Neuromorphic Systems Engineering

Neural Networks in Silicon

  • Tor Sverre Lande

Table of contents

  1. Front Matter
    Pages i-xvii
  2. Cochlear Systems

    1. Front Matter
      Pages 1-1
    2. Richard F. Lyon
      Pages 3-18
    3. Eric Fragnière, André van Schaik, Eric A. Vittoz
      Pages 19-47
    4. Rahul Sarpeshkar, Richard F. Lyon, Carver Mead
      Pages 49-103
    5. John Lazzaro, John Wawrzynek
      Pages 105-126
  3. Retinomorphic Systems

  4. Neuromorphic Communication

  5. Neuromorphic Technology

    1. Front Matter
      Pages 261-261
    2. Chris Diorio
      Pages 263-266
    3. Rahul Sarpeshkar, Richard F. Lyon, Carver Mead
      Pages 267-313
    4. Chris Diorio, Paul Hasler, Bradley A. Minch, Carver Mead
      Pages 315-337
    5. Wayne C. Westerman, David P. M. Northmore, John. G. Elias
      Pages 339-365

About this book

Introduction

Neuromorphic Systems Engineering: Neural Networks in Silicon emphasizes three important aspects of this exciting new research field. The term neuromorphic expresses relations to computational models found in biological neural systems, which are used as inspiration for building large electronic systems in silicon. By adequate engineering, these silicon systems are made useful to mankind.
Neuromorphic Systems Engineering: Neural Networks in Silicon provides the reader with a snapshot of neuromorphic engineering today. It is organized into five parts viewing state-of-the-art developments within neuromorphic engineering from different perspectives.
Neuromorphic Systems Engineering: Neural Networks in Silicon provides the first collection of neuromorphic systems descriptions with firm foundations in silicon. Topics presented include:
  • large scale analog systems in silicon
  • neuromorphic silicon
  • auditory (ear) and vision (eye) systems in silicon
  • learning and adaptation in silicon
  • merging biology and technology
  • micropower analog circuit design
  • analog memory
  • analog interchipcommunication on digital buses £/LIST£
    Neuromorphic Systems Engineering: Neural Networks in Silicon serves as an excellent resource for scientists, researchers and engineers in this emerging field, and may also be used as a text for advanced courses on the subject.
  • Keywords

    VLSI analog analog circuit design communication filter model network networks neural networks speech recognition technology transistor

    Editors and affiliations

    • Tor Sverre Lande
      • 1
    1. 1.University of OsloNorway

    Bibliographic information

    • DOI https://doi.org/10.1007/b102308
    • Copyright Information Kluwer Academic Publishers 1998
    • Publisher Name Springer, Boston, MA
    • eBook Packages Springer Book Archive
    • Print ISBN 978-0-7923-8158-7
    • Online ISBN 978-0-585-28001-1
    • Series Print ISSN 0893-3405
    • About this book
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