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Author: Daniel Gardner Publisher: MIT Press ISBN: 9780262071505 Category : Neural circuitry Languages : en Pages : 254
Book Description
This timely overview and synthesis of recent work in both artificial neural networks and neurobiology seeks to examine neurobiological data from a network perspective and to encourage neuroscientists to participate in constructing the next generation of neural networks.
Author: Daniel Gardner Publisher: Bradford Book ISBN: 9780262517126 Category : Neural circuitry Languages : en Pages : 0
Book Description
This timely overview and synthesis of recent work in both artificial neural networks and neurobiology seeks to examine neurobiological data from a network perspective and to encourage neuroscientists to participate in constructing the next generation of neural networks. Individual chapters were commissioned from selected authors to bridge the gap between present neural network models and the needs of neurophysiologists who are trying to use these models as part of their research on how the brain works.Daniel Gardner is Professor of Physiology and Biophysics at Cornell University Medical College.Contents: Introduction: Toward Neural Neural Networks, Daniel Gardner. Two Principles of Brain Organization: A Challenge for Artificial Neural Networks, Charles F. Stevens. Static Determinants of Synaptic Strength, Daniel Gardner. Learning Rules From Neurobiology, Douglas A. Baxter and John H. Byrne. Realistic Network Models of Distributed Processing in the Leech, Shawn R. Lockery and Terrence J. Sejnowski. Neural and Peripheral Dynamics as Determinants of Patterned Motor Behavior, Hillel J. Chiel and Randall D. Beer. Dynamic Neural Network Models of Sensorimotor Behavior, Eberhard E. Fetz.
Author: Daniel Gardner Publisher: ISBN: 9780262290876 Category : Electronic books Languages : en Pages : 0
Book Description
This timely overview and synthesis of recent work in both artificial neural networks and neurobiology seeks to examine neurobiological data from a network perspective and to encourage neuroscientists to participate in constructing the next generation of neural networks. Individual chapters were commissioned from selected authors to bridge the gap between present neural network models and the needs of neurophysiologists who are trying to use these models as part of their research on how the brain works.Daniel Gardner is Professor of Physiology and Biophysics at Cornell University Medical College.Contents: Introduction: Toward Neural Neural Networks, Daniel Gardner. Two Principles of Brain Organization: A Challenge for Artificial Neural Networks, Charles F. Stevens. Static Determinants of Synaptic Strength, Daniel Gardner. Learning Rules From Neurobiology, Douglas A. Baxter and John H. Byrne. Realistic Network Models of Distributed Processing in the Leech, Shawn R. Lockery and Terrence J. Sejnowski. Neural and Peripheral Dynamics as Determinants of Patterned Motor Behavior, Hillel J. Chiel and Randall D. Beer. Dynamic Neural Network Models of Sensorimotor Behavior, Eberhard E. Fetz.
Author: Michael A. Arbib Publisher: MIT Press ISBN: 0262011972 Category : Neural circuitry Languages : en Pages : 1328
Book Description
This second edition presents the enormous progress made in recent years in the many subfields related to the two great questions : how does the brain work? and, How can we build intelligent machines? This second edition greatly increases the coverage of models of fundamental neurobiology, cognitive neuroscience, and neural network approaches to language. (Midwest).
Author: Peter Robin Hiesinger Publisher: Princeton University Press ISBN: 0691241694 Category : Computers Languages : en Pages : 384
Book Description
"In this book, Peter Robin Hiesinger explores historical and contemporary attempts to understand the information needed to make biological and artificial neural networks. Developmental neurobiologists and computer scientists with an interest in artificial intelligence - driven by the promise and resources of biomedical research on the one hand, and by the promise and advances of computer technology on the other - are trying to understand the fundamental principles that guide the generation of an intelligent system. Yet, though researchers in these disciplines share a common interest, their perspectives and approaches are often quite different. The book makes the case that "the information problem" underlies both fields, driving the questions that are driving forward the frontiers, and aims to encourage cross-disciplinary communication and understanding, to help both fields make progress. The questions that challenge researchers in these fields include the following. How does genetic information unfold during the years-long process of human brain development, and can this be a short-cut to create human-level artificial intelligence? Is the biological brain just messy hardware that can be improved upon by running learning algorithms in computers? Can artificial intelligence bypass evolutionary programming of "grown" networks? These questions are tightly linked, and answering them requires an understanding of how information unfolds algorithmically to generate functional neural networks. Via a series of closely linked "discussions" (fictional dialogues between researchers in different disciplines) and pedagogical "seminars," the author explores the different challenges facing researchers working on neural networks, their different perspectives and approaches, as well as the common ground and understanding to be found amongst those sharing an interest in the development of biological brains and artificial intelligent systems"--
Author: Michael A. Arbib Publisher: MIT Press (MA) ISBN: 9780262511025 Category : Computers Languages : en Pages : 1118
Book Description
Choice Outstanding Academic Title, 1996. In hundreds of articles by experts from around the world, and in overviews and "road maps" prepared by the editor, The Handbook of Brain Theory and Neural Networks charts the immense progress made in recent years in many specific areas related to great questions: How does the brain work? How can we build intelligent machines? While many books discuss limited aspects of one subfield or another of brain theory and neural networks, the Handbook covers the entire sweep of topics—from detailed models of single neurons, analyses of a wide variety of biological neural networks, and connectionist studies of psychology and language, to mathematical analyses of a variety of abstract neural networks, and technological applications of adaptive, artificial neural networks. Expository material makes the book accessible to readers with varied backgrounds while still offering a clear view of the recent, specialized research on specific topics.
Author: Robert Kozma Publisher: Academic Press ISBN: 0323958168 Category : Computers Languages : en Pages : 398
Book Description
Artificial Intelligence in the Age of Neural Networks and Brain Computing, Second Edition demonstrates that present disruptive implications and applications of AI is a development of the unique attributes of neural networks, mainly machine learning, distributed architectures, massive parallel processing, black-box inference, intrinsic nonlinearity, and smart autonomous search engines. The book covers the major basic ideas of "brain-like computing" behind AI, provides a framework to deep learning, and launches novel and intriguing paradigms as possible future alternatives. The present success of AI-based commercial products proposed by top industry leaders, such as Google, IBM, Microsoft, Intel, and Amazon, can be interpreted using the perspective presented in this book by viewing the co-existence of a successful synergism among what is referred to as computational intelligence, natural intelligence, brain computing, and neural engineering. The new edition has been updated to include major new advances in the field, including many new chapters. Developed from the 30th anniversary of the International Neural Network Society (INNS) and the 2017 International Joint Conference on Neural Networks (IJCNN Authored by top experts, global field pioneers, and researchers working on cutting-edge applications in signal processing, speech recognition, games, adaptive control and decision-making Edited by high-level academics and researchers in intelligent systems and neural networks Includes all new chapters, including topics such as Frontiers in Recurrent Neural Network Research; Big Science, Team Science, Open Science for Neuroscience; A Model-Based Approach for Bridging Scales of Cortical Activity; A Cognitive Architecture for Object Recognition in Video; How Brain Architecture Leads to Abstract Thought; Deep Learning-Based Speech Separation and Advances in AI, Neural Networks
Author: Daniel Gardner Publisher: MIT Press ISBN: 9780262071505 Category : Neural circuitry Languages : en Pages : 254
Book Description
This timely overview and synthesis of recent work in both artificial neural networks and neurobiology seeks to examine neurobiological data from a network perspective and to encourage neuroscientists to participate in constructing the next generation of neural networks.
Author: James A. Anderson Publisher: MIT Press ISBN: 9780262510813 Category : Computers Languages : en Pages : 680
Book Description
An Introduction to Neural Networks falls into a new ecological niche for texts. Based on notes that have been class-tested for more than a decade, it is aimed at cognitive science and neuroscience students who need to understand brain function in terms of computational modeling, and at engineers who want to go beyond formal algorithms to applications and computing strategies. It is the only current text to approach networks from a broad neuroscience and cognitive science perspective, with an emphasis on the biology and psychology behind the assumptions of the models, as well as on what the models might be used for. It describes the mathematical and computational tools needed and provides an account of the author's own ideas. Students learn how to teach arithmetic to a neural network and get a short course on linear associative memory and adaptive maps. They are introduced to the author's brain-state-in-a-box (BSB) model and are provided with some of the neurobiological background necessary for a firm grasp of the general subject. The field now known as neural networks has split in recent years into two major groups, mirrored in the texts that are currently available: the engineers who are primarily interested in practical applications of the new adaptive, parallel computing technology, and the cognitive scientists and neuroscientists who are interested in scientific applications. As the gap between these two groups widens, Anderson notes that the academics have tended to drift off into irrelevant, often excessively abstract research while the engineers have lost contact with the source of ideas in the field. Neuroscience, he points out, provides a rich and valuable source of ideas about data representation and setting up the data representation is the major part of neural network programming. Both cognitive science and neuroscience give insights into how this can be done effectively: cognitive science suggests what to compute and neuroscience suggests how to compute it.
Author: Steven F. Zornetzer Publisher: ISBN: Category : Computer systems Languages : en Pages : 544
Book Description
This presentation of the foremost research and theory from disciplines that provide the foundations of neural network research--neurobiology, physics, computer science, electrical engineering, mathematics, and psychology--shows how neural networks and neurocomputing represent radical departures from conventional approaches to digital computers, in terms of algorithms and architecture.
Author: Christof Koch Publisher: MIT Press ISBN: 9780262112314 Category : Nervous system Languages : en Pages : 700
Book Description
Kinetic Models of Synaptic Transmission / Alain Destexhe, Zachary F. Mainen, Terrence J. Sejnowski / - Cable Theory for Dendritic Neurons / Wilfrid Rall, Hagai Agmon-Snir / - Compartmental Models of Complex Neurons / Idan Segev, Robert E. Burke / - Multiple Channels and Calcium Dynamics / Walter M. Yamada, Christof Koch, Paul R. Adams / - Modeling Active Dendritic Processes in Pyramidal Neurons / Zachary F. Mainen, Terrence J. Sejnowski / - Calcium Dynamics in Large Neuronal Models / Erik De Schutter, Paul Smolen / - Analysis of Neural Excitability and Oscillations / John Rinzel, Bard Ermentrout / - Design and Fabrication of Analog VLSI Neurons / Rodney Douglas, Misha Mahowald / - Principles of Spike Train Analysis / Fabrizio Gabbiani, Christof Koch / - Modeling Small Networks / Larry Abbott, Eve Marder / - Spatial and Temporal Processing in Central Auditory Networks / Shihab Shamma / - Simulating Large Networks of Neurons / Alexander D. Protopapas, Michael Vanier, James M. Bower / ...
Author: Daniel Gardner
Author: Daniel Gardner
Author: Daniel Gardner
Author: Michael A. Arbib
Author: Peter Robin Hiesinger
Author: Michael A. Arbib
Author: Robert Kozma
Author: Daniel Gardner
Author: James A. Anderson
Author: Steven F. Zornetzer
Author: Christof Koch