Oscillatory Neural Networks

Oscillatory Neural Networks Author Margarita G. Kuzmina
ISBN-10 9783110269208
Release 2014-01-01
Pages 172
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Neural-like oscillatory network models allow to elucidate the possibilities of dynamical, synchronization-based types of image processing exploited by the brain. The oscillatory network capabilities, studied by means of computer modeling and qualitative analysis, are presented and discussed in this book, as well as several other problems of parallel distributed information processing.



Design of Oscillatory Neural Network for Locomotion Control of Humanoid Robots

Design of Oscillatory Neural Network for Locomotion Control of Humanoid Robots Author Riadh Zaier
ISBN-10 9533079517
Release 2012
Pages
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Design of Oscillatory Neural Network for Locomotion Control of Humanoid Robots has been writing in one form or another for most of life. You can find so many inspiration from Design of Oscillatory Neural Network for Locomotion Control of Humanoid Robots also informative, and entertaining. Click DOWNLOAD or Read Online button to get full Design of Oscillatory Neural Network for Locomotion Control of Humanoid Robots book for free.



Artificial Neural Networks ICANN 96

Artificial Neural Networks   ICANN 96 Author Christoph von der Malsburg
ISBN-10 3540615105
Release 1996-07-10
Pages 922
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This book constitutes the refereed proceedings of the sixth International Conference on Artificial Neural Networks - ICANN 96, held in Bochum, Germany in July 1996. The 145 papers included were carefully selected from numerous submissions on the basis of at least three reviews; also included are abstracts of the six invited plenary talks. All in all, the set of papers presented reflects the state of the art in the field of ANNs. Among the topics and areas covered are a broad spectrum of theoretical aspects, applications in various fields, sensory processing, cognitive science and AI, implementations, and neurobiology.



Foundations of Neural Networks Fuzzy Systems and Knowledge Engineering

Foundations of Neural Networks  Fuzzy Systems  and Knowledge Engineering Author Nikola K. Kasabov
ISBN-10 9780262112123
Release 1996
Pages 550
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Neural networks and fuzzy systems are different approaches to introducing human-like reasoning into expert systems. This text is the first to combine the study of these two subjects, their basics and their use, along with symbolic AI methods to build comprehensive artificial intelligence systems. In a clear and accessible style, Kasabov describes rule- based and connectionist techniques and then their combinations, with fuzzy logic included, showing the application of the different techniques to a set of simple prototype problems, which makes comparisons possible. A particularly strong feature of the text is that it is filled with applications in engineering, business, and finance. AI problems that cover most of the application-oriented research in the field (pattern recognition, speech and image processing, classification, planning, optimization, prediction, control, decision making, and game simulations) are discussed and illustrated with concrete examples. Intended both as a text for advanced undergraduate and postgraduate students as well as a reference for researchers in the field of knowledge engineering, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering has chapters structured for various levels of teaching and includes original work by the author along with the classic material. Data sets for the examples in the book as well as an integrated software environment that can be used to solve the problems and do the exercises at the end of each chapter are available free through anonymous ftp.



The Handbook of Brain Theory and Neural Networks

The Handbook of Brain Theory and Neural Networks Author Michael A. Arbib
ISBN-10 9780262011976
Release 2003
Pages 1290
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A new, dramatically updated edition of the classic resource on the constantly evolving fields of brain theory and neural networks.



Weakly Connected Neural Networks

Weakly Connected Neural Networks Author Frank C. Hoppensteadt
ISBN-10 9781461218289
Release 2012-12-06
Pages 402
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Devoted to local and global analysis of weakly connected systems with applications to neurosciences, this book uses bifurcation theory and canonical models as the major tools of analysis. It presents a systematic and well motivated development of both weakly connected system theory and mathematical neuroscience, addressing bifurcations in neuron and brain dynamics, synaptic organisations of the brain, and the nature of neural codes. The authors present classical results together with the most recent developments in the field, making this a useful reference for researchers and graduate students in various branches of mathematical neuroscience.



Neural Networks

Neural Networks Author D J Amit
ISBN-10 9789814548403
Release 1995-10-18
Pages 308
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The papers appearing in this proceedings volume cover a broad range of subjects, owing to the highly cross-disciplinary character of the workshop, and include: experiments and models concerning the dynamics of the neural activity in the cortex (DMS experiments, attractor dynamics in the cortex, spontaneous activity…); hippocampus, space and memory; theoretical advances in neural network modeling; information processing in neural networks; applications of neural networks to experimental physics, particularly to high energy physics; digital and analog hardware implementations of neural networks; etc.



Artificial Neural Networks ICANN 2008

Artificial Neural Networks   ICANN 2008 Author Vera Kurkova-Pohlova
ISBN-10 9783540875598
Release 2008-08-29
Pages 986
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This two volume set LNCS 5163 and LNCS 5164 constitutes the refereed proceedings of the 18th International Conference on Artificial Neural Networks, ICANN 2008, held in Prague Czech Republic, in September 2008. The 200 revised full papers presented were carefully reviewed and selected from more than 300 submissions. The second volume is devoted to pattern recognition and data analysis, hardware and embedded systems, computational neuroscience, connectionistic cognitive science, neuroinformatics and neural dynamics. it also contains papers from two special sessions coupling, synchronies, and firing patterns: from cognition to disease, and constructive neural networks and two workshops new trends in self-organization and optimization of artificial neural networks, and adaptive mechanisms of the perception-action cycle.



The Relevance of the Time Domain to Neural Network Models

The Relevance of the Time Domain to Neural Network Models Author A. Ravishankar Rao
ISBN-10 1461407249
Release 2011-09-18
Pages 226
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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



Oscillations in Neural Systems

Oscillations in Neural Systems Author Daniel S. Levine
ISBN-10 9781135691905
Release 1999-09-01
Pages 456
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This book is the fourth in a series based on conferences sponsored by the Metroplex Institute for Neural Dynamics (MIND), an interdisciplinary organization of Dallas-Fort Worth area neural network professionals in both academia and industry. This topic was chosen as the focus for this special issue because of the increasing interest by neuroscientists and psychologists in both rhythmic and chaotic activity patterns observed in the nervous system. Neither the mathematical structure of neural oscillations nor their functional significance is precisely understood. There are a great many open problems in both the structure and function of neural oscillations, whether rhythmic, chaotic, or a combination of the two, and many of these problems are dealt with in the chapters of this book.



Models of Neural Networks

Models of Neural Networks Author Eytan Domany
ISBN-10 9781461243205
Release 2013-11-11
Pages 347
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Since the appearance of Vol. 1 of Models of Neural Networks in 1991, the theory of neural nets has focused on two paradigms: information coding through coherent firing of the neurons and functional feedback. Information coding through coherent neuronal firing exploits time as a cardinal degree of freedom. This capacity of a neural network rests on the fact that the neuronal action potential is a short, say 1 ms, spike, localized in space and time. Spatial as well as temporal correlations of activity may represent different states of a network. In particular, temporal correlations of activity may express that neurons process the same "object" of, for example, a visual scene by spiking at the very same time. The traditional description of a neural network through a firing rate, the famous S-shaped curve, presupposes a wide time window of, say, at least 100 ms. It thus fails to exploit the capacity to "bind" sets of coherently firing neurons for the purpose of both scene segmentation and figure-ground segregation. Feedback is a dominant feature of the structural organization of the brain. Recurrent neural networks have been studied extensively in the physical literature, starting with the ground breaking work of John Hop field (1982).



Mathematical Methods for Neural Network Analysis and Design

Mathematical Methods for Neural Network Analysis and Design Author Richard M. Golden
ISBN-10 0262071746
Release 1996
Pages 419
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This graduate-level text teaches students how to use a small number of powerful mathematical tools for analyzing and designing a wide variety of artificial neural network (ANN) systems, including their own customized neural networks. Mathematical Methods for Neural Network Analysis and Design offers an original, broad, and integrated approach that explains each tool in a manner that is independent of specific ANN systems. Although most of the methods presented are familiar, their systematic application to neural networks is new. Included are helpful chapter summaries and detailed solutions to over 100 ANN system analysis and design problems. For convenience, many of the proofs of the key theorems have been rewritten so that the entire book uses a relatively uniform notion. This text is unique in several ways. It is organized according to categories of mathematical tools—for investigating the behavior of an ANN system, for comparing (and improving) the efficiency of system computations, and for evaluating its computational goals— that correspond respectively to David Marr's implementational, algorithmic, and computational levels of description. And instead of devoting separate chapters to different types of ANN systems, it analyzes the same group of ANN systems from the perspective of different mathematical methodologies. A Bradford Book



Neural Networks for Instrumentation Measurement and Related Industrial Applications

Neural Networks for Instrumentation  Measurement and Related Industrial Applications Author Sergey Ablameyko
ISBN-10 1586033034
Release 2003
Pages 329
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This work aims to disseminate theoretical and practical knowledge about neural networks in measurement, instrumentation and the related industrial applications. It also creates a consciousness about the effectiveness of these techniques as well as the measurement problems in industrial environments



Emerging Nanoelectronic Devices

Emerging Nanoelectronic Devices Author An Chen
ISBN-10 9781118958278
Release 2014-11-26
Pages 576
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Emerging Nanoelectronic Devices focuses on the future direction of semiconductor and emerging nanoscale device technology. As the dimensional scaling of CMOS approaches its limits, alternate information processing devices and microarchitectures are being explored to sustain increasing functionality at decreasing cost into the indefinite future. This is driving new paradigms of information processing enabled by innovative new devices, circuits, and architectures, necessary to support an increasingly interconnected world through a rapidly evolving internet. This original title provides a fresh perspective on emerging research devices in 26 up to date chapters written by the leading researchers in their respective areas. It supplements and extends the work performed by the Emerging Research Devices working group of the International Technology Roadmap for Semiconductors (ITRS). Key features: • Serves as an authoritative tutorial on innovative devices and architectures that populate the dynamic world of “Beyond CMOS” technologies. • Provides a realistic assessment of the strengths, weaknesses and key unknowns associated with each technology. • Suggests guidelines for the directions of future development of each technology. • Emphasizes physical concepts over mathematical development. • Provides an essential resource for students, researchers and practicing engineers.



Emergent Neural Computational Architectures Based on Neuroscience

Emergent Neural Computational Architectures Based on Neuroscience Author Stefan Wermter
ISBN-10 9783540445975
Release 2003-05-15
Pages 582
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It is generally understood that the present approachs to computing do not have the performance, flexibility, and reliability of biological information processing systems. Although there is a comprehensive body of knowledge regarding how information processing occurs in the brain and central nervous system this has had little impact on mainstream computing so far. This book presents a broad spectrum of current research into biologically inspired computational systems and thus contributes towards developing new computational approaches based on neuroscience. The 39 revised full papers by leading researchers were carefully selected and reviewed for inclusion in this anthology. Besides an introductory overview by the volume editors, the book offers topical parts on modular organization and robustness, timing and synchronization, and learning and memory storage.



Engineering Applications of Bio Inspired Artificial Neural Networks

Engineering Applications of Bio Inspired Artificial Neural Networks Author Jose Mira
ISBN-10 3540660682
Release 1999-05-19
Pages 912
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This book constitutes, together with its compagnion LNCS 1606, the refereed proceedings of the International Work-Conference on Artificial and Neural Networks, IWANN'99, held in Alicante, Spain in June 1999. The 91 revised papers presented were carefully reviewed and selected for inclusion in the book. This volume is devoted to applications of biologically inspired artificial neural networks in various engineering disciplines. The papers are organized in parts on artificial neural nets simulation and implementation, image processing, and engineering applications.



Progress in Pattern Recognition Speech and Image Analysis

Progress in Pattern Recognition  Speech and Image Analysis Author Alberto Sanfeliu
ISBN-10 9783540205906
Release 2003-11-18
Pages 693
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This book constitutes the refereed proceedings of the 8th Iberoamerican Congress on Pattern Recognition, CIARP 2003, held in Havana, Cuba, in November 2003. The 82 revised full papers presented together with two invited papers were carefully reviewed and selected from 140 submissions. All current issues in pattern recognition, image processing, and computer vision are addressed as well as applications in domains like robotics, health, entertainment, space exploration, telecommunications, speech processing, data analysis, document recognition, etc.