process and single-layered neural networks built on the finite element method (FEM). The strength of the new method presented in this book is the automatic inclusion of task parameters in the final solution formula, which eliminates the need for repeated problem-solving. This is especially important for constructing individual models with unique features. The book illustrates key concepts through a large number of specific problems, both hypothetical models and practical interest. - Offers a new approach to neural networks using a unified simulation model at all stages of design and operation - Illustrates this new approach with numerous concrete examples throughout the book - Presents the methodology in separate and clearly-defined stages

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.

A Comprehensive Guide to Neural Network Modeling

Author : Steffen Skaar
Publisher : Nova Science Publishers
ISBN 13 : 9781536185423
Total Pages : 172 pages
Book Rating : 4.1/5 (854 download)

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Book Synopsis A Comprehensive Guide to Neural Network Modeling by : Steffen Skaar

Download or read book A Comprehensive Guide to Neural Network Modeling written by Steffen Skaar and published by Nova Science Publishers. This book was released on 2020-10-26 with total page 172 pages. Available in PDF, EPUB and Kindle. Book excerpt: As artificial neural networks have been gaining importance in the field of engineering, this compilation aims to review the scientific literature regarding the use of artificial neural networks for the modelling and optimization of food drying processes. The applications of artificial neural networks in food engineering are presented, particularly focusing on control, monitoring and modeling of industrial food processes.The authors emphasize the main achievements of artificial neural network modeling in recent years in the field of quantitative structure-activity relationships and quantitative structure-retention relationships.In the closing study, artificial intelligence techniques are applied to river water quality data and artificial intelligence models are developed in an effort to contribute to the reduction of the cost of future on-line measurement stations.

Neural Networks

Author : Gérard Dreyfus
Publisher : Springer Science & Business Media
ISBN 13 : 3540288473
Total Pages : 509 pages
Book Rating : 4.5/5 (42 download)

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Book Synopsis Neural Networks by : Gérard Dreyfus

Download or read book Neural Networks written by Gérard Dreyfus and published by Springer Science & Business Media. This book was released on 2005-11-25 with total page 509 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neural networks represent a powerful data processing technique that has reached maturity and broad application. When clearly understood and appropriately used, they are a mandatory component in the toolbox of any engineer who wants make the best use of the available data, in order to build models, make predictions, mine data, recognize shapes or signals, etc. Ranging from theoretical foundations to real-life applications, this book is intended to provide engineers and researchers with clear methodologies for taking advantage of neural networks in industrial, financial or banking applications, many instances of which are presented in the book. For the benefit of readers wishing to gain deeper knowledge of the topics, the book features appendices that provide theoretical details for greater insight, and algorithmic details for efficient programming and implementation. The chapters have been written by experts and edited to present a coherent and comprehensive, yet not redundant, practically oriented introduction.

The Handbook of Brain Theory and Neural Networks

Author : Michael A. Arbib
Publisher : MIT Press
ISBN 13 : 0262011972
Total Pages : 1328 pages
Book Rating : 4.2/5 (62 download)

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Book Synopsis The Handbook of Brain Theory and Neural Networks by : Michael A. Arbib

Download or read book The Handbook of Brain Theory and Neural Networks written by Michael A. Arbib and published by MIT Press. This book was released on 2003 with total page 1328 pages. Available in PDF, EPUB and Kindle. Book excerpt: This second edition presents the enormous progress made in recent years in the many subfields related to the two great questions : how does the brain work? and, How can we build intelligent machines? This second edition greatly increases the coverage of models of fundamental neurobiology, cognitive neuroscience, and neural network approaches to language. (Midwest).

Practical Applications of Sparse Modeling

Author : Irina Rish
Publisher : MIT Press
ISBN 13 : 0262027720
Total Pages : 265 pages
Book Rating : 4.2/5 (62 download)

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Book Synopsis Practical Applications of Sparse Modeling by : Irina Rish

Download or read book Practical Applications of Sparse Modeling written by Irina Rish and published by MIT Press. This book was released on 2014-09-12 with total page 265 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Sparse modeling is a rapidly developing area at the intersection of statistical learning and signal processing, motivated by the age-old statistical problem of selecting a small number of predictive variables in high-dimensional data sets. This collection describes key approaches in sparse modeling, focusing on its applications in such fields as neuroscience, computational biology, and computer vision. Sparse modeling methods can improve the interpretability of predictive models and aid efficient recovery of high-dimensional unobserved signals from a limited number of measurements. Yet despite significant advances in the field, a number of open issues remain when sparse modeling meets real-life applications. The book discusses a range of practical applications and state-of-the-art approaches for tackling the challenges presented by these applications. Topics considered include the choice of method in genomics applications; analysis of protein mass-spectrometry data; the stability of sparse models in brain imaging applications; sequential testing approaches; algorithmic aspects of sparse recovery; and learning sparse latent models"--Jacket.