Principles Of Neural Design

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Principles of Neural Design

Author : Peter Sterling
Publisher : MIT Press
ISBN 13 : 0262534681
Total Pages : 567 pages
Book Rating : 4.2/5 (625 download)

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Book Synopsis Principles of Neural Design by : Peter Sterling

Download or read book Principles of Neural Design written by Peter Sterling and published by MIT Press. This book was released on 2017-06-09 with total page 567 pages. Available in PDF, EPUB and Kindle. Book excerpt: Two distinguished neuroscientists distil general principles from more than a century of scientific study, “reverse engineering” the brain to understand its design. Neuroscience research has exploded, with more than fifty thousand neuroscientists applying increasingly advanced methods. A mountain of new facts and mechanisms has emerged. And yet a principled framework to organize this knowledge has been missing. In this book, Peter Sterling and Simon Laughlin, two leading neuroscientists, strive to fill this gap, outlining a set of organizing principles to explain the whys of neural design that allow the brain to compute so efficiently. Setting out to “reverse engineer” the brain—disassembling it to understand it—Sterling and Laughlin first consider why an animal should need a brain, tracing computational abilities from bacterium to protozoan to worm. They examine bigger brains and the advantages of “anticipatory regulation”; identify constraints on neural design and the need to “nanofy”; and demonstrate the routes to efficiency in an integrated molecular system, phototransduction. They show that the principles of neural design at finer scales and lower levels apply at larger scales and higher levels; describe neural wiring efficiency; and discuss learning as a principle of biological design that includes “save only what is needed.” Sterling and Laughlin avoid speculation about how the brain might work and endeavor to make sense of what is already known. Their distinctive contribution is to gather a coherent set of basic rules and exemplify them across spatial and functional scales.

Neural Network Design and the Complexity of Learning

Author : J. Stephen Judd
Publisher : MIT Press
ISBN 13 : 9780262100458
Total Pages : 188 pages
Book Rating : 4.1/5 (4 download)

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Book Synopsis Neural Network Design and the Complexity of Learning by : J. Stephen Judd

Download or read book Neural Network Design and the Complexity of Learning written by J. Stephen Judd and published by MIT Press. This book was released on 1990 with total page 188 pages. Available in PDF, EPUB and Kindle. Book excerpt: Using the tools of complexity theory, Stephen Judd develops a formal description of associative learning in connectionist networks. He rigorously exposes the computational difficulties in training neural networks and explores how certain design principles will or will not make the problems easier.Judd looks beyond the scope of any one particular learning rule, at a level above the details of neurons. There he finds new issues that arise when great numbers of neurons are employed and he offers fresh insights into design principles that could guide the construction of artificial and biological neural networks.The first part of the book describes the motivations and goals of the study and relates them to current scientific theory. It provides an overview of the major ideas, formulates the general learning problem with an eye to the computational complexity of the task, reviews current theory on learning, relates the book's model of learning to other models outside the connectionist paradigm, and sets out to examine scale-up issues in connectionist learning.Later chapters prove the intractability of the general case of memorizing in networks, elaborate on implications of this intractability and point out several corollaries applying to various special subcases. Judd refines the distinctive characteristics of the difficulties with families of shallow networks, addresses concerns about the ability of neural networks to generalize, and summarizes the results, implications, and possible extensions of the work. Neural Network Design and the Complexity of Learning is included in the Network Modeling and Connectionism series edited by Jeffrey Elman.

Principles of Neural Science

Author : Eric R. Kandel
Publisher :
ISBN 13 : 9780838580684
Total Pages : 1135 pages
Book Rating : 4.5/5 (86 download)

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Book Synopsis Principles of Neural Science by : Eric R. Kandel

Download or read book Principles of Neural Science written by Eric R. Kandel and published by . This book was released on 1991 with total page 1135 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Principles of Neural Information Theory

Author : James V Stone
Publisher :
ISBN 13 : 9780993367922
Total Pages : 214 pages
Book Rating : 4.3/5 (679 download)

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Book Synopsis Principles of Neural Information Theory by : James V Stone

Download or read book Principles of Neural Information Theory written by James V Stone and published by . This book was released on 2018-05-15 with total page 214 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this richly illustrated book, it is shown how Shannon's mathematical theory of information defines absolute limits on neural efficiency; limits which ultimately determine the neuroanatomical microstructure of the eye and brain. Written in an informal style this is an ideal introduction to cutting-edge research in neural information theory.

Principles of Neural Science

Author : Eric R. Kandel
Publisher :
ISBN 13 : 9781264267682
Total Pages : 1646 pages
Book Rating : 4.2/5 (676 download)

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Book Synopsis Principles of Neural Science by : Eric R. Kandel

Download or read book Principles of Neural Science written by Eric R. Kandel and published by . This book was released on 2021 with total page 1646 pages. Available in PDF, EPUB and Kindle. Book excerpt: The goal of this sixth edition of Principles of Neural Science is to provide readers with insight into how genes, molecules, neurons, and the circuits they form give rise to behavior. With the exponential growth in neuroscience research over the 40 years since the first edition of this book, an increasing challenge is to provide a comprehensive overview of the field while remaining true to the original goal of the first edition, which is to elevate imparting basic principles over detailed encyclopedic knowledge.

An Introductory Course in Computational Neuroscience

Author : Paul Miller
Publisher : MIT Press
ISBN 13 : 0262038250
Total Pages : 405 pages
Book Rating : 4.2/5 (62 download)

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Book Synopsis An Introductory Course in Computational Neuroscience by : Paul Miller

Download or read book An Introductory Course in Computational Neuroscience written by Paul Miller and published by MIT Press. This book was released on 2018-10-02 with total page 405 pages. Available in PDF, EPUB and Kindle. Book excerpt: A textbook for students with limited background in mathematics and computer coding, emphasizing computer tutorials that guide readers in producing models of neural behavior. This introductory text teaches students to understand, simulate, and analyze the complex behaviors of individual neurons and brain circuits. It is built around computer tutorials that guide students in producing models of neural behavior, with the associated Matlab code freely available online. From these models students learn how individual neurons function and how, when connected, neurons cooperate in a circuit. The book demonstrates through simulated models how oscillations, multistability, post-stimulus rebounds, and chaos can arise within either single neurons or circuits, and it explores their roles in the brain. The book first presents essential background in neuroscience, physics, mathematics, and Matlab, with explanations illustrated by many example problems. Subsequent chapters cover the neuron and spike production; single spike trains and the underlying cognitive processes; conductance-based models; the simulation of synaptic connections; firing-rate models of large-scale circuit operation; dynamical systems and their components; synaptic plasticity; and techniques for analysis of neuron population datasets, including principal components analysis, hidden Markov modeling, and Bayesian decoding. Accessible to undergraduates in life sciences with limited background in mathematics and computer coding, the book can be used in a “flipped” or “inverted” teaching approach, with class time devoted to hands-on work on the computer tutorials. It can also be a resource for graduate students in the life sciences who wish to gain computing skills and a deeper knowledge of neural function and neural circuits.

Principles Of Artificial Neural Networks: Basic Designs To Deep Learning (4th Edition)

Author : Graupe Daniel
Publisher : World Scientific
ISBN 13 : 9811201242
Total Pages : 440 pages
Book Rating : 4.8/5 (112 download)

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Book Synopsis Principles Of Artificial Neural Networks: Basic Designs To Deep Learning (4th Edition) by : Graupe Daniel

Download or read book Principles Of Artificial Neural Networks: Basic Designs To Deep Learning (4th Edition) written by Graupe Daniel and published by World Scientific. This book was released on 2019-03-15 with total page 440 pages. Available in PDF, EPUB and Kindle. Book excerpt: The field of Artificial Neural Networks is the fastest growing field in Information Technology and specifically, in Artificial Intelligence and Machine Learning.This must-have compendium presents the theory and case studies of artificial neural networks. The volume, with 4 new chapters, updates the earlier edition by highlighting recent developments in Deep-Learning Neural Networks, which are the recent leading approaches to neural networks. Uniquely, the book also includes case studies of applications of neural networks — demonstrating how such case studies are designed, executed and how their results are obtained.The title is written for a one-semester graduate or senior-level undergraduate course on artificial neural networks. It is also intended to be a self-study and a reference text for scientists, engineers and for researchers in medicine, finance and data mining.

Dynamical Systems in Neuroscience

Author : Eugene M. Izhikevich
Publisher : MIT Press
ISBN 13 : 0262514206
Total Pages : 459 pages
Book Rating : 4.2/5 (625 download)

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Book Synopsis Dynamical Systems in Neuroscience by : Eugene M. Izhikevich

Download or read book Dynamical Systems in Neuroscience written by Eugene M. Izhikevich and published by MIT Press. This book was released on 2010-01-22 with total page 459 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explains the relationship of electrophysiology, nonlinear dynamics, and the computational properties of neurons, with each concept presented in terms of both neuroscience and mathematics and illustrated using geometrical intuition. In order to model neuronal behavior or to interpret the results of modeling studies, neuroscientists must call upon methods of nonlinear dynamics. This book offers an introduction to nonlinear dynamical systems theory for researchers and graduate students in neuroscience. It also provides an overview of neuroscience for mathematicians who want to learn the basic facts of electrophysiology. Dynamical Systems in Neuroscience presents a systematic study of the relationship of electrophysiology, nonlinear dynamics, and computational properties of neurons. It emphasizes that information processing in the brain depends not only on the electrophysiological properties of neurons but also on their dynamical properties. The book introduces dynamical systems, starting with one- and two-dimensional Hodgkin-Huxley-type models and continuing to a description of bursting systems. Each chapter proceeds from the simple to the complex, and provides sample problems at the end. The book explains all necessary mathematical concepts using geometrical intuition; it includes many figures and few equations, making it especially suitable for non-mathematicians. Each concept is presented in terms of both neuroscience and mathematics, providing a link between the two disciplines. Nonlinear dynamical systems theory is at the core of computational neuroscience research, but it is not a standard part of the graduate neuroscience curriculum—or taught by math or physics department in a way that is suitable for students of biology. This book offers neuroscience students and researchers a comprehensive account of concepts and methods increasingly used in computational neuroscience. An additional chapter on synchronization, with more advanced material, can be found at the author's website, www.izhikevich.com.

Neural Network Principles

Author : Robert L. Harvey
Publisher :
ISBN 13 :
Total Pages : 216 pages
Book Rating : 4.3/5 (91 download)

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Book Synopsis Neural Network Principles by : Robert L. Harvey

Download or read book Neural Network Principles written by Robert L. Harvey and published by . This book was released on 1994 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt: Using models of biological systems as springboards to a broad range of applications, this volume presents the basic ideas of neural networks in mathematical form. Comprehensive in scope, Neural Network Principles outlines the structure of the human brain, explains the physics of neurons, derives the standard neuron state equations, and presents the consequences of these mathematical models. Author Robert L. Harvey derives a set of simple networks that can filter, recall, switch, amplify, and recognize input signals that are all patterns of neuron activation. The author also discusses properties of general interconnected neuron groups, including the well-known Hopfield and perception neural networks using a unified approach along with suggestions of new design procedures for both. He then applies the theory to synthesize artificial neural networks for specialized tasks. In addition, Neural Network Principles outlines the design of machine vision systems, explores motor control of the human brain and presents two examples of artificial hand-eye systems, demonstrates how to solve large systems of interconnected neurons, and considers control and modulation in the human brain-mind with insights for a new understanding of many mental illnesses.

Pulsed Neural Networks

Author : Wolfgang Maass
Publisher : MIT Press
ISBN 13 : 9780262632218
Total Pages : 414 pages
Book Rating : 4.6/5 (322 download)

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Book Synopsis Pulsed Neural Networks by : Wolfgang Maass

Download or read book Pulsed Neural Networks written by Wolfgang Maass and published by MIT Press. This book was released on 2001-01-26 with total page 414 pages. Available in PDF, EPUB and Kindle. Book excerpt: Most practical applications of artificial neural networks are based on a computational model involving the propagation of continuous variables from one processing unit to the next. In recent years, data from neurobiological experiments have made it increasingly clear that biological neural networks, which communicate through pulses, use the timing of the pulses to transmit information and perform computation. This realization has stimulated significant research on pulsed neural networks, including theoretical analyses and model development, neurobiological modeling, and hardware implementation. This book presents the complete spectrum of current research in pulsed neural networks and includes the most important work from many of the key scientists in the field. Terrence J. Sejnowski's foreword, "Neural Pulse Coding," presents an overview of the topic. The first half of the book consists of longer tutorial articles spanning neurobiology, theory, algorithms, and hardware. The second half contains a larger number of shorter research chapters that present more advanced concepts. The contributors use consistent notation and terminology throughout the book. Contributors Peter S. Burge, Stephen R. Deiss, Rodney J. Douglas, John G. Elias, Wulfram Gerstner, Alister Hamilton, David Horn, Axel Jahnke, Richard Kempter, Wolfgang Maass, Alessandro Mortara, Alan F. Murray, David P. M. Northmore, Irit Opher, Kostas A. Papathanasiou, Michael Recce, Barry J. P. Rising, Ulrich Roth, Tim Schönauer, Terrence J. Sejnowski, John Shawe-Taylor, Max R. van Daalen, J. Leo van Hemmen, Philippe Venier, Hermann Wagner, Adrian M. Whatley, Anthony M. Zador

What Is Health?

Author : Peter Sterling
Publisher : MIT Press
ISBN 13 : 0262043300
Total Pages : 259 pages
Book Rating : 4.2/5 (62 download)

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Book Synopsis What Is Health? by : Peter Sterling

Download or read book What Is Health? written by Peter Sterling and published by MIT Press. This book was released on 2020-02-25 with total page 259 pages. Available in PDF, EPUB and Kindle. Book excerpt: An argument that health is optimal responsiveness and is often best treated at the system level. Medical education centers on the venerable “no-fault” concept of homeostasis, whereby local mechanisms impose constancy by correcting errors, and the brain serves mainly for emergencies. Yet, it turns out that most parameters are not constant; moreover, despite the importance of local mechanisms, the brain is definitely in charge. In this book, the eminent neuroscientist Peter Sterling describes a broader concept: allostasis (coined by Sterling and Joseph Eyer in the 1980s), whereby the brain anticipates needs and efficiently mobilizes supplies to prevent errors. Allostasis evolved early, Sterling explains, to optimize energy efficiency, relying heavily on brain circuits that deliver a brief reward for each positive surprise. Modern life so reduces the opportunities for surprise that we are driven to seek it in consumption: bigger burgers, more opioids, and innumerable activities that involve higher carbon emissions. The consequences include addiction, obesity, type 2 diabetes, and climate change. Sterling concludes that solutions must go beyond the merely technical to restore possibilities for daily small rewards and revivify the capacities for egalitarianism that were hard-wired into our nature. Sterling explains that allostasis offers what is not found in any medical textbook: principled definitions of health and disease: health as the capacity for adaptive variation and disease as shrinkage of that capacity. Sterling argues that since health is optimal responsiveness, many significant conditions are best treated at the system level.

An Introduction to the Event-Related Potential Technique, second edition

Author : Steven J. Luck
Publisher : MIT Press
ISBN 13 : 0262525852
Total Pages : 417 pages
Book Rating : 4.2/5 (625 download)

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Book Synopsis An Introduction to the Event-Related Potential Technique, second edition by : Steven J. Luck

Download or read book An Introduction to the Event-Related Potential Technique, second edition written by Steven J. Luck and published by MIT Press. This book was released on 2014-05-30 with total page 417 pages. Available in PDF, EPUB and Kindle. Book excerpt: An essential guide to designing, conducting, and analyzing event-related potential (ERP) experiments, completely updated for this edition. The event-related potential (ERP) technique, in which neural responses to specific events are extracted from the EEG, provides a powerful noninvasive tool for exploring the human brain. This volume describes practical methods for ERP research along with the underlying theoretical rationale. It offers researchers and students an essential guide to designing, conducting, and analyzing ERP experiments. This second edition has been completely updated, with additional material, new chapters, and more accessible explanations. Freely available supplementary material, including several online-only chapters, offer expanded or advanced treatment of selected topics. The first half of the book presents essential background information, describing the origins of ERPs, the nature of ERP components, and the design of ERP experiments. The second half of the book offers a detailed treatment of the main steps involved in conducting ERP experiments, covering such topics as recording the EEG, filtering the EEG and ERP waveforms, and quantifying amplitudes and latencies. Throughout, the emphasis is on rigorous experimental design and relatively simple analyses. New material in the second edition includes entire chapters devoted to components, artifacts, measuring amplitudes and latencies, and statistical analysis; updated coverage of recording technologies; concrete examples of experimental design; and many more figures. Online chapters cover such topics as overlap, localization, writing and reviewing ERP papers, and setting up and running an ERP lab.

Neural Engineering

Author : Chris Eliasmith
Publisher : MIT Press
ISBN 13 : 9780262550604
Total Pages : 384 pages
Book Rating : 4.5/5 (56 download)

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Book Synopsis Neural Engineering by : Chris Eliasmith

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.

Neural Smithing

Author : Russell Reed
Publisher : MIT Press
ISBN 13 : 0262181908
Total Pages : 359 pages
Book Rating : 4.2/5 (621 download)

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Book Synopsis Neural Smithing by : Russell Reed

Download or read book Neural Smithing written by Russell Reed and published by MIT Press. This book was released on 1999-02-17 with total page 359 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial neural networks are nonlinear mapping systems whose structure is loosely based on principles observed in the nervous systems of humans and animals. The basic idea is that massive systems of simple units linked together in appropriate ways can generate many complex and interesting behaviors. This book focuses on the subset of feedforward artificial neural networks called multilayer perceptrons (MLP). These are the mostly widely used neural networks, with applications as diverse as finance (forecasting), manufacturing (process control), and science (speech and image recognition). This book presents an extensive and practical overview of almost every aspect of MLP methodology, progressing from an initial discussion of what MLPs are and how they might be used to an in-depth examination of technical factors affecting performance. The book can be used as a tool kit by readers interested in applying networks to specific problems, yet it also presents theory and references outlining the last ten years of MLP research.

Artificial Neural Networks in Biological and Environmental Analysis

Author : Grady Hanrahan
Publisher : CRC Press
ISBN 13 : 9781439812594
Total Pages : 214 pages
Book Rating : 4.8/5 (125 download)

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Book Synopsis Artificial Neural Networks in Biological and Environmental Analysis by : Grady Hanrahan

Download or read book Artificial Neural Networks in Biological and Environmental Analysis written by Grady Hanrahan and published by CRC Press. This book was released on 2011-01-18 with total page 214 pages. Available in PDF, EPUB and Kindle. Book excerpt: Originating from models of biological neural systems, artificial neural networks (ANN) are the cornerstones of artificial intelligence research. Catalyzed by the upsurge in computational power and availability, and made widely accessible with the co-evolution of software, algorithms, and methodologies, artificial neural networks have had a profound impact in the elucidation of complex biological, chemical, and environmental processes. Artificial Neural Networks in Biological and Environmental Analysis provides an in-depth and timely perspective on the fundamental, technological, and applied aspects of computational neural networks. Presenting the basic principles of neural networks together with applications in the field, the book stimulates communication and partnership among scientists in fields as diverse as biology, chemistry, mathematics, medicine, and environmental science. This interdisciplinary discourse is essential not only for the success of independent and collaborative research and teaching programs, but also for the continued interest in the use of neural network tools in scientific inquiry. The book covers: A brief history of computational neural network models in relation to brain function Neural network operations, including neuron connectivity and layer arrangement Basic building blocks of model design, selection, and application from a statistical perspective Neurofuzzy systems, neuro-genetic systems, and neuro-fuzzy-genetic systems Function of neural networks in the study of complex natural processes Scientists deal with very complicated systems, much of the inner workings of which are frequently unknown to researchers. Using only simple, linear mathematical methods, information that is needed to truly understand natural systems may be lost. The development of new algorithms to model such processes is needed, and ANNs can play a major role. Balancing basic principles and diverse applications, this text introduces newcomers to the field and reviews recent developments of interest to active neural network practitioners.

Neural Engineering

Author : Bin He
Publisher : Springer Science & Business Media
ISBN 13 : 1461452279
Total Pages : 801 pages
Book Rating : 4.4/5 (614 download)

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Book Synopsis Neural Engineering by : Bin He

Download or read book Neural Engineering written by Bin He and published by Springer Science & Business Media. This book was released on 2013-01-09 with total page 801 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neural Engineering, 2nd Edition, contains reviews and discussions of contemporary and relevant topics by leading investigators in the field. It is intended to serve as a textbook at the graduate and advanced undergraduate level in a bioengineering curriculum. This principles and applications approach to neural engineering is essential reading for all academics, biomedical engineers, neuroscientists, neurophysiologists, and industry professionals wishing to take advantage of the latest and greatest in this emerging field.

The Principles of Deep Learning Theory

Author : Daniel A. Roberts
Publisher : Cambridge University Press
ISBN 13 : 1316519333
Total Pages : 473 pages
Book Rating : 4.3/5 (165 download)

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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.