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Silicon Implementation of Pulse Coded Neural Networks

  • Book
  • © 1994

Overview

Editors:
  1. Mona E. Zaghloul

    1. The George Washington University, USA

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  2. Jack L. Meador

    1. Washington State University, USA

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    You can also search for this editor in PubMed   Google Scholar

  3. Robert W. Newcomb

    1. University of Maryland, USA

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Part of the book series: The Springer International Series in Engineering and Computer Science (SECS, volume 266)

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Table of contents (12 chapters)

  1. Front Matter

    Pages i-ix
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  2. Some Historical Perspectives on Early Pulse Coded Neural Network Circuits

    • Robert W. Newcomb
    Pages 1-8

  3. Pulse Techniques in Neural VLSI: A Review

    • Alan F. Murray
    Pages 9-37

  4. Silicon Dendritic Trees

    • John G. Elias
    Pages 39-63

  5. Silicon Neurons for Phase and Frequency Detection and Pattern Generation

    • Mark DeYong, Chris Fields
    Pages 65-77

  6. Pulse Coded Winner-Take-All Networks

    • Jack L. Meador, Paul D. Hylander
    Pages 79-99

  7. Realization of Boolean Functions Using a Pulse Coded Neuron

    • Marc de Savigny, Robert W. Newcomb
    Pages 101-111

  8. Design of Pulse Coded Neural Processing Element Using Modified Neural Type Cells

    • Gyu Moon, Mona E. Zaghloul
    Pages 113-151

  9. Low-Power Silicon Neurons, Axons and Synapses

    • John Lazzaro, John Wawrzynek
    Pages 153-164

  10. Synchronous Pulse Density Modulation in Neural Network Implementation

    • Jouni Tomberg
    Pages 165-197

  11. CMOS Analog Neural Network Systems Based on Oscillatory Neurons

    • Bernabé Linares-Barranco, Edgar Sánchez-Sinencio, Angel Rodríguez-Vázquez, José L. Huertas
    Pages 199-247

  12. A Digital Neural Network Architecture Using Random Pulse Trains

    • Gamze E. Salam, Rodney M Goodman
    Pages 249-261

  13. An Unsupervised Neural Processor

    • James Donald, Lex A. Akers
    Pages 263-290

  14. Back Matter

    Pages 291-292
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Keywords

About this book

When confronted with the hows and whys of nature's computational engines, some prefer to focus upon neural function: addressing issues of neural system behavior and its relation to natural intelligence. Then there are those who prefer the study of the "mechanics" of neural systems: the nuts and bolts of the "wetware": the neurons and synapses. Those who investigate pulse coded implementations ofartificial neural networks know what it means to stand at the boundary which lies between these two worlds: not just asking why natural neural systems behave as they do, but also how they achieve their marvelous feats. The research results presented in this book not only address more conventional abstract notions of neural-like processing, but also the more specific details ofneural-like processors. It has been established for some time that natural neural systems perform a great deal of information processing via electrochemical pulses. Accordingly, pulse coded neural network concepts are receiving increased attention in artificial neural network research. This increased interest is compounded by continuing advances in the field of VLSI circuit design. This is the first time in history in which it is practical to construct networks of neuron-like circuits of reasonable complexity that can be applied to real problems. We believe that the pioneering work in artificial neural systems presented in this book will lead to further advances that will not only be useful in some practical sense, but may also provide some additional insight into the operation of their natural counterparts.

Editors and Affiliations

  • The George Washington University, USA

    Mona E. Zaghloul

  • Washington State University, USA

    Jack L. Meador

  • University of Maryland, USA

    Robert W. Newcomb

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