Download or read book Dynamics of Continuous Attractor Neural Networks written by Chi Chung Fung and published by . This book was released on 2010 with total page 85 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Dynamics of Continuous Attractor Neural Networks with Dynamical Synapses written by Chi Chung Fung and published by . This book was released on 2013 with total page 114 pages. Available in PDF, EPUB and Kindle. Book excerpt:
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 Neural Information Processing written by Derong Liu and published by Springer. This book was released on 2017-11-07 with total page 911 pages. Available in PDF, EPUB and Kindle. Book excerpt: The six volume set LNCS 10634, LNCS 10635, LNCS 10636, LNCS 10637, LNCS 10638, and LNCS 10639 constitues the proceedings of the 24rd International Conference on Neural Information Processing, ICONIP 2017, held in Guangzhou, China, in November 2017. The 563 full papers presented were carefully reviewed and selected from 856 submissions. The 6 volumes are organized in topical sections on Machine Learning, Reinforcement Learning, Big Data Analysis, Deep Learning, Brain-Computer Interface, Computational Finance, Computer Vision, Neurodynamics, Sensory Perception and Decision Making, Computational Intelligence, Neural Data Analysis, Biomedical Engineering, Emotion and Bayesian Networks, Data Mining, Time-Series Analysis, Social Networks, Bioinformatics, Information Security and Social Cognition, Robotics and Control, Pattern Recognition, Neuromorphic Hardware and Speech Processing.
Download or read book Convergence Analysis of Recurrent Neural Networks written by Zhang Yi and published by Springer Science & Business Media. This book was released on 2013-11-11 with total page 244 pages. Available in PDF, EPUB and Kindle. Book excerpt: Since the outstanding and pioneering research work of Hopfield on recurrent neural networks (RNNs) in the early 80s of the last century, neural networks have rekindled strong interests in scientists and researchers. Recent years have recorded a remarkable advance in research and development work on RNNs, both in theoretical research as weIl as actual applications. The field of RNNs is now transforming into a complete and independent subject. From theory to application, from software to hardware, new and exciting results are emerging day after day, reflecting the keen interest RNNs have instilled in everyone, from researchers to practitioners. RNNs contain feedback connections among the neurons, a phenomenon which has led rather naturally to RNNs being regarded as dynamical systems. RNNs can be described by continuous time differential systems, discrete time systems, or functional differential systems, and more generally, in terms of non linear systems. Thus, RNNs have to their disposal, a huge set of mathematical tools relating to dynamical system theory which has tumed out to be very useful in enabling a rigorous analysis of RNNs.
Download or read book From Conditioning to Conscious Recollection written by Howard Eichenbaum and published by Oxford University Press. This book was released on 2004-11-18 with total page 594 pages. Available in PDF, EPUB and Kindle. Book excerpt: This cutting-edge book offers a theoretical account of the evolution of multiple memory systems of the brain. The authors conceptualize these memory systems from both behavioral and neurobiological perspectives, guided by three related principles. First, that our understanding of a wide range of memory phenomena can be advanced by breaking down memory into multiple forms with different operating characteristics. Second, that different forms of memory representation are supported by distinct brain pathways with circuitry and neural coding properties. Third, that the contributions of different brain systems can be compared and contrasted by distinguishing between dedicated (or specific) and elaborate (or general) memory systems. A primary goal of this work is to relate the neurobiological properties of dedicated and elaborate systems to their neuropsychological counterparts, and in so doing, account for the phenomenology of memory, from conditioning to conscious recollection.
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 Neurodynamics of Cognition and Consciousness written by Leonid I. Perlovsky and published by Springer. This book was released on 2007-08-26 with total page 369 pages. Available in PDF, EPUB and Kindle. Book excerpt: Experimental evidence in humans and other mammalians indicates that complex neurodynamics is crucial for the emergence of higher-level intelligence. Dynamical neural systems with encoding in limit cycle and non-convergent attractors have gained increasing popularity in the past decade. The role of synchronization, desynchronization, and intermittent synchronization on cognition has been studied extensively by various authors, in particular by authors contributing to the present volume. This book addresses dynamical aspects of brain functions and cognition.
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.
Download or read book Attractor Networks written by Fouad Sabry and published by One Billion Knowledgeable. This book was released on 2023-06-20 with total page 136 pages. Available in PDF, EPUB and Kindle. Book excerpt: What Is Attractor Networks A sort of recurrent dynamical network known as an attractor network is one that gradually settles into a consistent pattern over the course of time. The nodes that make up the attractor network gradually move in the direction of a pattern, which can be either fixed-point, cyclic, chaotic, or random (stochastic). In the field of computational neuroscience, attractor networks have been extensively utilized to mimic neural processes including associative memory and motor behavior. Additionally, these networks have been utilized in biologically inspired machine learning techniques.An attractor network is made up of a collection of n nodes, each of which can be interpreted as a vector in a space of d dimensions, with n being more than d. Over the course of time, the state of the network will eventually gravitate toward one of a set of predetermined states located on a d-manifold. These states are known as the attractors. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Attractor network Chapter 2: Artificial neural network Chapter 3: Hebbian theory Chapter 4: Hopfield network Chapter 5: Recurrent neural network Chapter 6: Autoassociative memory Chapter 7: Bidirectional associative memory Chapter 8: Competitive learning Chapter 9: Types of artificial neural networks Chapter 10: Dynamical neuroscience (II) Answering the public top questions about attractor networks. (III) Real world examples for the usage of attractor networks in many fields. 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 attractor networks. What Is Artificial Intelligence Series The Artificial Intelligence book series provides comprehensive coverage in over 200 topics. Each ebook covers a specific Artificial Intelligence topic in depth, written by experts in the field. The series aims to give readers a thorough understanding of the concepts, techniques, history and applications of artificial intelligence. Topics covered include machine learning, deep learning, neural networks, computer vision, natural language processing, robotics, ethics and more. The ebooks are written for professionals, students, and anyone interested in learning about the latest developments in this rapidly advancing field. The artificial intelligence book series provides an in-depth yet accessible exploration, from the fundamental concepts to the state-of-the-art research. With over 200 volumes, readers gain a thorough grounding in all aspects of Artificial Intelligence. The ebooks are designed to build knowledge systematically, with later volumes building on the foundations laid by earlier ones. This comprehensive series is an indispensable resource for anyone seeking to develop expertise in artificial intelligence.
Download or read book Computational Neuroscience written by Jianfeng Feng and published by CRC Press. This book was released on 2003-10-20 with total page 656 pages. Available in PDF, EPUB and Kindle. Book excerpt: How does the brain work? After a century of research, we still lack a coherent view of how neurons process signals and control our activities. But as the field of computational neuroscience continues to evolve, we find that it provides a theoretical foundation and a set of technological approaches that can significantly enhance our understanding.
Download or read book Cognitive Changes and the Aging Brain written by Kenneth M. Heilman and published by Cambridge University Press. This book was released on 2019-12-05 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes the changes in the brain and in cognitive functions that occur with aging in the absence of a neurological, psychiatric, or medical disease. It discusses aging-related changes in many brain functions, including memory, language, sensory perception, motor function, creativity, attention, executive functions, emotions and mood. The neural mechanisms that may account for specific aging-related changes in cognition, perception and behavior are explored, as well as the means by which aging-related cognitive decrements can be managed and possibly ameliorated. Consequently, this book will be of value to clinicians, including neurologists, psychiatrists, geriatricians, primary care physicians, psychologists and speech-language pathologists. In addition, researchers and graduate students who want to learn about the aging brain will find this an indispensable guide.
Download or read book Mathematical Foundations of Neuroscience written by G. Bard Ermentrout and published by Springer Science & Business Media. This book was released on 2010-07-01 with total page 434 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book applies methods from nonlinear dynamics to problems in neuroscience. It uses modern mathematical approaches to understand patterns of neuronal activity seen in experiments and models of neuronal behavior. The intended audience is researchers interested in applying mathematics to important problems in neuroscience, and neuroscientists who would like to understand how to create models, as well as the mathematical and computational methods for analyzing them. The authors take a very broad approach and use many different methods to solve and understand complex models of neurons and circuits. They explain and combine numerical, analytical, dynamical systems and perturbation methods to produce a modern approach to the types of model equations that arise in neuroscience. There are extensive chapters on the role of noise, multiple time scales and spatial interactions in generating complex activity patterns found in experiments. The early chapters require little more than basic calculus and some elementary differential equations and can form the core of a computational neuroscience course. Later chapters can be used as a basis for a graduate class and as a source for current research in mathematical neuroscience. The book contains a large number of illustrations, chapter summaries and hundreds of exercises which are motivated by issues that arise in biology, and involve both computation and analysis. Bard Ermentrout is Professor of Computational Biology and Professor of Mathematics at the University of Pittsburgh. David Terman is Professor of Mathematics at the Ohio State University.
Download or read book Neural Engineering written by Chris Eliasmith and published by MIT Press. This book was released on 2003 with total page 384 pages. Available in PDF, EPUB and Kindle. Book excerpt: A synthesis of current approaches to adapting engineering tools to the study of neurobiological systems.
Download or read book Neural Information Processing with Dynamical Synapses written by Si Wu and published by Frontiers E-books. This book was released on 2015-01-08 with total page 179 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book An Introduction to Neural Information Processing written by Peiji Liang and published by Springer. This book was released on 2015-12-22 with total page 338 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides an overview of neural information processing research, which is one of the most important branches of neuroscience today. Neural information processing is an interdisciplinary subject, and the merging interaction between neuroscience and mathematics, physics, as well as information science plays a key role in the development of this field. This book begins with the anatomy of the central nervous system, followed by an introduction to various information processing models at different levels. The authors all have extensive experience in mathematics, physics and biomedical engineering, and have worked in this multidisciplinary area for a number of years. They present classical examples of how the pioneers in this field used theoretical analysis, mathematical modeling and computer simulation to solve neurobiological problems, and share their experiences and lessons learned. The book is intended for researchers and students with a mathematics, physics or informatics background who are interested in brain research and keen to understand the necessary neurobiology and how they can use their specialties to address neurobiological problems. It is also provides inspiration for neuroscience students who are interested in learning how to use mathematics, physics or informatics approaches to solve problems in their field.
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"--
