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.

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.

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.

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.

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.

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"--

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.

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/

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.

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.

Download or read book Neural Networks and Neural Fields written by Roderick Edwards and published by . This book was released on 1994 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

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.

Download or read book Analysis and Modeling of Neural Systems written by Frank H. Eeckman and published by Springer Science & Business Media. This book was released on 2012-02-02 with total page 400 pages. Available in PDF, EPUB and Kindle. Book excerpt: I - Analysis and Modeling Tools and Techniques.- Section 1: Analysis.- Assembly Connectivity and Activity: Methods, Results, Interpretations.- Visualization of Cortical Connections With Voltage Sensitive Dyes.- Channels, Coupling, and Synchronized Rhythmic Bursting Activity.- Sparse-stimulation and Wiener Kernels.- Quantitative Search for Stimulus-Specific Patterns in the Human Electroencephalogram (EEG) During a Somatosensory Task.- Section 2: Modeling.- Functional Insights About Synaptic Inputs to Dendrites.- Dendritic Control of Hebbian Computations.- Low Threshold Spikes and Rhythmic Oscil.

Download or read book Neural Networks and Neural Fields microform Discrete and Continuous Space Neural Models written by Roderick Edwards and published by National Library of Canada = Bibliothèque nationale du Canada. This book was released on 1994 with total page 346 pages. Available in PDF, EPUB and Kindle. Book excerpt:

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.

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.

Download or read book Modeling in the Neurosciences written by R.R. Poznanski and published by Routledge. This book was released on 2019-01-22 with total page 528 pages. Available in PDF, EPUB and Kindle. Book excerpt: With contributions from more than 40 renowned experts, Modeling in the Neurosciences: From Ionic Channels to Neural Networks is essential for those interested in neuronal modeling and quantitative neiroscience. Focusing on new mathematical and computer models, techniques and methods, this monograph represents a cohesive and comprehensive treatment