Search Results for "principles-of-neural-design-mit-press"

Principles of Neural Design

Principles of Neural Design

  • Author: Peter Sterling,Simon Laughlin
  • Publisher: MIT Press
  • ISBN: 0262028700
  • Category: Science
  • Page: 568
  • View: 9708
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Two distinguished neuroscientists distil general principles from more than a century of scientific study, "reverse engineering" the brain to understand its design.

Artificial Neural Networks as Models of Neural Information Processing

Artificial Neural Networks as Models of Neural Information Processing

  • Author: Marcel van Gerven,Sander Bohte
  • Publisher: Frontiers Media SA
  • ISBN: 2889454010
  • Category:
  • Page: N.A
  • View: 1254
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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.

Neural Engineering

Neural Engineering

Computation, Representation, and Dynamics in Neurobiological Systems

  • Author: Chris Eliasmith,Charles H. Anderson
  • Publisher: MIT Press
  • ISBN: 9780262550604
  • Category: Medical
  • Page: 356
  • View: 2853
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A synthesis of current approaches to adapting engineering tools to the study of neurobiological systems.

Knowledge-based Neurocomputing

Knowledge-based Neurocomputing

  • Author: Ian Cloete,Jacek M. Zurada
  • Publisher: MIT Press
  • ISBN: 9780262032742
  • Category: Computers
  • Page: 486
  • View: 4250
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Neurocomputing methods are loosely based on a model of the brain as a network of simple interconnected processing elements corresponding to neurons. These methods derive their power from the collective processing of artificial neurons, the chief advantage being that such systems can learn and adapt to a changing environment. In knowledge-based neurocomputing, the emphasis is on the use and representation of knowledge about an application. Explicit modeling of the knowledge represented by such a system remains a major research topic. The reason is that humans find it difficult to interpret the numeric representation of a neural network. The key assumption of knowledge-based neurocomputing is that knowledge is obtainable from, or can be represented by, a neurocomputing system in a form that humans can understand. That is, the knowledge embedded in the neurocomputing system can also be represented in a symbolic or well-structured form, such as Boolean functions, automata, rules, or other familiar ways. The focus of knowledge-based computing is on methods to encode prior knowledge and to extract, refine, and revise knowledge within a neurocomputing system. Contributors: C. Aldrich, J. Cervenka, I. Cloete, R.A. Cozzio, R. Drossu, J. Fletcher, C.L. Giles, F.S. Gouws, M. Hilario, M. Ishikawa, A. Lozowski, Z. Obradovic, C.W. Omlin, M. Riedmiller, P. Romero, G.P.J. Schmitz, J. Sima, A. Sperduti, M. Spott, J. Weisbrod, J.M. Zurada.

Neural Network Design and the Complexity of Learning

Neural Network Design and the Complexity of Learning

  • Author: J. Stephen Judd
  • Publisher: MIT Press
  • ISBN: 9780262100458
  • Category: Psychology
  • Page: 150
  • View: 1798
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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.

Event-Based Neuromorphic Systems

Event-Based Neuromorphic Systems

  • Author: Shih-Chii Liu,Tobi Delbruck,Giacomo Indiveri,Rodney Douglas,Adrian Whatley
  • Publisher: John Wiley & Sons
  • ISBN: 0470018496
  • Category: Technology & Engineering
  • Page: 440
  • View: 3744
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Neuromorphic electronic engineering takes its inspiration from the functioning of nervous systems to build more power efficient electronic sensors and processors. Event-based neuromorphic systems are inspired by the brain's efficient data-driven communication design, which is key to its quick responses and remarkable capabilities. This cross-disciplinary text establishes how circuit building blocks are combined in architectures to construct complete systems. These include vision and auditory sensors as well as neuronal processing and learning circuits that implement models of nervous systems. Techniques for building multi-chip scalable systems are considered throughout the book, including methods for dealing with transistor mismatch, extensive discussions of communication and interfacing, and making systems that operate in the real world. The book also provides historical context that helps relate the architectures and circuits to each other and that guides readers to the extensive literature. Chapters are written by founding experts and have been extensively edited for overall coherence. This pioneering text is an indispensable resource for practicing neuromorphic electronic engineers, advanced electrical engineering and computer science students and researchers interested in neuromorphic systems. Key features: Summarises the latest design approaches, applications, and future challenges in the field of neuromorphic engineering. Presents examples of practical applications of neuromorphic design principles. Covers address-event communication, retinas, cochleas, locomotion, learning theory, neurons, synapses, floating gate circuits, hardware and software infrastructure, algorithms, and future challenges.

Hybrid Modeling and Optimization of Manufacturing

Hybrid Modeling and Optimization of Manufacturing

Combining Artificial Intelligence and Finite Element Method

  • Author: Ramón Quiza,Omar López-Armas,J. Paulo Davim
  • Publisher: Springer Science & Business Media
  • ISBN: 3642280846
  • Category: Computers
  • Page: 95
  • View: 9997
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Artificial intelligence (AI) techniques and the finite element method (FEM) are both powerful computing tools, which are extensively used for modeling and optimizing manufacturing processes. The combination of these tools has resulted in a new flexible and robust approach as several recent studies have shown. This book aims to review the work already done in this field as well as to expose the new possibilities and foreseen trends. The book is expected to be useful for postgraduate students and researchers, working in the area of modeling and optimization of manufacturing processes.

Principles of Neural Coding

Principles of Neural Coding

  • Author: Rodrigo Quian Quiroga,Stefano Panzeri
  • Publisher: CRC Press
  • ISBN: 1439853312
  • Category: Medical
  • Page: 663
  • View: 1203
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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.

Handbook of Research on Big Data Storage and Visualization Techniques

Handbook of Research on Big Data Storage and Visualization Techniques

  • Author: Segall, Richard S.,Cook, Jeffrey S.
  • Publisher: IGI Global
  • ISBN: 1522531432
  • Category: Computers
  • Page: 917
  • View: 4048
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The digital age has presented an exponential growth in the amount of data available to individuals looking to draw conclusions based on given or collected information across industries. Challenges associated with the analysis, security, sharing, storage, and visualization of large and complex data sets continue to plague data scientists and analysts alike as traditional data processing applications struggle to adequately manage big data. The Handbook of Research on Big Data Storage and Visualization Techniques is a critical scholarly resource that explores big data analytics and technologies and their role in developing a broad understanding of issues pertaining to the use of big data in multidisciplinary fields. Featuring coverage on a broad range of topics, such as architecture patterns, programing systems, and computational energy, this publication is geared towards professionals, researchers, and students seeking current research and application topics on the subject.

Form and Function in the Brain and Spinal Cord

Form and Function in the Brain and Spinal Cord

Perspectives of a Neurologist

  • Author: Stephen G. Waxman
  • Publisher: MIT Press
  • ISBN: 9780262731553
  • Category: Medical
  • Page: 502
  • View: 1581
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Reflections on Stephen Waxman's three decades of research on the form and functions of the brain and spinal cord.