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.
These processes constitute the final principle of neural design : adapt , match ,
learn , and forget . Conclusions If the principles suggested for neural design are
correct and not mere slogans , they should apply across a range of spatial and ...
Author: Peter Sterling
Understanding how populations of neurons encode information is the challenge faced by researchers in the field of neural coding. Focusing on the many mysteries and marvels of the mind has prompted a prominent team of experts in the field to put their heads together and fire up a book on the subject. Simply titled Principles of Neural Coding, this book covers the complexities of this discipline. It centers on some of the major developments in this area and presents a complete assessment of how neurons in the brain encode information. The book collaborators contribute various chapters that describe results in different systems (visual, auditory, somatosensory perception, etc.) and different species (monkeys, rats, humans, etc). Concentrating on the recording and analysis of the firing of single and multiple neurons, and the analysis and recording of other integrative measures of network activity and network states—such as local field potentials or current source densities—is the basis of the introductory chapters. Provides a comprehensive and interdisciplinary approach Describes topics of interest to a wide range of researchers The book then moves forward with the description of the principles of neural coding for different functions and in different species and concludes with theoretical and modeling works describing how information processing functions are implemented. The text not only contains the most important experimental findings, but gives an overview of the main methodological aspects for studying neural coding. In addition, the book describes alternative approaches based on simulations with neural networks and in silico modeling in this highly interdisciplinary topic. It can serve as an important reference to students and professionals.
Indeed, neuromorphic engineering takes inspiration from biology, physics,
mathematics, computer science, and electrical engineering to design artificial
neural systems for carrying out robust and efficient computation using low-power,
Author: Rodrigo Quian Quiroga
Publisher: CRC Press
Modern neural networks gave rise to major breakthroughs in several research areas. In neuroscience, we are witnessing a reappraisal of neural network theory and its relevance for understanding information processing in biological systems. The research presented in this book provides various perspectives on the use of artificial neural networks as models of neural information processing. We consider the biological plausibility of neural networks, performance improvements, spiking neural networks and the use of neural networks for understanding brain function.
Principles of Neural Design. Cambridge, MA: MIT Press. doi:10.7551/mitpress/
9780262028707.001.0001 Strukov, D. B. (2011). Smart connections. Nature 476,
403–405. doi: 10.1038/ 476403a Summerfield, C., and de Lange, F. P. (2014).
Author: Marcel van Gerven
Publisher: Frontiers Media SA
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.
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.
Author: Graupe Daniel
Publisher: World Scientific
Mechatronics as a discipline has an ever growing impact on engineering and engineering education as a defining approach to the design, development, and operation of an increasingly wide range of engineering systems. The increasing scope and complexity of mechatronic systems means that their design and development now involve not only the technical aspects of its core disciplines, but also aspects of organization, training, and management. Mechatronics and the Design of Intelligent Machines and Systems reflects the significant areas of development in mechatronics and focuses on the higher-level approaches needed to support the design and implementation of mechatronic systems. Throughout the book, the authors emphasize the importance of systems integration. Each chapter deals with a particular aspect of the design and development process, from the specification of the system to software design and from the human-machine interface to the requirements for safe operation and effective manufacture. Notable among this text's many features is the use of a running case study-the autonomous and robotic excavator LUCIE-to illustrate points made in various chapters. This, combined with the authors' clear prose, systematic organization, and generous use of examples and illustrations provides students with a firm understanding of mechatronics as a discipline, some of the problems encountered in its various areas, and the developing techniques used to solve those problems.
Neural networks and fuzzy systems Humans are capable of reasoning using
incomplete, uncertain, and noisy data and of ... In this chapter the basic principles
of neural networks and fuzzy systems are introduced along with examples of their
Author: David Allan Bradley
Publisher: CRC Press
Category: Technology & Engineering
The book should serve as a text for a university graduate course or for an advanced undergraduate course on neural networks in engineering and computer science departments. It should also serve as a self-study course for engineers and computer scientists in the industry. Covering major neural network approaches and architectures with the theories, this text presents detailed case studies for each of the approaches, accompanied with complete computer codes and the corresponding computed results. The case studies are designed to allow easy comparison of network performance to illustrate strengths and weaknesses of the different networks.
Chapter 5: Problem 5.1: Design a 2-layer Madaline neural network to recognize
digits from a set of 3 cursive digits in a 5-by-5 grid. Chapter 6: Problem 6.1:
Design a back propagation (BP) network to solve the XOR problem. Problem 6.2:
Author: Graupe Daniel
Publisher: World Scientific
Category: Neural networks (Computer science)
"This book presents basic principles of geometric modelling while featuring contemporary industrial case studies"--Provided by publisher.
Principles and Implementations Xu, Xun ... NEURAL. NETWORK. METHODS.
Although expert system applications have been found in many areas of design
and manufacturing, it has its intrinsic weaknesses. The system has total reliance
Author: Xu, Xun
Publisher: IGI Global
This textbook is intended for a first-year graduate course on Artificial Neural Networks. It assumes no prior background in the subject and is directed to MS students in electrical engineering, computer science and related fields, with background in at least one programming language or in a programming tool such as Matlab, and who have taken the basic undergraduate classes in systems or in signal processing.
The training is stable and does not switch once a winning neuron has been
identified. 4. ... It also leads us to consider the neural network designs of the next
chapters, specifically, the Cognitron/Neocognitron neural network design and the
Author: Daniel Graupe
Publisher: World Scientific
Principles of Neural Science, 5e describes our current understanding of how the nerves, brain, and mind function. From molecules and cells to anatomic structures and systems to senses and cognitive functions, this comprehensive reference covers every aspect of neuroscience.
Kv3 channels: voltage-gated Ki channels designed for high-frequency repetitive
firing. Trends Neurosci 24:517—526. ... of the voltage-gated sodium. 170 Part II /
Cell and Molecular Biology of the Neuron An Overall View Selected Readings.
Author: Eric Kandel
Publisher: McGraw Hill Professional
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.
By presenting the basic principles of neural networks together with real-world
applications in the field, I seek to stimulate ... Chapter 3 covers the eight-step
development process and presents the basic building blocks of model design, ...
Author: Grady Hanrahan
Publisher: CRC Press
This book constitutes the thoroughly refereed proceedings of the 30th Annual German Conference on Artificial Intelligence, KI 2007, held in Osnabrück, Germany, September 10-13, 2007. The 26 revised full papers presented together with 6 invited contributions and 21 posters were carefully reviewed and selected from 81 submissions. The papers are organized in topical sections on cognition and emotion, semantic Web, analogy, natural language, reasoning, ontologies, spatio-temporal reasoning, machine learning, spatial reasoning, robot learning, classical AI problems, and agents.
1 Introduction A meaningful combination of the principles of neural networks and
evolutionary computation is useful for designing agents that learn and adapt to
their environment through interaction. One step towards achieving such a ...
Author: Joachim Hertzberg
Publisher: Springer Science & Business Media
To achieve this goal, each model mechanizes basic principles of neural design
which we suggest are rate-limiting in the data gathered by any of several
methods. To test whether these principles are operative in the cases we discuss,
Author: Jonathan P. E. Hodgson
Publisher: Springer Verlag
*Introduces cutting-edge control systems to a wide readership of engineers and students *The first book on neuro-fuzzy control systems to take a practical, applications-based approach, backed up with worked examples and case studies *Learn to use VHDL in real-world applications Introducing cutting edge control systems through real-world applications Neural networks and fuzzy logic based systems offer a modern control solution to AC machines used in variable speed drives, enabling industry to save costs and increase efficiency by replacing expensive and high-maintenance DC motor systems. The use of fast micros has revolutionised the field with sensorless vector control and direct torque control. This book reflects recent research findings and acts as a useful guide to the new generation of control systems for a wide readership of advanced undergraduate and graduate students, as well as practising engineers. The authors guide readers quickly and concisely through the complex topics of neural networks, fuzzy logic, mathematical modelling of electrical machines, power systems control and VHDL design. Unlike the academic monographs that have previously been published on each of these subjects, this book combines them and is based round case studies of systems analysis, control strategies, design, simulation and implementation. The result is a guide to applied control systems design that will appeal equally to students and professional design engineers. The book can also be used as a unique VHDL design aid, based on real-world power engineering applications.
... implementation example: the neural network controlling the current through the
stator windings of the induction motor. Experimental results are presented to
demonstrate the validity of the adopted design and implementation principles.
Author: Marcian Cirstea
A fundamental and comprehensive framework for network security designed for military, government, industry, and academic network personnel. Scientific validation of "security on demand" through computer modeling and simulation methods. The book presents an example wherein the framework is utilized to integrate security into the operation of a network. As a result of the integration, the inherent attributes of the network may be exploited to reduce the impact of security on network performance and the security availability may be increased down to the user level. The example selected is the ATM network which is gaining widespread acceptance and use.
Weerasooriya and colleagues ( 43 ) present a neural - network solution to the
problem of security assessment in large - scale power systems . They use neural
nets for fast pattern matching of the state of the power system immediately ...
Author: Sumit Ghosh
Publisher: Springer Science & Business Media
In this book, Richard Michod offers a fresh, dynamical interpretation of evolution and fitness concepts. He argues that evolution has no enduring products; what matters is the process of genetic change. Whereas many biologists have focused on competition and aggression as determining factors in survival, Michod, by concentrating on the emergence of individuality at new and more complex levels, finds that cooperation plays an even greater role.
CHAPTER 8 A Philosophy of Fitness Dynamics of Design Not so long ago ,
before Darwin , the source of design ... For an explanation of the principles of
neural Darwinism oriented to evolutionary biologists , see Michod ( 1989 , 1990 )
Author: Richard E. Michod
Publisher: Princeton University Press
Proceedings of the Artificial Neural Networks in Engineering Conference, November 5-8, 2000, St Louis, Missouri. The 179 papers compiled in this book focus on building smart components to engineering systems currently available. Topics discussed include: Neural Networks, Fuzzy Systems, Complex Systems, Pattern Recognition, Smart Engineering Systems, Evolutionary Programming, Data Mining, Adaptive Control, and Biology and Medicine. Special tenth anniverary edition includes subject and author indices.
From basic network principles to neural architecture : Emergence of orientation
columns . Proceedings of the National Academy of Sciences USA , 83 : 8779 -
8783 . Linsker , R . ( 1986c ) . From basic network principles to neural
Author: Cihan H. Dagli
Publisher: Amer Society of Mechanical
Category: Technology & Engineering
A synthesis of current approaches to adapting engineering tools to the study of neurobiological systems.
those representations ( i.e. , without simulating every individual neuron's function
) while simulating other parts of the model with ... These experiments may take
the form of simply changing the input to the network , or they might involve
changing system properties defined in the design ... In sum , the main purpose of
the final step of the methodology is to apply the principles of neural engineering
Author: Chris Eliasmith
Publisher: MIT Press
The neural dynamics approach is based on neurophysiological data and facts ,
and also aims to construct the principles of neural design ( Grossberg and
Mingolla 1985a ) by means of particular computational and simulation
Category: Electronic journals
Dictionary of Scientific Principles presents a unique and timeless collection of (almost) all known rules or laws commonly called principles, identified throughout the history of scientific development, their definition, and use. Exploring a broad range of disciplines, the book first lists more than 2,000 principles organized in a standard alphabetical order, then provides a list of subject headings for which related principles are identified. A staple addition to every library, the dictionary will also be of interest to scientists and general readers.
N ATOMIC POSITION VECTORS Minimal Principle NARCISSISM Beyond The
Pleasure Principle; Jewish Pleasure ... Principle of Least Motion, Equidistortion
Principle; Quantization Design Principle 572 NEURAL NETWORKS NEURAL ...
Author: Stephen Marvin
Publisher: John Wiley & Sons
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.
A neural network is characterized by its emphasis on using many richly
interconnected processors that perform ... As yet, there seem to be few principles
or methodologies for designing the specific connectivity patterns in these
Author: J. Stephen Judd
Publisher: MIT Press