Advertisement

Neural Network Simulation Environments

  • Josef Skrzypek

Part of the The Kluwer International Series in Engineering and Computer Science book series (SECS, volume 254)

Table of contents

  1. Front Matter
    Pages i-xxiii
  2. Edmond Mesrobian, Josef Skrzypek, Andy Lee, Brain Ringer
    Pages 1-28
  3. Paul Sajda, Ko Sakai, Shih-Cheng Yen, Leif H. Finkel
    Pages 29-45
  4. Örjan Ekeberg, Per Hammarlund, Björn Levin, Anders Lansner
    Pages 47-71
  5. Alfredo Weitzenfeld, Michael A. Arbib
    Pages 73-93
  6. E. K. Blum, P. M. Khademi, P. K. Leung
    Pages 95-111
  7. J.-F. Vibert, K. Pakdaman, F. Cloppet, N. Azmy
    Pages 113-133
  8. Frank H. Eeckman, Frédéric E. Theunissen, John P. Miller
    Pages 135-145
  9. Michael Hines
    Pages 147-163
  10. Andreas Zell, Niels Mache, Ralf Hübner, Günter Mamier, Michael Vogt, Michael Schmalzl et al.
    Pages 165-186
  11. Nigel H. Goddard
    Pages 187-207
  12. Russell R. Leighton, Alexis P. Wieland
    Pages 209-227
  13. Marwan A. Jabri, Edward A. Tinker, Laurens Leerink
    Pages 229-247
  14. Back Matter
    Pages 249-251

About this book

Introduction

Neural Network Simulation Environments describes some of the best examples of neural simulation environments.
All current neural simulation tools can be classified into four overlapping categories of increasing sophistication in software engineering. The least sophisticated are undocumented and dedicated programs, developed to solve just one specific problem; these tools cannot easily be used by the larger community and have not been included in this volume. The next category is a collection of custom-made programs, some perhaps borrowed from other application domains, and organized into libraries, sometimes with a rudimentary user interface. More recently, very sophisticated programs started to appear that integrate advanced graphical user interface and other data analysis tools. These are frequently dedicated to just one neural architecture/algorithm as, for example, three layers of interconnected artificial `neurons' learning to generalize input vectors using a backpropagation algorithm. Currently, the most sophisticated simulation tools are complete, system-level environments, incorporating the most advanced concepts in software engineering that can support experimentation and model development of a wide range of neural networks. These environments include sophisticated graphical user interfaces as well as an array of tools for analysis, manipulation and visualization of neural data.
Neural Network Simulation Environments is an excellent reference for researchers in both academia and industry, and can be used as a text for advanced courses on the subject.

Keywords

algorithms development interfaces language modeling networks neural networks simulation software software engineering user interface

Editors and affiliations

  • Josef Skrzypek
    • 1
  1. 1.University of CaliforniaLos AngelesUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4615-2736-7
  • Copyright Information Kluwer Academic Publishers 1994
  • Publisher Name Springer, Boston, MA
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4613-6180-0
  • Online ISBN 978-1-4615-2736-7
  • Series Print ISSN 0893-3405
  • Buy this book on publisher's site
Industry Sectors
Electronics
Energy, Utilities & Environment