Principles of Neural Design

Author: Peter Sterling
Publisher: MIT Press
ISBN: 0262028700
Category : Education
Languages : en
Pages : 567

Book Description
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.

Principles of Neural Design

Author: Peter Sterling
Publisher: MIT Press
ISBN: 0262534681
Category : Science
Languages : en
Pages : 567

Book Description
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.

Principles of Neural Science

Author: Eric R. Kandel
Publisher:
ISBN: 9780838580684
Category : Anatomy
Languages : en
Pages : 1135

Book Description

Neural Network Design and the Complexity of Learning

Author: J. Stephen Judd
Publisher: MIT Press
ISBN: 9780262100458
Category : Computers
Languages : en
Pages : 188

Book Description
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.

Introduction To The Theory Of Neural Computation

Author: John A. Hertz
Publisher: CRC Press
ISBN: 0429968213
Category : Science
Languages : en
Pages : 352

Book Description
Comprehensive introduction to the neural network models currently under intensive study for computational applications. It also provides coverage of neural network applications in a variety of problems of both theoretical and practical interest.

Neural Network Principles

Author: Robert L. Harvey
Publisher:
ISBN:
Category : Computers
Languages : en
Pages : 216

Book Description
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.

Dynamical Systems in Neuroscience

Author: Eugene M. Izhikevich
Publisher: MIT Press
ISBN: 0262514206
Category : Medical
Languages : en
Pages : 459

Book Description
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.

Principles of Neural Science

Author: Eric R. Kandel
Publisher:
ISBN: 9781264267682
Category : Neurology
Languages : en
Pages : 1646

Book Description
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.

What Is Health?

Author: Peter Sterling
Publisher: MIT Press
ISBN: 0262043300
Category : Science
Languages : en
Pages : 259

Book Description
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.

Principles of Neural Information Theory

Author: James V Stone
Publisher:
ISBN: 9780993367922
Category : Computers
Languages : en
Pages : 214

Book Description
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.