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Neural Foundations
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Book Synopsis Philosophical Foundations of Neuroscience by : M. R. Bennett
Download or read book Philosophical Foundations of Neuroscience written by M. R. Bennett and published by Wiley-Blackwell. This book was released on 2003-04-28 with total page 461 pages. Available in PDF, EPUB and Kindle. Book excerpt: Writing from a scientifically and philosophically informed perspective, the authors provide a critical overview of the conceptual difficulties encountered in many current neuroscientific and psychological theories.
Book Synopsis Foundations of Neuroscience by : Casey Henley
Download or read book Foundations of Neuroscience written by Casey Henley and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering by : Nikola K. Kasabov
Download or read book Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering written by Nikola K. Kasabov and published by Marcel Alencar. This book was released on 1996 with total page 581 pages. Available in PDF, EPUB and Kindle. Book excerpt: Combines the study of neural networks and fuzzy systems with symbolic artificial intelligence (AI) methods to build comprehensive AI systems. Describes major AI problems (pattern recognition, speech recognition, prediction, decision-making, game-playing) and provides illustrative examples. Includes applications in engineering, business and finance.
Book Synopsis Unsupervised Learning by : Geoffrey Hinton
Download or read book Unsupervised Learning written by Geoffrey Hinton and published by MIT Press. This book was released on 1999-05-24 with total page 420 pages. Available in PDF, EPUB and Kindle. Book excerpt: Since its founding in 1989 by Terrence Sejnowski, Neural Computation has become the leading journal in the field. Foundations of Neural Computation collects, by topic, the most significant papers that have appeared in the journal over the past nine years. This volume of Foundations of Neural Computation, on unsupervised learning algorithms, focuses on neural network learning algorithms that do not require an explicit teacher. The goal of unsupervised learning is to extract an efficient internal representation of the statistical structure implicit in the inputs. These algorithms provide insights into the development of the cerebral cortex and implicit learning in humans. They are also of interest to engineers working in areas such as computer vision and speech recognition who seek efficient representations of raw input data.
Book Synopsis Foundations of Neural Development by : S Marc Breedlove
Download or read book Foundations of Neural Development written by S Marc Breedlove and published by Sinauer. This book was released on 2017-03-08 with total page 380 pages. Available in PDF, EPUB and Kindle. Book excerpt: Foundations of Neural Development is an accessible textbook, written with a conversational style and topics appropriate for an undergraduate audience. Each chapter begins with a thought-provoking vignette, or a real-life story, that the subsequent material illuminates. The “Researchers at Work” feature, available in every chapter, describes a classic study in detail, taking the reader through the hypothesis, test, result, and conclusion of an experiment. Other features include a marginal glossary, review questions, and bulleted summary in each chapter. Chapters 1–7 unfold in the order of ontogeny, covering induction, the establishment of a body plan, neural migration, differentiation, axonal pathfinding, synapse formation, and apoptosis. Chapters 8–10 address activity-guided, experience-guided, and socially guided neural development—mechanisms that were crucial for the evolution of the human brain. Lively and engaging, with the finest illustrations, this is the perfect book to help any undergraduate student understand how a single microscopic cell, a human zygote, can develop into the most complex machine on earth, the brain./div
Book Synopsis Graph Neural Networks: Foundations, Frontiers, and Applications by : Lingfei Wu
Download or read book Graph Neural Networks: Foundations, Frontiers, and Applications written by Lingfei Wu and published by Springer Nature. This book was released on 2022-01-03 with total page 701 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep Learning models are at the core of artificial intelligence research today. It is well known that deep learning techniques are disruptive for Euclidean data, such as images or sequence data, and not immediately applicable to graph-structured data such as text. This gap has driven a wave of research for deep learning on graphs, including graph representation learning, graph generation, and graph classification. The new neural network architectures on graph-structured data (graph neural networks, GNNs in short) have performed remarkably on these tasks, demonstrated by applications in social networks, bioinformatics, and medical informatics. Despite these successes, GNNs still face many challenges ranging from the foundational methodologies to the theoretical understandings of the power of the graph representation learning. This book provides a comprehensive introduction of GNNs. It first discusses the goals of graph representation learning and then reviews the history, current developments, and future directions of GNNs. The second part presents and reviews fundamental methods and theories concerning GNNs while the third part describes various frontiers that are built on the GNNs. The book concludes with an overview of recent developments in a number of applications using GNNs. This book is suitable for a wide audience including undergraduate and graduate students, postdoctoral researchers, professors and lecturers, as well as industrial and government practitioners who are new to this area or who already have some basic background but want to learn more about advanced and promising techniques and applications.
Book Synopsis Mathematical Foundations of Neuroscience by : G. Bard Ermentrout
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.
Book Synopsis Neural Network Learning by : Martin Anthony
Download or read book Neural Network Learning written by Martin Anthony and published by Cambridge University Press. This book was released on 1999-11-04 with total page 405 pages. Available in PDF, EPUB and Kindle. Book excerpt: This work explores probabilistic models of supervised learning problems and addresses the key statistical and computational questions. Chapters survey research on pattern classification with binary-output networks, including a discussion of the relevance of the Vapnik Chervonenkis dimension, and of estimates of the dimension for several neural network models. In addition, the authors develop a model of classification by real-output networks, and demonstrate the usefulness of classification...
Book Synopsis Affective Neuroscience by : Jaak Panksepp
Download or read book Affective Neuroscience written by Jaak Panksepp and published by Oxford University Press. This book was released on 2004-09-30 with total page 481 pages. Available in PDF, EPUB and Kindle. Book excerpt: Some investigators have argued that emotions, especially animal emotions, are illusory concepts outside the realm of scientific inquiry. However, with advances in neurobiology and neuroscience, researchers are demonstrating that this position is wrong as they move closer to a lasting understanding of the biology and psychology of emotion. In Affective Neuroscience, Jaak Panksepp provides the most up-to-date information about the brain-operating systems that organize the fundamental emotional tendencies of all mammals. Presenting complex material in a readable manner, the book offers a comprehensive summary of the fundamental neural sources of human and animal feelings, as well as a conceptual framework for studying emotional systems of the brain. Panksepp approaches emotions from the perspective of basic emotion theory but does not fail to address the complex issues raised by constructionist approaches. These issues include relations to human consciousness and the psychiatric implications of this knowledge. The book includes chapters on sleep and arousal, pleasure and fear systems, the sources of rage and anger, and the neural control of sexuality, as well as the more subtle emotions related to maternal care, social loss, and playfulness. Representing a synthetic integration of vast amounts of neurobehavioral knowledge, including relevant neuroanatomy, neurophysiology, and neurochemistry, this book will be one of the most important contributions to understanding the biology of emotions since Darwins The Expression of the Emotions in Man and Animals
Book Synopsis Biophysical Neural Networks by : Roman R. Poznanski
Download or read book Biophysical Neural Networks written by Roman R. Poznanski and published by . This book was released on 2001 with total page 522 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Philosophical Foundations of Neuroscience by : M. R. Bennett
Download or read book Philosophical Foundations of Neuroscience written by M. R. Bennett and published by John Wiley & Sons. This book was released on 2022-03-14 with total page 564 pages. Available in PDF, EPUB and Kindle. Book excerpt: The second edition of the seminal work in the field—revised, updated, and extended In Philosophical Foundations of Neuroscience, M.R. Bennett and P.M.S. Hacker outline and address the conceptual confusions encountered in various neuroscientific and psychological theories. The result of a collaboration between an esteemed philosopher and a distinguished neuroscientist, this remarkable volume presents an interdisciplinary critique of many of the neuroscientific and psychological foundations of modern cognitive neuroscience. The authors point out conceptual entanglements in a broad range of major neuroscientific and psychological theories—including those of such neuroscientists as Blakemore, Crick, Damasio, Dehaene, Edelman, Gazzaniga, Kandel, Kosslyn, LeDoux, Libet, Penrose, Posner, Raichle and Tononi, as well as psychologists such as Baar, Frith, Glynn, Gregory, William James, Weiskrantz, and biologists such as Dawkins, Humphreys, and Young. Confusions arising from the work of philosophers such as Dennett, Chalmers, Churchland, Nagel and Searle are subjected to detailed criticism. These criticisms are complemented by constructive analyses of the major cognitive, cogitative, emotional and volitional attributes that lie at the heart of cognitive neuroscientific research. Now in its second edition, this groundbreaking work has been exhaustively revised and updated to address current issues and critiques. New discussions offer insight into functional magnetic resonance imaging (fMRI), the notions of information and representation, conflict monitoring and the executive, minimal states of consciousness, integrated information theory and global workspace theory. The authors also reply to criticisms of the fundamental arguments posed in the first edition, defending their conclusions regarding mereological fallacy, the necessity of distinguishing between empirical and conceptual questions, the mind-body problem, and more. Essential as both a comprehensive reference work and as an up-to-date critical review of cognitive neuroscience, this landmark volume: Provides a scientifically and philosophically informed survey of the conceptual problems in a wide variety of neuroscientific theories Offers a clear and accessible presentation of the subject, minimizing the use of complex philosophical and scientific jargon Discusses how the ways the brain relates to the mind affect the intelligibility of neuroscientific research Includes fresh insights on mind-body and mind-brain relations, and on the relation between the notion of person and human being Features more than 100 new pages and a wealth of additional diagrams, charts, and tables Continuing to challenge and educate readers like no other book on the subject, the second edition of Philosophical Foundations of Neuroscience is required reading not only for neuroscientists, psychologists, and philosophers, but also for academics, researchers, and students involved in the study of the mind and consciousness.
Book Synopsis Single Neuron Computation by : Thomas M. McKenna
Download or read book Single Neuron Computation written by Thomas M. McKenna and published by Academic Press. This book was released on 2014-05-19 with total page 644 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book contains twenty-two original contributions that provide a comprehensive overview of computational approaches to understanding a single neuron structure. The focus on cellular-level processes is twofold. From a computational neuroscience perspective, a thorough understanding of the information processing performed by single neurons leads to an understanding of circuit- and systems-level activity. From the standpoint of artificial neural networks (ANNs), a single real neuron is as complex an operational unit as an entire ANN, and formalizing the complex computations performed by real neurons is essential to the design of enhanced processor elements for use in the next generation of ANNs. The book covers computation in dendrites and spines, computational aspects of ion channels, synapses, patterned discharge and multistate neurons, and stochastic models of neuron dynamics. It is the most up-to-date presentation of biophysical and computational methods.
Author :Artur S. d'Avila Garcez Publisher :Springer Science & Business Media ISBN 13 :1447102118 Total Pages :276 pages Book Rating :4.4/5 (471 download)
Book Synopsis Neural-Symbolic Learning Systems by : Artur S. d'Avila Garcez
Download or read book Neural-Symbolic Learning Systems written by Artur S. d'Avila Garcez and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial Intelligence is concerned with producing devices that help or replace human beings in their daily activities. Neural-symbolic learning systems play a central role in this task by combining, and trying to benefit from, the advantages of both the neural and symbolic paradigms of artificial intelligence. This book provides a comprehensive introduction to the field of neural-symbolic learning systems, and an invaluable overview of the latest research issues in this area. It is divided into three sections, covering the main topics of neural-symbolic integration - theoretical advances in knowledge representation and learning, knowledge extraction from trained neural networks, and inconsistency handling in neural-symbolic systems. Each section provides a balance of theory and practice, giving the results of applications using real-world problems in areas such as DNA sequence analysis, power systems fault diagnosis, and software requirements specifications. Neural-Symbolic Learning Systems will be invaluable reading for researchers and graduate students in Engineering, Computing Science, Artificial Intelligence, Machine Learning and Neurocomputing. It will also be of interest to Intelligent Systems practitioners and anyone interested in applications of hybrid artificial intelligence systems.
Book Synopsis Social Behavior from Rodents to Humans by : Markus Wöhr
Download or read book Social Behavior from Rodents to Humans written by Markus Wöhr and published by Springer. This book was released on 2017-01-31 with total page 429 pages. Available in PDF, EPUB and Kindle. Book excerpt: This compelling volume provides a broad and accessible overview on the rapidly developing field of social neuroscience. A major goal of the volume is to integrate research findings on the neural basis of social behavior across different levels of analysis from rodent studies on molecular neurobiology to behavioral neuroscience to fMRI imaging data on human social behavior.
Book Synopsis Neurobiological Foundations for EMDR Practice by : Uri Bergmann, PhD
Download or read book Neurobiological Foundations for EMDR Practice written by Uri Bergmann, PhD and published by Springer Publishing Company. This book was released on 2012-05-21 with total page 248 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume introduces the most current research about the neural underpinnings of consciousness and EMDR (eye movement desensitization and reprocessing) in regard to attachment, traumatic stress, and dissociation. It is the first book to comprehensively integrate new findings in information processing, consciousness, traumatic disorders of information processing, chronic trauma and autoimmune compromises, and the implications of these data on the Adaptive Information Processing (AIP) model and EMDR treatment The text examines online/wakeful information processing, including sensation, perception, somatosensory integration, cognition, memory, language and motricity, and off-line/sleep information processing, such as slow wave sleep and cognitive memorial processing, as well as REM/dream sleep and its function in emotional memory processing. The volume also addresses disorders of consciousness, including coma, anesthesia, and other neurological disorders, particularly disorders of Type 1 PTSD, complex PTSD/dissociative disorders, and personality disorders. It delves into chronic trauma and autoimmune function, especially in regard to diseases of unknown origin, and examines them from the perspective of autoimmune compromises resulting from the unusual neuroendocrine profile of PTSD sufferers. The final section integrates all material to illustrate the tenets of the AIP model and the implication of this material with respect to current EMDR treatment, as well as techniques to render it more robust Key Features: Provides a neurobiological foundation that informs our understanding of human development, disorders of attachment, and information processing Examines biological underpinnings of EMDR and other psychotherapeutic modalities regarding successful treatment outcomes for attachment, stress, and dissociation Offers the latest research in neurosciences relevant to attachment, traumatic stress, and dissociation Explicates disorders as outcomes of chronically dysregulated, evolutionarily based, biological action systems Illustrates EMDR's sensorial input to the brain as a neural catalyst that can facilitate repair of dysfunctional neural circuitry Includes illustrative neural maps
Book Synopsis Foundations of Cognitive Psychology by : Daniel J. Levitin
Download or read book Foundations of Cognitive Psychology written by Daniel J. Levitin and published by MIT Press. This book was released on 2002 with total page 884 pages. Available in PDF, EPUB and Kindle. Book excerpt: An anthology of core readings on cognitive psychology.
Download or read book Deep Learning written by Josh Patterson and published by "O'Reilly Media, Inc.". This book was released on 2017-07-28 with total page 532 pages. Available in PDF, EPUB and Kindle. Book excerpt: Although interest in machine learning has reached a high point, lofty expectations often scuttle projects before they get very far. How can machine learning—especially deep neural networks—make a real difference in your organization? This hands-on guide not only provides the most practical information available on the subject, but also helps you get started building efficient deep learning networks. Authors Adam Gibson and Josh Patterson provide theory on deep learning before introducing their open-source Deeplearning4j (DL4J) library for developing production-class workflows. Through real-world examples, you’ll learn methods and strategies for training deep network architectures and running deep learning workflows on Spark and Hadoop with DL4J. Dive into machine learning concepts in general, as well as deep learning in particular Understand how deep networks evolved from neural network fundamentals Explore the major deep network architectures, including Convolutional and Recurrent Learn how to map specific deep networks to the right problem Walk through the fundamentals of tuning general neural networks and specific deep network architectures Use vectorization techniques for different data types with DataVec, DL4J’s workflow tool Learn how to use DL4J natively on Spark and Hadoop
