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Dynamic Interactions In Neural Networks Models And Data
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Book Synopsis Dynamic Interactions in Neural Networks: Models and Data by : Michael A. Arbib
Download or read book Dynamic Interactions in Neural Networks: Models and Data written by Michael A. Arbib and published by Springer Science & Business Media. This book was released on 1989 with total page 296 pages. Available in PDF, EPUB and Kindle. Book excerpt: The study of neural networks is enjoying a great renaissance, both in computational neuroscience, the development of information processing models of living brains, and in neural computing, the use of neurally inspired concepts in the construction of "intelligent" machines. Thus the title of this volume has two interpretations: It presents models and data on the dynamic interactions occurring in the brain, and it exhibits the dynamic interactions between research in computational neuroscience and in neural computing, as scientists seek to find common principles to guide the understanding of the living brain and the design of artificial neural networks. This collection of contributions presents the current state of research, future trends and open problems in an exciting field of today's science.
Book Synopsis Dynamic Interactions in Neural Networks by : Michael A Arbib
Download or read book Dynamic Interactions in Neural Networks written by Michael A Arbib and published by . This book was released on 1988-12-01 with total page 292 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Dynamic Interactions in Neural Networks: Models and Data (Volume 1). by : MA Arbib (ed)
Download or read book Dynamic Interactions in Neural Networks: Models and Data (Volume 1). written by MA Arbib (ed) and published by . This book was released on 1988 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis The Principles of Deep Learning Theory by : Daniel A. Roberts
Download or read book The Principles of Deep Learning Theory written by Daniel A. Roberts and published by Cambridge University Press. This book was released on 2022-05-26 with total page 473 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume develops an effective theory approach to understanding deep neural networks of practical relevance.
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 Artificial Neural Networks - ICANN 2006 by : Stefanos Kollias
Download or read book Artificial Neural Networks - ICANN 2006 written by Stefanos Kollias and published by Springer. This book was released on 2006-09-01 with total page 1041 pages. Available in PDF, EPUB and Kindle. Book excerpt: The two-volume set LNCS 4131 and LNCS 4132 constitutes the refereed proceedings of the 16th International Conference on Artificial Neural Networks, ICANN 2006. The set presents 208 revised full papers, carefully reviewed and selected from 475 submissions. This first volume presents 103 papers, organized in topical sections on feature selection and dimension reduction for regression, learning algorithms, advances in neural network learning methods, ensemble learning, hybrid architectures, and more.
Book Synopsis Connectionistic Problem Solving by : HAMPSON
Download or read book Connectionistic Problem Solving written by HAMPSON and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 277 pages. Available in PDF, EPUB and Kindle. Book excerpt: 1. 1 The problem and the approach The model developed here, which is actually more a collection of com ponents than a single monolithic structure, traces a path from relatively low-level neural/connectionistic structures and processes to relatively high-level animal/artificial intelligence behaviors. Incremental extension of this initial path permits increasingly sophisticated representation and processing strategies, and consequently increasingly sophisticated behavior. The initial chapters develop the basic components of the sys tem at the node and network level, with the general goal of efficient category learning and representation. The later chapters are more con cerned with the problems of assembling sequences of actions in order to achieve a given goal state. The model is referred to as connectionistic rather than neural, be cause, while the basic components are neuron-like, there is only limited commitment to physiological realism. Consequently the neuron-like ele ments are referred to as "nodes" rather than "neurons". The model is directed more at the behavioral level, and at that level, numerous con cepts from animal learning theory are directly applicable to connectionis tic modeling. An attempt to actually implement these behavioral theories in a computer simulation can be quite informative, as most are only partially specified, and the gaps may be apparent only when actual ly building a functioning system. In addition, a computer implementa tion provides an improved capability to explore the strengths and limita tions of the different approaches as well as their various interactions.
Book Synopsis Neural Nets Wirn Vietri - 93, Proceedings Of Sixth Italian Workshop by : E R Caianiello
Download or read book Neural Nets Wirn Vietri - 93, Proceedings Of Sixth Italian Workshop written by E R Caianiello and published by World Scientific. This book was released on 1994-04-06 with total page 422 pages. Available in PDF, EPUB and Kindle. Book excerpt: The proceedings of this workshop include invited lectures by Professors Stan Gielen and Lotfi Zadeh. The workshop was the last to be organised by the late Professor Eduardo R. Cainello, known worldwide for his fundamental contributions to cybernetics and theoretical physics, and to whom this volume is dedicated.
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.
Book Synopsis The Emerging Spatial Mind by : Jodie M. Plumert
Download or read book The Emerging Spatial Mind written by Jodie M. Plumert and published by Oxford University Press. This book was released on 2007-04-12 with total page 429 pages. Available in PDF, EPUB and Kindle. Book excerpt: Humans are profoundly influenced by the space around us. This volume sheds light on how our experiences thinking about and interacting in space through time foster and shape the emerging spatial mind.
Book Synopsis Current Catalog by : National Library of Medicine (U.S.)
Download or read book Current Catalog written by National Library of Medicine (U.S.) and published by . This book was released on with total page 1024 pages. Available in PDF, EPUB and Kindle. Book excerpt: First multi-year cumulation covers six years: 1965-70.
Book Synopsis National Library of Medicine Current Catalog by : National Library of Medicine (U.S.)
Download or read book National Library of Medicine Current Catalog written by National Library of Medicine (U.S.) and published by . This book was released on 1990 with total page 1032 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Information Sciences 2007 - Proceedings Of The 10th Joint Conference by : Paul P Wang
Download or read book Information Sciences 2007 - Proceedings Of The 10th Joint Conference written by Paul P Wang and published by World Scientific. This book was released on 2007-07-18 with total page 1709 pages. Available in PDF, EPUB and Kindle. Book excerpt: This proceeding contains the cutting-edge research results in information science and technology, and their related technology. Recent scientific breakthroughs such as invisibility cloak and meta-materials, data mining techniques, advanced game playing in artificial intelligence, nano-technology, unlikely event probability, and fuzzy logic reasoning are just a few outstanding examples. Walter Freeman's 80th birthday celebration is another highlight of this proceedings, because this major event is attended by many leading scientists from around the world. Key speakers include Charles Falco, Water Freeman, Thomas Huang, Meyya Meyyappan, Lotfi Zadeh, Bernette Bouchon Meunier, Heather Carlson, Ling Guan, Etienne Kerre and John Mordes.
Book Synopsis Dynamic Neural Networks for Robot Systems: Data-Driven and Model-Based Applications by : Long Jin
Download or read book Dynamic Neural Networks for Robot Systems: Data-Driven and Model-Based Applications written by Long Jin and published by Frontiers Media SA. This book was released on 2024-07-24 with total page 301 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neural network control has been a research hotspot in academic fields due to the strong ability of computation. One of its wildly applied fields is robotics. In recent years, plenty of researchers have devised different types of dynamic neural network (DNN) to address complex control issues in robotics fields in reality. Redundant manipulators are no doubt indispensable devices in industrial production. There are various works on the redundancy resolution of redundant manipulators in performing a given task with the manipulator model information known. However, it becomes knotty for researchers to precisely control redundant manipulators with unknown model to complete a cyclic-motion generation CMG task, to some extent. It is worthwhile to investigate the data-driven scheme and the corresponding novel dynamic neural network (DNN), which exploits learning and control simultaneously. Therefore, it is of great significance to further research the special control features and solve challenging issues to improve control performance from several perspectives, such as accuracy, robustness, and solving speed.
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.
Book Synopsis Data-Driven Science and Engineering by : Steven L. Brunton
Download or read book Data-Driven Science and Engineering written by Steven L. Brunton and published by Cambridge University Press. This book was released on 2022-05-05 with total page 615 pages. Available in PDF, EPUB and Kindle. Book excerpt: A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®.
Book Synopsis Neural Approaches to Dynamics of Signal Exchanges by : Anna Esposito
Download or read book Neural Approaches to Dynamics of Signal Exchanges written by Anna Esposito and published by Springer Nature. This book was released on 2019-09-18 with total page 525 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book presents research that contributes to the development of intelligent dialog systems to simplify diverse aspects of everyday life, such as medical diagnosis and entertainment. Covering major thematic areas: machine learning and artificial neural networks; algorithms and models; and social and biometric data for applications in human–computer interfaces, it discusses processing of audio-visual signals for the detection of user-perceived states, the latest scientific discoveries in processing verbal (lexicon, syntax, and pragmatics), auditory (voice, intonation, vocal expressions) and visual signals (gestures, body language, facial expressions), as well as algorithms for detecting communication disorders, remote health-status monitoring, sentiment and affect analysis, social behaviors and engagement. Further, it examines neural and machine learning algorithms for the implementation of advanced telecommunication systems, communication with people with special needs, emotion modulation by computer contents, advanced sensors for tracking changes in real-life and automatic systems, as well as the development of advanced human–computer interfaces. The book does not focus on solving a particular problem, but instead describes the results of research that has positive effects in different fields and applications.
