Danny Amor's Online Library/Danny Amor's Online Library - Volume 1.iso/html/faqs/faq/neural-net-faq

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Subject: FAQ in comp.ai.neural-nets -- monthly posting
Newsgroups: comp.ai.neural-nets,comp.answers,news.answers
From: [e-mail] (Lutz Prechelt)
Date: 28 Oct 1994 03:17:40 GMT

Archive-name: neural-net-faq
Last-modified: 1994/10/25
URL: http://wwwipd.ira.uka.de/~prechelt/FAQ/neural-net-faq.html
Maintainer: [e-mail] (Lutz Prechelt)


  ------------------------------------------------------------------------
        Additions, corrections, or improvements are always welcome.
        Anybody who is willing to contribute any information,
        please email me; if it is relevant, I will incorporate it.

        The monthly posting departs at the 28th of every month.
  ------------------------------------------------------------------------


This is a monthly posting to the Usenet newsgroup comp.ai.neural-nets
(and comp.answers, where it should be findable at ANY time). Its
purpose is to provide basic information for individuals who are new to the
field of neural networks or are just beginning to read this group. It shall
help to avoid lengthy discussion of questions that usually arise for
beginners of one or the other kind.

   SO, PLEASE, SEARCH THIS POSTING FIRST IF YOU HAVE A QUESTION
                           and
   DON'T POST ANSWERS TO FAQs: POINT THE ASKER TO THIS POSTING

This posting is archived in the periodic posting archive on host
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you can access the archives by mail server as well. Send an E-mail
message to [e-mail] with "help" and "index" in the
body on separate lines for more information.

For those of you who read this posting anywhere other than in
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you need Usenet News access. Try the commands, 'xrn', 'rn', 'nn', or 'trn'
on your Unix machine, 'news' on your VMS machine, or ask a local
guru.

This monthly posting is also available as a hypertext document in WWW
(World Wide Web) under the URL
"http://wwwipd.ira.uka.de/Tichy/neural-net-faq.html"

The monthly posting is not meant to discuss any topic exhaustively.

Disclaimer:
   This posting is provided 'as is'.
   No warranty whatsoever is expressed or implied,
   in particular, no warranty that the information contained herein
   is correct or useful in any way, although both is intended.

To find the answer of question number 'x', search for the string
"x. A:" (so the answer to question 12 is at   12. A:  )


And now, in the end, we begin:

========== Questions ==========
********************************

 1. What is this newsgroup for? How shall it be used?
 2. What is a neural network (NN)?
 3. What can you do with a Neural Network and what not?
 4. Who is concerned with Neural Networks?

 5. What does 'backprop' mean?
 6. How many learning methods for NNs exist? Which?
 7. What about Genetic Algorithms?
 8. What about Fuzzy Logic?

 9. Good introductory literature about Neural Networks?
 10. Any journals and magazines about Neural Networks?
 11. The most important conferences concerned with Neural
   Networks?
 12. Neural Network Associations?
 13. Other sources of information about NNs?

 14. Freely available software packages for NN simulation?
 15. Commercial software packages for NN simulation?
 16. Neural Network hardware?

 17. Databases for experimentation with NNs?

========== Answers ==========
******************************

 1. A: What is this newsgroup for? How shall it be
 =================================================
   used?
   =====

   The newsgroup comp.ai.neural-nets is inteded as a forum for
   people who want to use or explore the capabilities of Artificial
   Neural Networks or Neural-Network-like structures.

   There should be the following types of articles in this newsgroup:

    1. Requests
    +++++++++++

      Requests are articles of the form "I am looking for
      X" where X is something public like a book, an article, a
      piece of software. The most important about such a request
      is to be as specific as possible!

      If multiple different answers can be expected, the person
      making the request should prepare to make a summary of
      the answers he/she got and announce to do so with a
      phrase like "Please reply by email, I'll
      summarize to the group" at the end of the posting.

      The Subject line of the posting should then be something
      like "Request: X"

    2. Questions
    ++++++++++++

      As opposed to requests, questions ask for a larger piece of
      information or a more or less detailed explanation of
      something. To avoid lots of redundant traffic it is important
      that the poster provides with the question all information
      s/he already has about the subject asked and state the
      actual question as precise and narrow as possible. The
      poster should prepare to make a summary of the answers
      s/he got and announce to do so with a phrase like
      "Please reply by email, I'll summarize to
      the group" at the end of the posting.

      The Subject line of the posting should be something like
      "Question: this-and-that" or have the form of a
      question (i.e., end with a question mark)

    3. Answers
    ++++++++++

      These are reactions to questions or requests. As a rule of
      thumb articles of type "answer" should be rare. Ideally, in
      most cases either the answer is too specific to be of general
      interest (and should thus be e-mailed to the poster) or a
      summary was announced with the question or request (and
      answers should thus be e-mailed to the poster).

      The subject lines of answers are automatically adjusted by
      the news software. Note that sometimes longer threads of
      discussion evolve from an answer to a question or request.
      In this case posters should change the subject line suitably
      as soon as the topic goes too far away from the one
      announced in the original subject line. You can still carry
      along the old subject in parentheses in the form
      "Subject: new subject (was: old subject)"

    4. Summaries
    ++++++++++++

      In all cases of requests or questions the answers for which
      can be assumed to be of some general interest, the poster of
      the request or question shall summarize the ansers he/she
      received. Such a summary should be announced in the
      original posting of the question or request with a phrase
      like "Please answer by email, I'll
      summarize"

      In such a case, people who answer to a question should
      NOT post their answer to the newsgroup but instead mail
      them to the poster of the question who collects and reviews
      them. After about 5 to 20 days after the original posting, its
      poster should make the summary of answers and post it to
      the newsgroup.

      Some care should be invested into a summary:
       o simple concatenation of all the answers is not
         enough: instead, redundancies, irrelevancies,
         verbosities, and errors should be filtered out (as good
         as possible)
       o the answers should be separated clearly
       o the contributors of the individual answers should be
         identifiable (unless they requested to remain
         anonymous [yes, that happens])
       o the summary should start with the "quintessence" of
         the answers, as seen by the original poster
       o A summary should, when posted, clearly be
         indicated to be one by giving it a Subject line
         starting with "SUMMARY:"
      Note that a good summary is pure gold for the rest of the
      newsgroup community, so summary work will be most
      appreciated by all of us. Good summaries are more valuable
      than any moderator ! :-)

    5. Announcements
    ++++++++++++++++

      Some articles never need any public reaction. These are
      called announcements (for instance for a workshop,
      conference or the availability of some technical report or
      software system).

      Announcements should be clearly indicated to be such by
      giving them a subject line of the form "Announcement:
      this-and-that"

    6. Reports
    ++++++++++

      Sometimes people spontaneously want to report something
      to the newsgroup. This might be special experiences with
      some software, results of own experiments or conceptual
      work, or especially interesting information from
      somewhere else.

      Reports should be clearly indicated to be such by giving
      them a subject line of the form "Report:
      this-and-that"

    7. Discussions
    ++++++++++++++

      An especially valuable possibility of Usenet is of course
      that of discussing a certain topic with hundreds of potential
      participants. All traffic in the newsgroup that can not be
      subsumed under one of the above categories should belong
      to a discussion.

      If somebody explicitly wants to start a discussion, he/she
      can do so by giving the posting a subject line of the form
      "Subject: Discussion: this-and-that"

      It is quite difficult to keep a discussion from drifting into
      chaos, but, unfortunately, as many many other newsgroups
      show there seems to be no secure way to avoid this. On the
      other hand, comp.ai.neural-nets has not had many
      problems with this effect in the past, so let's just go and
      hope...

   ------------------------------------------------------------------------

 2. A: What is a neural network (NN)?
 ====================================

   First of all, when we are talking about a neural network, we
   *should* usually better say "artificial neural network" (ANN),
   because that is what we mean most of the time. Biological neural
   networks are much more complicated in their elementary
   structures than the mathematical models we use for ANNs.

   A vague description is as follows:

   An ANN is a network of many very simple processors ("units"),
   each possibly having a (small amount of) local memory. The units
   are connected by unidirectional communication channels
   ("connections"), which carry numeric (as opposed to symbolic)
   data. The units operate only on their local data and on the inputs
   they receive via the connections.

   The design motivation is what distinguishes neural networks from
   other mathematical techniques:

   A neural network is a processing device, either an algorithm, or
   actual hardware, whose design was motivated by the design and
   functioning of human brains and components thereof.

   Most neural networks have some sort of "training" rule whereby
   the weights of connections are adjusted on the basis of presented
   patterns. In other words, neural networks "learn" from examples,
   just like children learn to recognize dogs from examples of dogs,
   and exhibit some structural capability for generalization.

   Neural networks normally have great potential for parallelism,
   since the computations of the components are independent of each
   other.

   ------------------------------------------------------------------------

 3. A: What can you do with a Neural Network and
 ===============================================
   what not?
   =========

   In principle, NNs can compute any computable function, i.e. they
   can do everything a normal digital computer can do. Especially
   anything that can be represented as a mapping between vector
   spaces can be approximated to arbitrary precision by feedforward
   NNs (which is the most often used type).

   In practice, NNs are especially useful for mapping problems which
   are tolerant of some errors, have lots of example data available,
   but to which hard and fast rules can not easily be applied. NNs
   are, at least today, difficult to apply successfully to problems that
   concern manipulation of symbols and memory.

   ------------------------------------------------------------------------

 4. A: Who is concerned with Neural Networks?
 ============================================

   Neural Networks are interesting for quite a lot of very dissimilar
   people:
    o Computer scientists want to find out about the properties
      of non-symbolic information processing with neural nets
      and about learning systems in general.
    o Engineers of many kinds want to exploit the capabilities of
      neural networks on many areas (e.g. signal processing) to
      solve their application problems.
    o Cognitive scientists view neural networks as a possible
      apparatus to describe models of thinking and conscience
      (High-level brain function).
    o Neuro-physiologists use neural networks to describe and
      explore medium-level brain function (e.g. memory, sensory
      system, motorics).
    o Physicists use neural networks to model phenomena in
      statistical mechanics and for a lot of other tasks.
    o Biologists use Neural Networks to interpret nucleotide
      sequences.
    o Philosophers and some other people may also be interested
      in Neural Networks for various reasons.

   ------------------------------------------------------------------------

 5. A: What does 'backprop' mean?
 ================================

   It is an abbreviation for 'backpropagation of error' which is the
   most widely used learning method for neural networks today.
   Although it has many disadvantages, which could be summarized
   in the sentence "You are almost not knowing what you are
   actually doing when using backpropagation" :-) it has pretty
   much success on practical applications and is relatively easy to
   apply.

   It is for the training of layered (i.e., nodes are grouped in layers)
   feedforward (i.e., the arcs joining nodes are unidirectional, and
   there are no cycles) nets (often called "multi layer perceptrons").

   Back-propagation needs a teacher that knows the correct output
   for any input ("supervised learning") and uses gradient descent on
   the error (as provided by the teacher) to train the weights. The
   activation function is (usually) a sigmoidal (i.e., bounded above
   and below, but differentiable) function of a weighted sum of the
   nodes inputs.

   The use of a gradient descent algorithm to train its weights makes
   it slow to train; but being a feedforward algorithm, it is quite rapid
   during the recall phase.

   Literature:
      Rumelhart, D. E. and McClelland, J. L. (1986): Parallel
      Distributed Processing: Explorations in the Microstructure
      of Cognition (volume 1, pp 318-362). The MIT Press.

   (this is the classic one) or one of the dozens of other books or
   articles on backpropagation (see also answer 9).

   ------------------------------------------------------------------------

 6. A: How many learning methods for NNs exist?
 ==============================================
   Which?
   ======

   There are many many learning methods for NNs by now. Nobody
   knows exactly how many. New ones (at least variations of existing
   ones) are invented every week. Below is a collection of some of the
   most well known methods; not claiming to be complete.

   The main categorization of these methods is the distiction of
   supervised from unsupervised learning:

   In supervised learning, there is a "teacher" who in the learning
   phase "tells" the net how well it performs ("reinforcement
   learning") or what the correct behavior would have been ("fully
   supervised learning").

   In unsupervised learning the net is autonomous: it just looks at the
   data it is presented with, finds out about some of the properties of
   the data set and learns to reflect these properties in its output.
   What exactly these properties are, that the network can learn to
   recognise, depends on the particular network model and learning
   method.

   Many of these learning methods are closely connected with a
   certain (class of) network topology.

   Now here is the list, just giving some names:

   1. UNSUPERVISED LEARNING (i.e. without a "teacher"):
        1). Feedback Nets:
           a). Additive Grossberg (AG)
           b). Shunting Grossberg (SG)
           c). Binary Adaptive Resonance Theory (ART1)
           d). Analog Adaptive Resonance Theory (ART2, ART2a)
           e). Discrete Hopfield (DH)
           f). Continuous Hopfield (CH)
           g). Discrete Bidirectional Associative Memory (BAM)
           h). Temporal Associative Memory (TAM)
           i). Adaptive Bidirectional Associative Memory (ABAM)
           j). Kohonen Self-organizing Map/Topology-preserving map (SOM/TPM)
           k). Competitive learning
        2). Feedforward-only Nets:
           a). Learning Matrix (LM)
           b). Driver-Reinforcement Learning (DR)
           c). Linear Associative Memory (LAM)
           d). Optimal Linear Associative Memory (OLAM)
           e). Sparse Distributed Associative Memory (SDM)
           f). Fuzzy Associative Memory (FAM)
           g). Counterprogation (CPN)

   2. SUPERVISED LEARNING (i.e. with a "teacher"):
        1). Feedback Nets:
           a). Brain-State-in-a-Box (BSB)
           b). Fuzzy Congitive Map (FCM)
           c). Boltzmann Machine (BM)
           d). Mean Field Annealing (MFT)
           e). Recurrent Cascade Correlation (RCC)
           f). Learning Vector Quantization (LVQ)
           g). Backpropagation through time (BPTT)
           h). Real-time recurrent learning (RTRL)
           i). Recurrent Extended Kalman Filter (EKF)
        2). Feedforward-only Nets:
           a). Perceptron
           b). Adaline, Madaline
           c). Backpropagation (BP)
           d). Cauchy Machine (CM)
           e). Adaptive Heuristic Critic (AHC)
           f). Time Delay Neural Network (TDNN)
           g). Associative Reward Penalty (ARP)
           h). Avalanche Matched Filter (AMF)
           i). Backpercolation (Perc)
           j). Artmap
           k). Adaptive Logic Network (ALN)
           l). Cascade Correlation (CasCor)
           m). Extended Kalman Filter(EKF)

   ------------------------------------------------------------------------

 7. A: What about Genetic Algorithms?
 ====================================

   There are a number of definitions of GA (Genetic Algorithm). A
   possible one is

     A GA is an optimization program
     that starts with
     a population of encoded procedures,       (Creation of Life :-> )
     mutates them stochastically,              (Get cancer or so :-> )
     and uses a selection process              (Darwinism)
     to prefer the mutants with high fitness
     and perhaps a recombination process       (Make babies :-> )
     to combine properties of (preferably) the succesful mutants.

   Genetic Algorithms are just a special case of the more general idea
   of ``evolutionary computation''. There is a newsgroup that is
   dedicated to the field of evolutionary computation called
   comp.ai.genetic. It has a detailed FAQ posting which, for instance,
   explains the terms "Genetic Algorithm", "Evolutionary
   Programming", "Evolution Strategy", "Classifier System", and
   "Genetic Programming". That FAQ also contains lots of pointers
   to relevant literature, software, other sources of information, et
   cetera et cetera. Please see the comp.ai.genetic FAQ for further
   information.

   ------------------------------------------------------------------------

 8. A: What about Fuzzy Logic?
 =============================

   Fuzzy Logic is an area of research based on the work of L.A.
   Zadeh. It is a departure from classical two-valued sets and logic,
   that uses "soft" linguistic (e.g. large, hot, tall) system variables and
   a continuous range of truth values in the interval [0,1], rather
   than strict binary (True or False) decisions and assignments.

   Fuzzy logic is used where a system is difficult to model exactly
   (but an inexact model is available), is controlled by a human
   operator or expert, or where ambiguity or vagueness is common. A
   typical fuzzy system consists of a rule base, membership functions,
   and an inference procedure.

   Most Fuzzy Logic discussion takes place in the newsgroup
   comp.ai.fuzzy, but there is also some work (and discussion) about
   combining fuzzy logic with Neural Network approaches in
   comp.ai.neural-nets.

   For more details see (for example):

   Klir, G.J. and Folger, T.A.: Fuzzy Sets, Uncertainty, and
   Information Prentice-Hall, Englewood Cliffs, N.J., 1988.
   Kosko, B.: Neural Networks and Fuzzy Systems Prentice Hall,
   Englewood Cliffs, NJ, 1992.

------------------------------------------------------------------------

o A: Good introductory literature about Neural
o ============================================
Networks?
=========

0.) The best (subjectively, of course -- please don't flame me):
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++

Haykin, S. (1994). Neural Networks, a Comprehensive Foundation.
Macmillan, New York, NY. "A very readable, well written intermediate
to advanced text on NNs Perspective is primarily one of pattern
recognition, estimation and signal processing. However, there are
well-written chapters on neurodynamics and VLSI implementation.
Though there is emphasis on formal mathematical models of NNs as
universal approximators, statistical estimators, etc., there are also
examples of NNs used in practical applications. The problem sets at the
end of each chapter nicely complement the material. In the bibliography
are over 1000 references. If one buys only one book on neural networks,
this should be it."

Hecht-Nielsen, R. (1990). Neurocomputing. Addison Wesley. Comments:
"A good book", "comprises a nice historical overview and a chapter about
NN hardware. Well structured prose. Makes important concepts clear."

Hertz, J., Krogh, A., and Palmer, R. (1991). Introduction to the Theory of
Neural Computation. Addison-Wesley: Redwood City, California. ISBN
0-201-50395-6 (hardbound) and 0-201-51560-1 (paperbound)
Comments: "My first impression is that this one is by far the best book on
the topic. And it's below $30 for the paperback."; "Well written,
theoretical (but not overwhelming)"; It provides a good balance of model
development, computational algorithms, and applications. The
mathematical derivations are especially well done"; "Nice mathematical
analysis on the mechanism of different learning algorithms"; "It is NOT
for mathematical beginner. If you don't have a good grasp of higher level
math, this book can be really tough to get through."

Masters,Timothy (1994). Practical Neural Network Recipes in C++.
Academic Press, ISBN 0-12-479040-2, US $45 incl. disks. "Lots of very
good practical advice which most other books lack."

1.) Books for the beginner:
+++++++++++++++++++++++++++

Aleksander, I. and Morton, H. (1990). An Introduction to Neural
Computing. Chapman and Hall. (ISBN 0-412-37780-2). Comments:
"This book seems to be intended for the first year of university
education."

Beale, R. and Jackson, T. (1990). Neural Computing, an Introduction.
Adam Hilger, IOP Publishing Ltd : Bristol. (ISBN 0-85274-262-2).
Comments: "It's clearly written. Lots of hints as to how to get the
adaptive models covered to work (not always well explained in the
original sources). Consistent mathematical terminology. Covers
perceptrons, error-backpropagation, Kohonen self-org model, Hopfield
type models, ART, and associative memories."

Dayhoff, J. E. (1990). Neural Network Architectures: An Introduction.
Van Nostrand Reinhold: New York. Comments: "Like Wasserman's
book, Dayhoff's book is also very easy to understand".

Fausett, L. V. (1994. Fundamentals of Neural Networks: Architectures,
Algorithms and Applications, Prentice Hall, ISBN 0-13-334186-0. Also
published as a Prentice Hall International Edition, ISBN 0-13-042250-9.
Sample softeware (source code listings in C and Fortran) is included in
an Instructor's Manual. "Intermediate in level between Wasserman and
Hertz/Krogh/Palmer. Algorithms for a broad range of neural networks,
including a chapter on Adaptive Resonace Theory with ART2. Simple
examples for each network."

McClelland, J. L. and Rumelhart, D. E. (1988). Explorations in Parallel
Distributed Processing: Computational Models of Cognition and
Perception (software manual). The MIT Press. Comments: "Written in a
tutorial style, and includes 2 diskettes of NN simulation programs that
can be compiled on MS-DOS or Unix (and they do too !)"; "The
programs are pretty reasonable as an introduction to some of the things
that NNs can do."; "There are *two* editions of this book. One comes
with disks for the IBM PC, the other comes with disks for the
Macintosh".

McCord Nelson, M. and Illingworth, W.T. (1990). A Practical Guide to
Neural Nets. Addison-Wesley Publishing Company, Inc. (ISBN
0-201-52376-0). Comments: "No formulas at all"; "It does not have
much detailed model development (very few equations), but it does
present many areas of application. It includes a chapter on current areas
of research. A variety of commercial applications is discussed in chapter
1. It also includes a program diskette with a fancy graphical interface
(unlike the PDP diskette)".

Muller, B. and Reinhardt, J. (1990). Neural Networks, An Introduction.
Springer-Verlag: Berlin Heidelberg New York (ISBN: 3-540-52380-4
and 0-387-52380-4). Comments: The book was developed out of a
course on neural-network models with computer demonstrations that
was taught by the authors to Physics students. The book comes together
with a PC-diskette. The book is divided into three parts: (1) Models of
Neural Networks; describing several architectures and learing rules,
including the mathematics. (2) Statistical Physiscs of Neural Networks;
"hard-core" physics section developing formal theories of stochastic
neural networks. (3) Computer Codes; explanation about the
demonstration programs. First part gives a nice introduction into neural
networks together with the formulas. Together with the demonstration
programs a 'feel' for neural networks can be developed.

Orchard, G.A. & Phillips, W.A. (1991). Neural Computation: A
Beginner's Guide. Lawrence Earlbaum Associates: London. Comments:
"Short user-friendly introduction to the area, with a non-technical
flavour. Apparently accompanies a software package, but I haven't seen
that yet".

Rao, V.B & H.V. (1993). C++ Neural Networks and Fuzzy Logic.
MIS:Press, ISBN 1-55828-298-x, US $45 incl. disks. "Probably not
'leading edge' stuff but detailed enough to get your hands dirty!"

Wasserman, P. D. (1989). Neural Computing: Theory & Practice. Van
Nostrand Reinhold: New York. (ISBN 0-442-20743-3) Comments:
"Wasserman flatly enumerates some common architectures from an
engineer's perspective ('how it works') without ever addressing the
underlying fundamentals ('why it works') - important basic concepts
such as clustering, principal components or gradient descent are not
treated. It's also full of errors, and unhelpful diagrams drawn with what
appears to be PCB board layout software from the '70s. For anyone who
wants to do active research in the field I consider it quite inadequate";
"Okay, but too shallow"; "Quite easy to understand"; "The best bedtime
reading for Neural Networks. I have given this book to numerous
collegues who want to know NN basics, but who never plan to implement
anything. An excellent book to give your manager."

Wasserman, P.D. (1993). Advanced Methods in Neural Computing. Van
Nostrand Reinhold: New York (ISBN: 0-442-00461-3). Comments:
Several neural network topics are discussed e.g. Probalistic Neural
Networks, Backpropagation and beyond, neural control, Radial Basis
Function Networks, Neural Engineering. Furthermore, several subjects
related to neural networks are mentioned e.g. genetic algorithms, fuzzy
logic, chaos. Just the functionality of these subjects is described; enough
to get you started. Lots of references are given to more elaborate
descriptions. Easy to read, no extensive mathematical background
necessary.

2.) The classics:
+++++++++++++++++

Kohonen, T. (1984). Self-organization and Associative Memory.
Springer-Verlag: New York. (2nd Edition: 1988; 3rd edition: 1989).
Comments: "The section on Pattern mathematics is excellent."

Rumelhart, D. E. and McClelland, J. L. (1986). Parallel Distributed
Processing: Explorations in the Microstructure of Cognition (volumes 1
& 2). The MIT Press. Comments: "As a computer scientist I found the
two Rumelhart and McClelland books really heavy going and definitely
not the sort of thing to read if you are a beginner."; "It's quite readable,
and affordable (about $65 for both volumes)."; "THE Connectionist
bible".

3.) Introductory journal articles:
++++++++++++++++++++++++++++++++++

Hinton, G. E. (1989). Connectionist learning procedures. Artificial
Intelligence, Vol. 40, pp. 185--234. Comments: "One of the better neural
networks overview papers, although the distinction between network
topology and learning algorithm is not always very clear. Could very well
be used as an introduction to neural networks."

Knight, K. (1990). Connectionist, Ideas and Algorithms. Communications
of the ACM. November 1990. Vol.33 nr.11, pp 59-74. Comments:"A good
article, while it is for most people easy to find a copy of this journal."

Kohonen, T. (1988). An Introduction to Neural Computing. Neural
Networks, vol. 1, no. 1. pp. 3-16. Comments: "A general review".

4.) Not-quite-so-introductory literature:
+++++++++++++++++++++++++++++++++++++++++

Anderson, J. A. and Rosenfeld, E. (Eds). (1988). Neurocomputing:
Foundations of Research. The MIT Press: Cambridge, MA. Comments:
"An expensive book, but excellent for reference. It is a collection of
reprints of most of the major papers in the field."

Anderson, J. A., Pellionisz, A. and Rosenfeld, E. (Eds). (1990).
Neurocomputing 2: Directions for Research. The MIT Press: Cambridge,
MA. Comments: "The sequel to their well-known Neurocomputing
book."

Caudill, M. and Butler, C. (1990). Naturally Intelligent Systems. MIT
Press: Cambridge, Massachusetts. (ISBN 0-262-03156-6). Comments:
"I guess one of the best books I read"; "May not be suited for people who
want to do some research in the area".

Cichocki, A. and Unbehauen, R. (1994). Neural Networks for
Optimization and Signal Processing. John Wiley & Sons, West Sussex,
England, 1993, ISBN 0-471-930105 (hardbound), 526 pages, $57.95.
"Partly a textbook and partly a research monograph; introduces the basic
concepts, techniques, and models related to neural networks and
optimization, excluding rigorous mathematical details. Accessible to a
wide readership with a differential calculus background. The main
coverage of the book is on recurrent neural networks with continuous
state variables. The book title would be more appropriate without
mentioning signal processing. Well edited, good illustrations."

Khanna, T. (1990). Foundations of Neural Networks. Addison-Wesley:
New York. Comments: "Not so bad (with a page of erroneous formulas
(if I remember well), and #hidden layers isn't well described).";
"Khanna's intention in writing his book with math analysis should be
commended but he made several mistakes in the math part".

Kung, S.Y. (1993). Digital Neural Networks, Prentice Hall, Englewood
Cliffs, NJ.

Levine, D. S. (1990). Introduction to Neural and Cognitive Modeling.
Lawrence Erlbaum: Hillsdale, N.J. Comments: "Highly recommended".

Lippmann, R. P. (April 1987). An introduction to computing with neural
nets. IEEE Acoustics, Speech, and Signal Processing Magazine. vol. 2, no.
4, pp 4-22. Comments: "Much acclaimed as an overview of neural
networks, but rather inaccurate on several points. The categorization into
binary and continuous- valued input neural networks is rather arbitrary,
and may work confusing for the unexperienced reader. Not all networks
discussed are of equal importance."

Maren, A., Harston, C. and Pap, R., (1990). Handbook of Neural
Computing Applications. Academic Press. ISBN: 0-12-471260-6. (451
pages) Comments: "They cover a broad area"; "Introductory with
suggested applications implementation".

Pao, Y. H. (1989). Adaptive Pattern Recognition and Neural Networks
Addison-Wesley Publishing Company, Inc. (ISBN 0-201-12584-6)
Comments: "An excellent book that ties together classical approaches to
pattern recognition with Neural Nets. Most other NN books do not even
mention conventional approaches."

Rumelhart, D. E., Hinton, G. E. and Williams, R. J. (1986). Learning
representations by back-propagating errors. Nature, vol 323 (9 October),
pp. 533-536. Comments: "Gives a very good potted explanation of
backprop NN's. It gives sufficient detail to write your own NN
simulation."

Simpson, P. K. (1990). Artificial Neural Systems: Foundations,
Paradigms, Applications and Implementations. Pergamon Press: New
York. Comments: "Contains a very useful 37 page bibliography. A large
number of paradigms are presented. On the negative side the book is very
shallow. Best used as a complement to other books".

Zeidenberg. M. (1990). Neural Networks in Artificial Intelligence. Ellis
Horwood, Ltd., Chichester. Comments: "Gives the AI point of view".

Zornetzer, S. F., Davis, J. L. and Lau, C. (1990). An Introduction to
Neural and Electronic Networks. Academic Press. (ISBN
0-12-781881-2) Comments: "Covers quite a broad range of topics
(collection of articles/papers )."; "Provides a primer-like introduction and
overview for a broad audience, and employs a strong interdisciplinary
emphasis".

------------------------------------------------------------------------

o A: Any journals and magazines about Neural
o ==========================================
Networks?
=========

[to be added: comments on speed of reviewing and publishing,
              whether they accept TeX format or ASCII by e-mail, etc.]

A. Dedicated Neural Network Journals:
+++++++++++++++++++++++++++++++++++++

Title:   Neural Networks
Publish: Pergamon Press
Address: Pergamon Journals Inc., Fairview Park, Elmsford,
         New York 10523, USA and Pergamon Journals Ltd.
         Headington Hill Hall, Oxford OX3, 0BW, England
Freq.:   10 issues/year (vol. 1 in 1988)
Cost/Yr: Free with INNS or JNNS or ENNS membership ($45?),
         Individual $65, Institution $175
ISSN #:  0893-6080
Remark:  Official Journal of International Neural Network Society (INNS),
         European Neural Network Society (ENNS) and Japanese Neural
         Network Society (JNNS).
         Contains Original Contributions, Invited Review Articles, Letters
         to Editor, Book Reviews, Editorials, Announcements, Software Surveys.

Title:   Neural Computation
Publish: MIT Press
Address: MIT Press Journals, 55 Hayward Street Cambridge,
         MA 02142-9949, USA, Phone: [phone]
Freq.:   Quarterly (vol. 1 in 1989)
Cost/Yr: Individual $45, Institution $90, Students $35; Add $9 Outside USA
ISSN #:  0899-7667
Remark:  Combination of Reviews (10,000 words), Views (4,000 words)
         and Letters (2,000 words).  I have found this journal to be of
         outstanding quality.
         (Note: Remarks supplied by Mike Plonski "[e-mail]")

Title:   IEEE Transactions on Neural Networks
Publish: Institute of Electrical and Electronics Engineers (IEEE)
Address: IEEE Service Cemter, 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ,
         08855-1331 USA. Tel: [phone]
Cost/Yr: $10 for Members belonging to participating IEEE societies
Freq.:   Quarterly (vol. 1 in March 1990)
Remark:  Devoted to the science and technology of neural networks
         which disclose significant  technical knowledge, exploratory
         developments and applications of neural networks from biology to
         software to hardware.  Emphasis is on artificial neural networks.
         Specific aspects include self organizing systems, neurobiological
         connections, network dynamics and architecture, speech recognition,
         electronic and photonic implementation, robotics and controls.
         Includes Letters concerning new research results.
         (Note: Remarks are from journal announcement)

Title:   International Journal of Neural Systems
Publish: World Scientific Publishing
Address: USA: World Scientific Publishing Co., 687 Hartwell Street, Teaneck,
         NJ 07666. Tel: [phone]; Eurpoe: World Scientific Publishing
         Co. Pte. Ltd., 73 Lynton Mead, Totteridge, London N20-8DH, England.
         Tel: [phone]; Other: World Scientific Publishing Co. Pte. Ltd.,
         Farrer Road, P.O. Box 128, Singapore 9128. Tel: [phone]
Freq.:   Quarterly (Vol. 1 in 1990?)
Cost/Yr: Individual $42, Institution $88 (plus $9-$17 for postage)
ISSN #:  0129-0657 (IJNS)
Remark:  The International Journal of Neural Systems is a quarterly
         journal which covers information processing in natural
         and artificial neural systems.
         It publishes original contributions on all aspects of this
         broad subject which involves physics, biology, psychology, computer
         science and engineering. Contributions include research papers,
         reviews and short communications.  The journal presents a fresh
         undogmatic attitude towards this multidisciplinary field with the
         aim to be a forum for novel ideas and improved understanding of
         collective and cooperative phenomena with computational
         capabilities.
         (Note: Remarks supplied by B. Lautrup (editor),
         "[e-mail]" )
         Review is reported to be very slow.

Title:   International Journal of Neurocomputing
Publish: Elsevier Science Publishers, Journal Dept.; PO Box 211;
         1000 AE Amsterdam, The Netherlands
Freq.:   Quarterly (vol. 1 in 1989)
Editor:  V.D. Sanchez A.; German Aerospace Research Establishment;
         Institute for Robotics and System Dynamics, 82230 Wessling, Germany.
         Current events and software news editor: Dr. F. Murtagh, ESA,
         Karl-Schwarzschild Strasse 2, D-85748, Garching, Germany,
         phone [phone], fax [phone], email [e-mail]

Title:   Neural Processing Letters
Publish: D facto publications
Address: 45 rue Masui; B-1210 Brussels, Belgium
         Phone: [phone];  Fax: [phone]
Freq:    6 issues/year (vol. 1 in September 1994)
Cost/Yr: BEF 4400 (about $140)
ISSN #:  1370-4621
Remark:  The aim of the journal is to rapidly publish new ideas, original
         developments and work in progress.  Neural Processing Letters
         covers all aspects of the Artificial Neural Networks field.
         Publication delay is about 3 months.
         FTP server available:
          ftp://ftp.dice.ucl.ac.be/pub/neural-nets/NPL.
         WWW server available:
            http://www.dice.ucl.ac.be/neural-nets/NPL/NPL.html

Title:   Neural Network News
Publish: AIWeek Inc.
Address: Neural Network News, 2555 Cumberland Parkway, Suite 299,
         Atlanta, GA 30339 USA. Tel: [phone]
Freq.:   Monthly (beginning September 1989)
Cost/Yr: USA and Canada $249, Elsewhere $299
Remark:  Commericial Newsletter

Title:   Network: Computation in Neural Systems
Publish: IOP Publishing Ltd
Address: Europe: IOP Publishing Ltd, Techno House, Redcliffe Way, Bristol
         BS1 6NX, UK; IN USA: American Institute of Physics, Subscriber
         Services 500 Sunnyside Blvd., Woodbury, NY  11797-2999
Freq.:   Quarterly (1st issue 1990)
Cost/Yr: USA: $180,  Europe: 110 pounds
Remark:  Description: "a forum for integrating theoretical and experimental
         findings across relevant interdisciplinary boundaries."  Contents:
         Submitted articles reviewed by two technical referees  paper's
         interdisciplinary format and accessability."  Also Viewpoints and
         Reviews commissioned by the editors, abstracts (with reviews) of
         articles published in other journals, and book reviews.
         Comment: While the price discourages me (my comments are based
         upon a free sample copy), I think that the journal succeeds
         very well.  The highest density of interesting articles I
         have found in any journal.
         (Note: Remarks supplied by [e-mail])

Title:   Connection Science: Journal of Neural Computing,
         Artificial Intelligence and Cognitive Research
Publish: Carfax Publishing
Address: Europe: Carfax Publishing Company, P. O. Box 25, Abingdon,
         Oxfordshire  OX14 3UE, UK.  USA: Carafax Publishing Company,
         85 Ash Street, Hopkinton, MA 01748
Freq.:   Quarterly (vol. 1 in 1989)
Cost/Yr: Individual $82, Institution $184, Institution (U.K.) 74 pounds

Title:   International Journal of Neural Networks
Publish: Learned Information
Freq.:   Quarterly (vol. 1 in 1989)
Cost/Yr: 90 pounds
ISSN #:  0954-9889
Remark:  The journal contains articles, a conference report (at least the
         issue I have), news and a calendar.
         (Note: remark provided by J.R.M. Smits "[e-mail]")

Title:   Concepts in NeuroScience
Publish: World Scientific Publishing
Address: Same Address (?) as for International Journal of Neural Systems
Freq.:   Twice per year (vol. 1 in 1989)
Remark:  Mainly Review Articles(?)
         (Note: remarks by Osamu Saito "[e-mail]")

Title:   Sixth Generation Systems (formerly Neurocomputers)
Publish: Gallifrey Publishing
Address: Gallifrey Publishing, PO Box 155, Vicksburg, Michigan, 49097, USA
         Tel: [phone] fax
Freq.    Monthly (1st issue January, 1987)
ISSN #:  0893-1585
Editor:  Derek F. Stubbs
Cost/Yr: $79 (USA, Canada), US$95 (elsewhere)
Remark:  Runs eight to 16 pages monthly. In 1995 will go to floppy disc-based
publishing with databases +, "the equivalent to 50 pages per issue are
planned." Often focuses on specific topics: e.g., August, 1994 contains two
articles: "Economics, Times Series and the Market," and "Finite Particle
Analysis - [part] II."  Stubbs also directs the company Advanced Forecasting
Technologies. (Remark by Ed Rosenfeld: [e-mail])

Title:   JNNS Newsletter (Newsletter of the Japan Neural Network Society)
Publish: The Japan Neural Network Society
Freq.:   Quarterly (vol. 1 in 1989)
Remark:  (IN JAPANESE LANGUAGE) Official Newsletter of the Japan Neural
         Network Society(JNNS)
         (Note: remarks by Osamu Saito "[e-mail]")

Title:   Neural Networks Today
Remark:  I found this title in a bulletin board of october last year.
         It was a message of Tim Pattison, [e-mail]
         (Note: remark provided by J.R.M. Smits "[e-mail]")

Title:   Computer Simulations in Brain Science

Title:   Internation Journal of Neuroscience

Title:   Neural Network Computation
Remark:  Possibly the same as "Neural Computation"

Title:   Neural Computing and Applications
Freq.:   Quarterly
Publish: Springer Verlag
Cost/yr: 120 Pounds
Remark:  Is the journal of the Neural Computing Applications Forum.
         Publishes original research and other information
         in the field of practical applications of neural computing.

B. NN Related Journals:
+++++++++++++++++++++++

Title:   Complex Systems
Publish: Complex Systems Publications
Address: Complex Systems Publications, Inc., P.O. Box 6149, Champaign,
         IL 61821-8149, USA
Freq.:   6 times per year (1st volume is 1987)
ISSN #:  0891-2513
Cost/Yr: Individual $75, Institution $225
Remark:  Journal COMPLEX SYSTEMS  devotes to rapid publication of research
         on science, mathematics, and engineering of systems with simple
         components but complex overall behavior. Send mail to
         "[e-mail]" for additional info.
         (Remark is from announcement on Net)

Title:   Biological Cybernetics (Kybernetik)
Publish: Springer Verlag
Remark:  Monthly (vol. 1 in 1961)

Title:   Various IEEE Transactions and Magazines
Publish: IEEE
Remark:  Primarily see IEEE Trans. on System, Man and Cybernetics;
         Various Special Issues: April 1990 IEEE Control Systems
         Magazine.; May 1989 IEEE Trans. Circuits and Systems.;
         July 1988 IEEE Trans. Acoust. Speech Signal Process.

Title:   The Journal of Experimental and Theoretical Artificial Intelligence
Publish: Taylor & Francis, Ltd.
Address: London, New York, Philadelphia
Freq.:   ? (1st issue Jan 1989)
Remark:  For submission information, please contact either of the editors:
         Eric Dietrich                        Chris Fields
         PACSS - Department of Philosophy     Box 30001/3CRL
         SUNY Binghamton                      New Mexico State University
         Binghamton, NY 13901                 Las Cruces, NM 88003-0001
         [e-mail]  [e-mail]

Title:   The Behavioral and Brain Sciences
Publish: Cambridge University Press
Remark:  (Expensive as hell, I'm sure.)
         This is a delightful journal that encourages discussion on a
         variety of controversial topics.  I have especially enjoyed
         reading some papers in there by Dana Ballard and Stephen
         Grossberg (separate papers, not collaborations) a few years
         back.  They have a really neat concept: they get a paper,
         then invite a number of noted scientists in the field to
         praise it or trash it.  They print these commentaries, and
         give the author(s) a chance to make a rebuttal or
         concurrence.  Sometimes, as I'm sure you can imagine, things
         get pretty lively.  I'm reasonably sure they are still at
         it--I think I saw them make a call for reviewers a few
         months ago.  Their reviewers are called something like
         Behavioral and Brain Associates, and I believe they have to
         be nominated by current associates, and should be fairly
         well established in the field.  That's probably more than I
         really know about it but maybe if you post it someone who
         knows more about it will correct any errors I have made.
         The main thing is that I liked the articles I read. (Note:
         remarks by Don Wunsch )

Title:   International Journal of Applied Intelligence
Publish: Kluwer Academic Publishers
Remark:  first issue in 1990(?)

Title:   Bulletin of Mathematica Biology

Title:   Intelligence

Title:   Journal of Mathematical Biology

Title:   Journal of Complex System

Title:   AI Expert
Publish: Miller Freeman Publishing Co., for subscription call ++415-267-7672.
Remark:  Regularly includes ANN related articles, product
         announcements, and application reports. Listings of ANN
         programs are available on AI Expert affiliated BBS's

Title:   International Journal of Modern Physics C
Publish: World Scientific Publ. Co.
         Farrer Rd. P.O.Box 128, Singapore 9128
         or: 687 Hartwell St., Teaneck, N.J. 07666 U.S.A
         or: 73 Lynton Mead, Totteridge, London N20 8DH, England
Freq:    published quarterly
Eds:     G. Fox, H. Herrmann and K. Kaneko

Title:   Machine Learning
Publish: Kluwer Academic Publishers
Address: Kluwer Academic Publishers
         P.O. Box 358
         Accord Station
         Hingham, MA 02018-0358 USA
Freq.:   Monthly (8 issues per year; increasing to 12 in 1993)
Cost/Yr: Individual $140 (1992); Member of AAAI or CSCSI $88
Remark:  Description: Machine Learning is an international forum for
         research on computational approaches to learning.  The journal
         publishes articles reporting substantive research results on a
         wide range of learning methods applied to a variety of task
         domains.  The ideal paper will make a theoretical contribution
         supported by a computer implementation.
         The journal has published many key papers in learning theory,
         reinforcement learning, and decision tree methods.  Recently
         it has published a special issue on connectionist approaches
         to symbolic reasoning.  The journal regularly publishes
         issues devoted to genetic algorithms as well.

Title:   INTELLIGENCE - The Future of Computing
Published by: Intelligence
Address: INTELLIGENCE, P.O. Box 20008, New York, NY 10025-1510, USA,
212-222-1123 voice & fax; email: [e-mail], CIS: 72400,1013
Freq.    Monthly plus four special reports each year (1st issue: May, 1984)
ISSN #:  1042-4296
Editor:  Edward Rosenfeld
Cost/Yr: $395 (USA), US$450 (elsewhere)
Remark:  Has absorbed several other newsletters, like Synapse/Connection
         and Critical Technology Trends (formerly AI Trends).
         Covers NN, genetic algorithms, fuzzy systems, wavelets, chaos
         and other advanced computing approaches, as well as molecular
         computing and nanotechnology.

Title:   Journal of Physics A: Mathematical and General
Publish: Inst. of Physics, Bristol
Freq:    24 issues per year.
Remark:  Statistical mechanics aspects of neural networks
         (mostly Hopfield models).

Title:   Physical Review A: Atomic, Molecular and Optical Physics
Publish: The American Physical Society (Am. Inst. of Physics)
Freq:    Monthly
Remark:  Statistical mechanics of neural networks.

C. Journals loosely related to NNs:
+++++++++++++++++++++++++++++++++++

Title:   JOURNAL OF COMPLEXITY
Remark:  (Must rank alongside Wolfram's Complex Systems)

Title:   IEEE ASSP Magazine
Remark:  (April 1987 had the Lippmann intro. which everyone likes to cite)

Title:   ARTIFICIAL INTELLIGENCE
Remark:  (Vol 40, September 1989 had the survey paper by Hinton)

Title:   COGNITIVE SCIENCE
Remark:  (the Boltzmann machine paper by Ackley et al appeared here
         in Vol 9, 1983)

Title:   COGNITION
Remark:  (Vol 28, March 1988 contained the Fodor and Pylyshyn
         critique of connectionism)

Title:   COGNITIVE PSYCHOLOGY
Remark:  (no comment!)

Title:   JOURNAL OF MATHEMATICAL PSYCHOLOGY
Remark:  (several good book reviews)

------------------------------------------------------------------------

o A: The most important conferences concerned with
o ================================================
Neural Networks?
================

[to be added: has taken place how often yet; most emphasized topics;
 where to get proceedings/calls-for-papers etc. ]

A. Dedicated Neural Network Conferences:
++++++++++++++++++++++++++++++++++++++++

 1. Neural Information Processing Systems (NIPS) Annually since
   1988 in Denver, Colorado; late November or early December.
   Interdisciplinary conference with computer science, physics,
   engineering, biology, medicine, cognitive science topics. Covers all
   aspects of NNs. Proceedings appear several months after the
   conference as a book from Morgan Kaufman, San Mateo, CA.
 2. International Joint Conference on Neural Networks (IJCNN)
   formerly co-sponsored by INNS and IEEE, no longer held.
 3. Annual Conference on Neural Networks (ACNN)
 4. International Conference on Artificial Neural Networks (ICANN)
   Annually in Europe. First was 1991. Major conference of
   European Neur. Netw. Soc. (ENNS)
 5. WCNN. Sponsored by INNS.
 6. European Symposium on Artificial Neural Networks (ESANN).
   Anually since 1993 in Brussels, Belgium; late April; conference on
   the fundamental aspects of artificial neural networks: theory,
   mathematics, biology, relations between neural networks and
   other disciplines, statistics, learning, algorithms, models and
   architectures, self-organization, signal processing, approximation
   of functions, evolutive learning, etc. Contact: Michel Verleysen, D
   facto conference services, 45 rue Masui, B-1210 Brussels,
   Belgium, phone: [phone], fax: + 32 2 245 46 94, e-mail:
   [e-mail]
 7. Artificial Neural Networks in Engineering (ANNIE) Anually
   since 1991 in St. Louis, Missouri; held in November. (Topics: NN
   architectures, pattern recognition, neuro-control,
   neuro-engineering systems. Contact: ANNIE; Engineering
   Management Department; 223 Engineering Management
   Building; University of Missouri-Rolla; Rolla, MO 65401; FAX:
   [phone])
 8. many many more....

B. Other Conferences
++++++++++++++++++++

 1. International Joint Conference on Artificial Intelligence (IJCAI)
 2. Intern. Conf. on Acustics, Speech and Signal Processing
   (ICASSP)
 3. Annual Conference of the Cognitive Science Society
 4. [Vision Conferences?]

C. Pointers to Conferences
++++++++++++++++++++++++++

 1. The journal "Neural Networks" has a list of conferences,
   workshops and meetings in each issue. This is quite
   interdisciplinary.
 2. There is a regular posting on comp.ai.neural-nets from Paultje
   Bakker: "Upcoming Neural Network Conferences", which lists
   names, dates, locations, contacts, and deadlines. It is also available
   for anonymous ftp from ftp.cs.uq.oz.au as /pub/pdp/conferences

------------------------------------------------------------------------

o A: Neural Network Associations?
o ===============================

 1. International Neural Network Society (INNS).
 +++++++++++++++++++++++++++++++++++++++++++++++

   INNS membership includes subscription to "Neural Networks",
   the official journal of the society. Membership is $55 for
   non-students and $45 for students per year. Address: INNS
   Membership, P.O. Box 491166, Ft. Washington, MD 20749.

 2. International Student Society for Neural Networks (ISSNNets).
 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++

   Membership is $5 per year. Address: ISSNNet, Inc., P.O. Box
   15661, Boston, MA 02215 USA

 3. Women In Neural Network Research and technology
 ++++++++++++++++++++++++++++++++++++++++++++++++++
   (WINNERS).
   ++++++++++

   Address: WINNERS, c/o Judith Dayhoff, 11141 Georgia Ave.,
   Suite 206, Wheaton, MD 20902. Phone: [phone].

 4. European Neural Network Society (ENNS)
 +++++++++++++++++++++++++++++++++++++++++

   ENNS membership includes subscription to "Neural Networks",
   the official journal of the society. Membership is currently (1994)
   50 UK pounds (35 UK pounds for students) per year. Address:
   ENNS Membership, Centre for Neural Networks, King's College
   London, Strand, London WC2R 2LS, United Kingdom.

 5. Japanese Neural Network Society (JNNS)
 +++++++++++++++++++++++++++++++++++++++++

   Address: Japanese Neural Network Society; Department of
   Engineering, Tamagawa University; 6-1-1, Tamagawa Gakuen,
   Machida City, Tokyo; 194 JAPAN; Phone: [phone], Fax:
   [phone]

 6. Association des Connexionnistes en THese (ACTH)
 ++++++++++++++++++++++++++++++++++++++++++++++++++

   (the French Student Association for Neural Networks);
   Membership is 100 FF per year; Activities : newsletter, conference
   (every year), list of members, electronic forum; Journal 'Valgo'
   (ISSN 1243-4825); Contact : [e-mail]

 7. Neurosciences et Sciences de l'Ingenieur (NSI)
 +++++++++++++++++++++++++++++++++++++++++++++++++

   Biology & Computer Science Activity : conference (every year)
   Address : NSI - TIRF / INPG 46 avenue Felix Viallet 38031
   Grenoble Cedex FRANCE

------------------------------------------------------------------------

o A: Other sources of information about NNs?
o ==========================================

 1. Neuron Digest
 ++++++++++++++++

   Internet Mailing List. From the welcome blurb: "Neuron-Digest
   is a list (in digest form) dealing with all aspects of neural
   networks (and any type of network or neuromorphic system)" To
   subscribe, send email to [e-mail]
   comp.ai.neural-net readers also find the messages in that
   newsgroup in the form of digests.

 2. Usenet groups comp.ai.neural-nets (Oha!) and
 +++++++++++++++++++++++++++++++++++++++++++++++
   comp.theory.self-org-sys.
   +++++++++++++++++++++++++

   There is a periodic posting on comp.ai.neural-nets sent by
   [e-mail] (Gregory Aharonian) about Neural
   Network patents.

 3. Central Neural System Electronic Bulletin Board
 ++++++++++++++++++++++++++++++++++++++++++++++++++

   Modem: 409-589-3338; Sysop: Wesley R. Elsberry; P.O. Box
   4201, College Station, TX 77843; [e-mail] Many
   MS-DOS PD and shareware simulations, source code,
   benchmarks, demonstration packages, information files; some
   Unix, Macintosh, Amiga related files. Also available are files on
   AI, AI Expert listings 1986-1991, fuzzy logic, genetic algorithms,
   artificial life, evolutionary biology, and many Project Gutenberg
   and Wiretap etexts. No user fees have ever been charged. Home of
   the NEURAL_NET Echo, available thrugh FidoNet, RBBS-Net,
   and other EchoMail compatible bulletin board systems.

 4. Neural ftp archive site ftp.funet.fi
 +++++++++++++++++++++++++++++++++++++++

   Is administrating a large collection of neural network papers and
   software at the Finnish University Network file archive site
   ftp.funet.fi in directory /pub/sci/neural Contains all the public
   domain software and papers that they have been able to find. All
   of these files have been transferred from FTP sites in U.S. and are
   mirrored about every 3 months at fastest. Contact:
   [e-mail]

 5. USENET newsgroup comp.org.issnnet
 ++++++++++++++++++++++++++++++++++++

   Forum for discussion of academic/student-related issues in NNs,
   as well as information on ISSNNet (see answer 12) and its
   activities.

 6. AI CD-ROM
 ++++++++++++

   Network Cybernetics Corporation produces the "AI CD-ROM". It
   is an ISO-9660 format CD-ROM and contains a large
   assortment of software related to artificial intelligence, artificial
   life, virtual reality, and other topics. Programs for OS/2,
   MS-DOS, Macintosh, UNIX, and other operating systems are
   included. Research papers, tutorials, and other text files are
   included in ASCII, RTF, and other universal formats. The files
   have been collected from AI bulletin boards, Internet archive sites,
   University computer deptartments, and other government and
   civilian AI research organizations. Network Cybernetics
   Corporation intends to release annual revisions to the AI
   CD-ROM to keep it up to date with current developments in the
   field. The AI CD-ROM includes collections of files that address
   many specific AI/AL topics including Neural Networks (Source
   code and executables for many different platforms including Unix,
   DOS, and Macintosh. ANN development tools, example networks,
   sample data, tutorials. A complete collection of Neural Digest is
   included as well.) The AI CD-ROM may be ordered directly by
   check, money order, bank draft, or credit card from: Network
   Cybernetics Corporation; 4201 Wingren Road Suite 202; Irving,
   TX 75062-2763; Tel [phone]; Fax [phone]; The cost
   is $129 per disc + shipping ($5/disc domestic or $10/disc foreign)
   (See the comp.ai FAQ for further details)

 7. World Wide Web
 +++++++++++++++++

   In World-Wide-Web (WWW, for example via the xmosaic
   program) you can read neural network information by opening
   one of the following universal resource locators (URLs):
   http://www.neuronet.ph.kcl.ac.uk (NEuroNet, King's College,
   London), http://www.eeb.ele.tue.nl (Eindhoven, Netherlands),
   http://www.msrc.pnl.gov:2080/docs/cie/neural/neural.homepage.html
   (Richland, Washington),
   http://www.cosy.sbg.ac.at/~rschwaig/rschwaig/projects.html
   (Salzburg, Austria),
   http://http2.sils.umich.edu/Public/nirg/nirg1.html (Michigan).

 8. Neurosciences Internet Resource Guide
 ++++++++++++++++++++++++++++++++++++++++

   This document aims to be a guide to existing, free,
   Internet-accessible resources helpful to neuroscientists of all
   stripes. An ASCII text version (86K) is available in the
   Clearinghouse of Subject-Oriented Internet Resource Guides as
   follows:

   anonymous FTP, Gopher, WWW Hypertext

 9. INTCON mailing list
 ++++++++++++++++++++++

   INTCON (Intelligent Control) is a moderated mailing list set up
   to provide a forum for communication and exchange of ideas
   among researchers in neuro-control, fuzzy logic control,
   reinforcement learning and other related subjects grouped under
   the topic of intelligent control. Send your subscribe requests to
   [e-mail]

------------------------------------------------------------------------

o A: Freely available software packages for NN
o ============================================
simulation?
===========

 1. Rochester Connectionist Simulator
 ++++++++++++++++++++++++++++++++++++

   A quite versatile simulator program for arbitrary types of neural
   nets. Comes with a backprop package and a X11/Sunview
   interface. Available via anonymous FTP from cs.rochester.edu
   [192.5.53.209] in directory pub/simulator as the files README (8
   KB), rcs_v4.2.justdoc.tar.Z (1.6 MB, Documentation),
   rcs_v4.2.justsrc.tar.Z (1.4 MB, Source code),

 2. UCLA-SFINX
 +++++++++++++

   ftp retina.cs.ucla.edu [131.179.16.6]; Login name: sfinxftp;
   Password: joshua; directory: pub; files : README;
   sfinx_v2.0.tar.Z; Email info request : [e-mail]

 3. NeurDS
 +++++++++

   simulator for DEC systems supporting VT100 terminal. available
   for anonymous ftp from gatekeeper.dec.com [16.1.0.2] in directory:
   pub/DEC as the file NeurDS031.tar.Z (111 Kb)

 4. PlaNet5.7 (formerly known as SunNet)
 +++++++++++++++++++++++++++++++++++++++

   A popular connectionist simulator with versions to run under X
   Windows, and non-graphics terminals created by Yoshiro Miyata
   (Chukyo Univ., Japan). 60-page User's Guide in Postscript. Send
   any questions to [e-mail] Available for
   anonymous ftp from ftp.ira.uka.de as /pub/neuron/PlaNet5.7.tar.Z
   (800 kb) or from boulder.colorado.edu [128.138.240.1] as
   /pub/generic-sources/PlaNet5.7.tar.Z

 5. GENESIS
 ++++++++++

   GENESIS 1.4.1 (GEneral NEural SImulation System) is a general
   purpose simulation platform which was developed to support the
   simulation of neural systems ranging from complex models of
   single neurons to simulations of large networks made up of more
   abstract neuronal components. Most current GENESIS
   applications involve realistic simulations of biological neural
   systems. Although the software can also model more abstract
   networks, other simulators are more suitable for backpropagation
   and similar connectionist modeling. Available for ftp from
   genesis.cns.caltech.edu [131.215.137.64]. Use 'telnet' to
   genesis.cns.caltech.edu beforehands and login as the user "genesis"
   (no password required). If you answer all the questions asked of
   you an 'ftp' account will automatically be created for you. You
   can then 'ftp' back to the machine and download the software (ca.
   3 MB). Contact: [e-mail].

 6. Mactivation
 ++++++++++++++

   A neural network simulator for the Apple Macintosh. Available
   for ftp from ftp.cs.colorado.edu [128.138.243.151] as
   /pub/cs/misc/Mactivation-3.3.sea.hqx

 7. Cascade Correlation Simulator
 ++++++++++++++++++++++++++++++++

   A simulator for Scott Fahlman's Cascade Correlation algorithm.
   Available for ftp from ftp.cs.cmu.edu [128.2.206.173] in directory
   /afs/cs/project/connect/code as the file cascor-v1.0.4.shar (218 KB)
   There is also a version of recurrent cascade correlation in the
   same directory in file rcc1.c (108 KB).

 8. Quickprop
 ++++++++++++

   A variation of the back-propagation algorithm developed by Scott
   Fahlman. A simulator is available in the same directory as the
   cascade correlation simulator above in file nevprop1.16.shar (137
   KB) (see also the description of NEVPROP below)

 9. DartNet
 ++++++++++

   DartNet is a Macintosh-based backpropagation simulator,
   developed at Dartmouth by Jamshed Bharucha and Sean Nolan as
   a pedagogical tool. It makes use of the Mac's graphical interface,
   and provides a number of tools for building, editing, training,
   testing and examining networks. This program is available by
   anonymous ftp from dartvax.dartmouth.edu [129.170.16.4] as
   /pub/mac/dartnet.sit.hqx (124 KB).

 10. SNNS
 ++++++++

   "Stuttgart Neural Network Simulator" from the University of
   Stuttgart, Germany. A luxurious simulator for many types of nets;
   with X11 interface: Graphical 2D and 3D topology
   editor/visualizer, training visualisation, multiple pattern set
   handling etc. Currently supports backpropagation (vanilla, online,
   with momentum term and flat spot elimination, batch, time
   delay), counterpropagation, quickprop, backpercolation 1,
   generalized radial basis functions (RBF), RProp, ART1, ART2,
   ARTMAP, Cascade Correlation, Recurrent Cascade Correlation,
   Dynamic LVQ, Backpropagation through time (for recurrent
   networks), batch backpropagation through time (for recurrent
   networks), Quickpropagation through time (for recurrent
   networks), Hopfield networks, Jordan and Elman networks,
   autoassociative memory, self-organizing maps, time-delay
   networks (TDNN), and is user-extendable (user-defined
   activation functions, output functions, site functions, learning
   procedures). Works on SunOS, Solaris, IRIX, Ultrix, AIX,
   HP/UX, and Linux. Available for ftp from
   ftp.informatik.uni-stuttgart.de [129.69.211.2] in directory
   /pub/SNNS as SNNSv3.2.tar.Z (2 MB, Source code) and
   SNNSv3.2.Manual.ps.Z (1.4 MB, Documentation). There are also
   various other files in this directory (e.g. the source version of the
   manual, a Sun Sparc executable, older versions of the software,
   some papers, and the software in several smaller parts). It may be
   best to first have a look at the file SNNSv3.2.Readme (10 kb).
   This file contains a somewhat more elaborate short description of
   the simulator.

 11. Aspirin/MIGRAINES
 +++++++++++++++++++++

   Aspirin/MIGRAINES 6.0 consists of a code generator that builds
   neural network simulations by reading a network description
   (written in a language called "Aspirin") and generates a C
   simulation. An interface (called "MIGRAINES") is provided to
   export data from the neural network to visualization tools. The
   system has been ported to a large number of platforms. The goal
   of Aspirin is to provide a common extendible front-end language
   and parser for different network paradigms. The MIGRAINES
   interface is a terminal based interface that allows you to open
   Unix pipes to data in the neural network. Users can display the
   data using either public or commercial graphics/analysis tools.
   Example filters are included that convert data exported through
   MIGRAINES to formats readable by Gnuplot 3.0, Matlab,
   Mathematica, and xgobi. The software is available from two FTP
   sites: from CMU's simulator collection on pt.cs.cmu.edu
   [128.2.254.155] in /afs/cs/project/connect/code/am6.tar.Z and from
   UCLA's cognitive science machine ftp.cognet.ucla.edu
   [128.97.50.19] in /pub/alexis/am6.tar.Z (2 MB).

 12. Adaptive Logic Network kit
 ++++++++++++++++++++++++++++++

   This package differs from the traditional nets in that it uses logic
   functions rather than floating point; for many tasks, ALN's can
   show many orders of magnitude gain in training and performance
   speed. Anonymous ftp from menaik.cs.ualberta.ca [129.128.4.241]
   in directory /pub/atree. See the files README (7 KB),
   atree2.tar.Z (145 kb, Unix source code and examples), atree2.ps.Z
   (76 kb, documentation), a27exe.exe (412 kb, MS-Windows 3.x
   executable), atre27.exe (572 kb, MS-Windows 3.x source code).

 13. NeuralShell
 +++++++++++++++

   Formerly available from FTP site quanta.eng.ohio-state.edu
   [128.146.35.1] as /pub/NeuralShell/NeuralShell.tar". Currently not
   available and undergoing a major reconstruction (April 94).

 14. PDP
 +++++++

   The PDP simulator package is available via anonymous FTP at
   nic.funet.fi [128.214.6.100] as /pub/sci/neural/sims/pdp.tar.Z (202
   kb). The simulator is also available with the book "Explorations in
   Parallel Distributed Processing: A Handbook of Models, Programs,
   and Exercises" by McClelland and Rumelhart. MIT Press, 1988.
   Comment: "This book is often referred to as PDP vol III which is
   a very misleading practice! The book comes with software on an
   IBM disk but includes a makefile for compiling on UNIX systems.
   The version of PDP available at ftp.funet.fi seems identical to the
   one with the book except for a bug in bp.c which occurs when you
   try to run a script of PDP commands using the DO command.
   This can be found and fixed easily."

 15. Xerion
 ++++++++++

   Xerion runs on SGI and Sun machines and uses X Windows for
   graphics. The software contains modules that implement Back
   Propagation, Recurrent Back Propagation, Boltzmann Machine,
   Mean Field Theory, Free Energy Manipulation, Hard and Soft
   Competitive Learning, and Kohonen Networks. Sample networks
   built for each of the modules are also included. Contact:
   [e-mail]. Xerion is available via anonymous ftp
   from ftp.cs.toronto.edu [128.100.1.105] in directory /pub/xerion as
   xerion-3.1.ps.Z (153 kB) and xerion-3.1.tar.Z (1.3 MB) plus
   several concrete simulators built with xerion (about 40 kB each).

 16. Neocognitron simulator
 ++++++++++++++++++++++++++

   The simulator is written in C and comes with a list of references
   which are necessary to read to understand the specifics of the
   implementation. The unsupervised version is coded without (!)
   C-cell inhibition. Available for anonymous ftp from
   unix.hensa.ac.uk [129.12.21.7] in /pub/neocognitron.tar.Z (130 kB).

 17. Multi-Module Neural Computing Environment (MUME)
 ++++++++++++++++++++++++++++++++++++++++++++++++++++

   MUME is a simulation environment for multi-modules neural
   computing. It provides an object oriented facility for the
   simulation and training of multiple nets with various architectures
   and learning algorithms. MUME includes a library of network
   architectures including feedforward, simple recurrent, and
   continuously running recurrent neural networks. Each
   architecture is supported by a variety of learning algorithms.
   MUME can be used for large scale neural network simulations as
   it provides support for learning in multi-net environments. It also
   provide pre- and post-processing facilities. The modules are
   provided in a library. Several "front-ends" or clients are also
   available. X-Window support by editor/visualization tool Xmume.
   MUME can be used to include non-neural computing modules
   (decision trees, ...) in applications. MUME is available anonymous
   ftp on mickey.sedal.su.oz.au [129.78.24.170] after signing and
   sending a licence: /pub/license.ps (67 kb). Contact: Marwan Jabri,
   SEDAL, Sydney University Electrical Engineering, NSW 2006
   Australia, [e-mail]

 18. LVQ_PAK, SOM_PAK
 ++++++++++++++++++++

   These are packages for Learning Vector Quantization and
   Self-Organizing Maps, respectively. They have been built by the
   LVQ/SOM Programming Team of the Helsinki University of
   Technology, Laboratory of Computer and Information Science,
   Rakentajanaukio 2 C, SF-02150 Espoo, FINLAND There are
   versions for Unix and MS-DOS available from cochlea.hut.fi
   [130.233.168.48] as /pub/lvq_pak/lvq_pak-2.1.tar.Z (340 kB, Unix
   sources), /pub/lvq_pak/lvq_p2r1.exe (310 kB, MS-DOS
   self-extract archive), /pub/som_pak/som_pak-1.2.tar.Z (251 kB,
   Unix sources), /pub/som_pak/som_p1r2.exe (215 kB, MS-DOS
   self-extract archive). (further programs to be used with
   SOM_PAK and LVQ_PAK can be found in /pub/utils).

 19. SESAME
 ++++++++++

   ("Software Environment for the Simulation of Adaptive Modular
   Systems") SESAME is a prototypical software implementation
   which facilitates
    o Object-oriented building blocks approach.
    o Contains a large set of C++ classes useful for neural nets,
      neurocontrol and pattern recognition. No C++ classes can
      be used as stand alone, though!
    o C++ classes include CartPole, nondynamic two-robot
      arms, Lunar Lander, Backpropagation, Feature Maps,
      Radial Basis Functions, TimeWindows, Fuzzy Set Coding,
      Potential Fields, Pandemonium, and diverse utility building
      blocks.
    o A kernel which is the framework for the C++ classes and
      allows run-time manipulation, construction, and
      integration of arbitrary complex and hybrid experiments.
    o Currently no graphic interface for construction, only for
      visualization.
    o Platform is SUN4, XWindows
   Unfortunately no reasonable good introduction has been written
   until now. We hope to have something soon. For now we provide
   papers (eg. NIPS-92), a reference manual (>220 pages), source
   code (ca. 35.000 lines of code), and a SUN4-executable by ftp
   only. Sesame and its description is available in various files for
   anonymous ftp on ftp ftp.gmd.de [129.26.8.90] in the directories
   /gmd/as/sesame and /gmd/as/paper. Questions to
   [e-mail]; there is only very limited support
   available.

 20. Nevada Backpropagation (NevProp)
 ++++++++++++++++++++++++++++++++++++

   NevProp is a free, easy-to-use feedforward backpropagation
   (multilayer perceptron) program. It uses an interactive
   character-based interface, and is distributed as C source code that
   should compile and run on most platforms. (Precompiled
   executables are available for Macintosh and DOS.) The original
   version was Quickprop 1.0 by Scott Fahlman, as translated from
   Common Lisp by Terry Regier. We added early-stopped training
   based on a held-out subset of data, c index (ROC curve area)
   calculation, the ability to force gradient descent (per-epoch or
   per-pattern), and additional options. FEATURES (NevProp
   version 1.16): UNLIMITED (except by machine memory) number
   of input PATTERNS; UNLIMITED number of input, hidden, and
   output UNITS; Arbitrary CONNECTIONS among the various
   layers' units; Clock-time or user-specified RANDOM SEED for
   initial random weights; Choice of regular GRADIENT
   DESCENT or QUICKPROP; Choice of PER-EPOCH or
   PER-PATTERN (stochastic) weight updating;
   GENERALIZATION to a test dataset; AUTOMATICALLY
   STOPPED TRAINING based on generalization; RETENTION of
   best-generalizing weights and predictions; Simple but useful
   GRAPHIC display to show smoothness of generalization;
   SAVING of results to a file while working interactively; SAVING
   of weights file and reloading for continued training;
   PREDICTION-only on datasets by applying an existing weights
   file; In addition to RMS error, the concordance, or c index is
   displayed. The c index (area under the ROC curve) shows the
   correctness of the RELATIVE ordering of predictions AMONG
   the cases; ie, it is a measure of discriminative power of the model.
   AVAILABILITY: The most updated version of NevProp will be
   made available by anonymous ftp from the University of Nevada,
   Reno: On ftp.scs.unr.edu [134.197.10.130] in the directory
   "pub/goodman/nevpropdir", e.g. README.FIRST (45 kb) or
   nevprop1.16.shar (138 kb). VERSION 2 to be released in Spring of
   1994 -- some of the new features: more flexible file formatting
   (including access to external data files; option to prerandomize
   data order; randomized stochastic gradient descent; option to
   rescale predictor (input) variables); linear output units as an
   alternative to sigmoidal units for use with continuous-valued
   dependent variables (output targets); cross-entropy (maximum
   likelihood) criterion function as an alternative to square error for
   use with categorical dependent variables
   (classification/symbolic/nominal targets); and interactive interrupt
   to change settings on-the-fly. Limited support is available from
   Phil Goodman ([e-mail]), University of Nevada Center
   for Biomedical Research.

 21. Fuzzy ARTmap
 ++++++++++++++++

   This is just a small example program. Available for anonymous ftp
   from park.bu.edu [128.176.121.56] /pub/fuzzy-artmap.tar.Z (44
   kB).

 22. PYGMALION
 +++++++++++++

   This is a prototype that stems from an ESPRIT project. It
   implements back-propagation, self organising map, and Hopfield
   nets. Avaliable for ftp from ftp.funet.fi [128.214.248.6] as
   /pub/sci/neural/sims/pygmalion.tar.Z (1534 kb). (Original site is
   imag.imag.fr: archive/pygmalion/pygmalion.tar.Z).

 23. Basis-of-AI-backprop
 ++++++++++++++++++++++++

   Earlier versions have been posted in comp.sources.misc and people
   around the world have used them and liked them. This package is
   free for ordinary users but shareware for businesses and
   government agencies ($200/copy, but then for this you get the
   professional version as well). I do support this package via email.
   Some of the highlights are:
    o in C for UNIX and DOS and DOS binaries
    o gradient descent, delta-bar-delta and quickprop
    o extra fast 16-bit fixed point weight version as well as a
      conventional floating point version
    o recurrent networks
    o numerous sample problems
   Available for ftp from ftp.mcs.com in directory /mcsnet.users/drt.
   The expanded professional version is $30/copy for ordinary
   individuals including academics and $200/copy for businesses and
   government agencies (improved user interface, more activation
   functions, networks can be read into your own programs, dynamic
   node creation, weight decay, SuperSAB). More details can be
   found in the documentation for the student version. Contact: Don
   Tveter; 5228 N. Nashville Ave.; Chicago, Illinois 60656;
   [e-mail]

 24. Matrix Backpropagation
 ++++++++++++++++++++++++++

   MBP (Matrix Back Propagation) is a very efficient
   implementation of the back-propagation algorithm for
   current-generation workstations. The algorithm includes a
   per-epoch adaptive technique for gradient descent. All the
   computations are done through matrix multiplications and make
   use of highly optimized C code. The goal is to reach almost
   peak-performances on RISCs with superscalar capabilities and
   fast caches. On some machines (and with large networks) a
   30-40x speed-up can be measured respect to conventional
   implementations. The software is available by anonymous ftp
   from risc6000.dibe.unige.it [130.251.89.154] as /pub/MBPv1.1.tar.Z
   (Unix version), /pub/MBPv11.zip.Z (MS-DOS version),
   /pub/mpbv11.ps (Documentation). For more information, contact
   Davide Anguita or .

 25. WinNN
 +++++++++

   WinNN is a shareware Neural Networks (NN) package for
   windows 3.1. WinNN incorporates a very user friendly interface
   with a powerful computational engine. WinNN is intended to be
   used as a tool for beginners and more advanced neural networks
   users, it provides an alternative to using more expensive and hard
   to use packages. WinNN can implement feed forward
   multi-layered NN and uses a modified fast back-propagation for
   training. Extensive on line help. Has various neuron functions.
   Allows on the fly testing of the network performance and
   generalization. All training parameters can be easily modified
   while WinNN is training. Results can be saved on disk or copied to
   the clipboard. Supports plotting of the outputs and weight
   distribution. Available for ftp from winftp.cica.indiana.edu as
   /pub/pc/win3/programr/winnn093.zip (545 kB).

 26. BIOSIM
 ++++++++++

   BIOSIM is a biologically oriented neural network simulator.
   Public domain, runs on Unix (less powerful PC-version is
   available, too), easy to install, bilingual (german and english), has
   a GUI (Graphical User Interface), designed for research and
   teaching, provides online help facilities, offers controlling
   interfaces, batch version is available, a DEMO is provided.
   REQUIREMENTS (Unix version): X11 Rel. 3 and above, Motif
   Rel 1.0 and above, 12 MB of physical memory, recommended are
   24 MB and more, 20 MB disc space. REQUIREMENTS (PC
   version): PC-compatible with MS Windows 3.0 and above, 4 MB
   of physical memory, recommended are 8 MB and more, 1 MB disc
   space. Four neuron models are implemented in BIOSIM: a simple
   model only switching ion channels on and off, the original
   Hodgkin-Huxley model, the SWIM model (a modified HH model)
   and the Golowasch-Buchholz model. Dendrites consist of a chain
   of segments without bifurcation. A neural network can be created
   by using the interactive network editor which is part of BIOSIM.
   Parameters can be changed via context sensitive menus and the
   results of the simulation can be visualized in observation windows
   for neurons and synapses. Stochastic processes such as noise can
   be included. In addition, biologically orientied learning and
   forgetting processes are modeled, e.g. sensitization, habituation,
   conditioning, hebbian learning and competitive learning. Three
   synaptic types are predefined (an excitatatory synapse type, an
   inhibitory synapse type and an electrical synapse). Additional
   synaptic types can be created interactively as desired. Available for
   ftp from ftp.uni-kl.de [131.246.9.95] in directory /pub/bio/neurobio:
   Get /pub/bio/neurobio/biosim.readme (2 kb) and
   /pub/bio/neurobio/biosim.tar.Z (2.6 MB) for the Unix version or
   /pub/bio/neurobio/biosimpc.readme (2 kb) and
   /pub/bio/neurobio/biosimpc.zip (150 kb) for the PC version.
   Contact: Stefan Bergdoll; Department of Software Engineering
   (ZXA/US); BASF Inc.; D-67056 Ludwigshafen; Germany;
   [e-mail]; phone [phone]; fax
   0621-60-43735

 27. The Brain
 +++++++++++++

   The Brain is an advanced neural network simulator for PCs that
   is simple enough to be used by non-technical people, yet
   sophisticated enough for serious research work. It is based upon
   the backpropagation learning algorithm. Three sample networks
   are included. The documentation included provides you with an
   introduction and overview of the concepts and applications of
   neural networks as well as outlining the features and capabilities
   of The Brain. The Brain requires 512K memory and MS-DOS or
   PC-DOS version 3.20 or later (versions for other OS's and
   machines are available). A 386 (with maths coprocessor) or higher
   is recommended for serious use of The Brain. Shareware payment
   required. Demo version is restricted to number of units the
   network can handle due to memory contraints on PC's. Registered
   version allows use of extra memory. External documentation
   included: 39Kb, 20 Pages. Source included: No (Source comes
   with registration). Available via anonymous ftp from
   ftp.technion.ac.il [132.68.1.10] as
   /pub/unsupported/dos/local/brain12.zip (78 kb) or from
   ftp.tu.clausthal.de [139.174.2.10] as /pub/msdos/misc/brain12.zip
   (78 kb) If you do not have access to anonymous ftp please contact
   me and I will try to email the program to you. Contact: David
   Perkovic; DP Computing; PO Box 712; Noarlunga Center SA
   5168; Australia; Email: [e-mail] (preferred) or
   [e-mail] or [e-mail]

 28. FuNeGen 1.0
 +++++++++++++++

   FuNeGen is a MLP based software program to generate fuzzy rule
   based classifiers. A limited version (maximum of 7 inputs and 3
   membership functions for each input) for PCs is available for
   anonymous ftp from
   obelix.microelectronic.e-technik.th-darmstadt.de in directory
   /pub/neurofuzzy. For further information see the file read.me.
   Contact: Saman K. Halgamuge

 29. NeuDL -- Neural-Network Description Language
 ++++++++++++++++++++++++++++++++++++++++++++++++

   NeuDL is a description language for the design, training, and
   operation of neural networks. It is currently limited to the
   backpropagation neural-network model; however, it offers a great
   deal of flexibility. For example, the user can explicitly specify the
   connections between nodes and can create or destroy connections
   dynamically as training progresses. NeuDL is an interpreted
   language resembling C or C++. It also has instructions dealing
   with training/testing set manipulation as well as neural network
   operation. A NeuDL program can be run in interpreted mode or it
   can be automatically translated into C++ which can be compiled
   and then executed. The NeuDL interpreter is written in C++ and
   can be easly extended with new instructions. NeuDL is available
   from the anonymous ftp site at The University of Alabama:
   cs.ua.edu (130.160.44.1) in the file /pub/neudl/NeuDLver021.tar.
   The tarred file contains the interpreter source code (in C++) a user
   manual, a paper about NeuDL, and about 25 sample NeuDL
   programs. A document demonstrating NeuDL's capabilities is also
   available from the ftp site: /pub/neudl/NeuDL/demo.doc
   /pub/neudl/demo.doc. For more information contact the author:
   Joey Rogers ([e-mail]).

 30. NeoC Explorer (Pattern Maker included)
 ++++++++++++++++++++++++++++++++++++++++++

   The NeoC software is an implementation of Fukushima's
   Neocognitron neural network. Its purpose is to test the model and
   to facilitate interactivity for the experiments. Some substantial
   features: GUI, explorer and tester operation modes, recognition
   statistics, performance analysis, elements displaying, easy net
   construction. PLUS, a pattern maker utility for testing ANN:
   GUI, text file output, transformations. Available for anonymous
   FTP from OAK.Oakland.Edu (141.210.10.117) as
   /SimTel/msdos/neurlnet/neocog10.zip (193 kB, DOS version)

For some of these simulators there are user mailing lists. Get the
packages and look into their documentation for further info.

If you are using a small computer (PC, Mac, etc.) you may want to have
a look at the Central Neural System Electronic Bulletin Board (see
answer 13) Modem: 509-627-6CNS; Sysop: Wesley R. Elsberry; P.O.
Box 1187, Richland, WA 99352; [e-mail]. There
are lots of small simulator packages, the CNS ANNSIM file set. There is
an ftp mirror site for the CNS ANNSIM file set at me.uta.edu
[129.107.2.20] in the /pub/neural directory. Most ANN offerings are in
/pub/neural/annsim.

------------------------------------------------------------------------

o A: Commercial software packages for NN
o ======================================
simulation?
===========

 1. nn/xnn
 +++++++++

        Name: nn/xnn
     Company: Neureka ANS
     Address: Klaus Hansens vei 31B
              5037 Solheimsviken
              NORWAY
       Phone:   [phone]
       Email:   [e-mail]
     Basic capabilities:
      Neural network development tool. nn is a language for specification of
      neural network simulators. Produces C-code and executables for the
      specified models, therefore ideal for application development. xnn is
      a graphical front-end to nn and the simulation code produced by nn.
      Gives graphical representations in a number of formats of any
      variables during simulation run-time. Comes with a number of
      pre-implemented models, including: Backprop (several variants), Self
      Organizing Maps, LVQ1, LVQ2, Radial Basis Function Networks,
      Generalized Regression Neural Networks, Jordan nets, Elman nets,
      Hopfield, etc.
     Operating system: nn: UNIX or MS-DOS, xnn: UNIX/X-windows
     System requirements: 10 Mb HD, 2 Mb RAM
     Approx. price: USD 2000,-

 2. BrainMaker
 +++++++++++++

           Name: BrainMaker, BrainMaker Pro
        Company: California Scientific Software
        Address: 10024 Newtown rd, Nevada City, CA, 95959 USA
      Phone,Fax: [phone]
          Email:  calsci![e-mail] (flakey connection)
     Basic capabilities:  train backprop neural nets
     Operating system:   DOS, Windows, Mac
     System requirements:
     Uses XMS or EMS for large models(PCs only): Pro version
     Approx. price:  $195, $795

     BrainMaker Pro 3.0 (DOS/Windows)     $795
         Gennetic Training add-on         $250
       ainMaker 3.0 (DOS/Windows/Mac)     $195
         Network Toolkit add-on           $150
     BrainMaker 2.5 Student version       (quantity sales only, about $38 each)

     BrainMaker Pro C30 Accelerator Board
               w/ 5Mb memory              $9750
               w/32Mb memory              $13,000

     Intel iNNTS NN Development System    $11,800
          Intel EMB Multi-Chip Board      $9750
          Intel 80170 chip set            $940

     Introduction To Neural Networks book $30

     California Scientific Software can be reached at:
     Phone: [phone]     Fax: [phone]    Tech Support: 916 478 9035
     Mail: 10024 newtown rd, Nevada City, CA, 95959, USA
     30 day money back guarantee, and unlimited free technical support.
     BrainMaker package includes:
      The book Introduction to Neural Networks
      BrainMaker Users Guide and reference manual
          300 pages , fully indexed, with tutorials, and sample networks
      Netmaker
          Netmaker makes building and training Neural Networks easy, by
          importing and automatically creating BrainMaker's Neural Network
          files.  Netmaker imports Lotus, Excel, dBase, and ASCII files.
      BrainMaker
          Full menu and dialog box interface, runs Backprop at 750,000 cps
          on a 33Mhz 486.
     ---Features ("P" means is avaliable in professional version only):
     Pull-down Menus, Dialog Boxes, Programmable Output Files,
     Editing in BrainMaker,  Network Progress Display (P),
     Fact Annotation,  supports many printers,  NetPlotter,
     Graphics Built In (P),  Dynamic Data Exchange (P),
     Binary Data Mode, Batch Use Mode (P), EMS and XMS Memory (P),
     Save Network Periodically,  Fastest Algorithms,
     512 Neurons per Layer (P: 32,000), up to 8 layers,
     Specify Parameters by Layer (P), Recurrence Networks (P),
     Prune Connections and Neurons (P),  Add Hidden Neurons In Training,
     Custom Neuron Functions,  Testing While Training,
     Stop training when...-function (P),  Heavy Weights (P),
     Hypersonic Training,  Sensitivity Analysis (P),  Neuron Sensitivity (P),
     Global Network Analysis (P),  Contour Analysis (P),
     Data Correlator (P),  Error Statistics Report,
     Print or Edit Weight Matrices,  Competitor (P), Run Time System (P),
     Chip Support for Intel, American Neurologics, Micro Devices,
     Genetic Training Option (P),  NetMaker,  NetChecker,
     Shuffle,  Data Import from Lotus, dBASE, Excel, ASCII, binary,
     Finacial Data (P),  Data Manipulation,  Cyclic Analysis (P),
     User's Guide quick start booklet,
     Introduction to Neural Networks 324 pp book

 3. SAS Software/ Neural Net add-on
 ++++++++++++++++++++++++++++++++++

          Name: SAS Software
       Company: SAS Institute, Inc.
       Address: SAS Campus Drive, Cary, NC 27513, USA
     Phone,Fax: [phone]
         Email: [e-mail] (Neural net inquiries only)

    Basic capabilities:
      Feedforward nets with numerous training methods
      and loss functions, plus statistical analogs of
      counterpropagation and various unsupervised
      architectures
    Operating system: Lots
    System requirements: Lots
    Uses XMS or EMS for large models(PCs only): Runs under Windows, OS/2
    Approx. price: Free neural net software, but you have to license
                   SAS/Base software and preferably the SAS/OR, SAS/ETS,
                   and/or SAS/STAT products.
    Comments: Oriented toward data analysis and statistical applications

 4. NeuralWorks
 ++++++++++++++

        Name: NeuralWorks Professional II Plus (from NeuralWare)
     Company: NeuralWare Inc.
      Adress: Pittsburgh, PA 15276-9910
       Phone: [phone]
         FAX: [phone]

    Distributor for Europe:
      Scientific Computers GmbH.
      Franzstr. 107, 52064 Aachen
      Germany
      Tel.   (49) [phone]
      Fax.   (49) [phone]
      Email. [e-mail]

    Basic capabilities:
      supports over 30 different nets: backprop, art-1,kohonen,
      modular neural network, General regression, Fuzzy art-map,
      probabilistic nets, self-organizing map, lvq, boltmann,
      bsb, spr, etc...
      Extendable with optional package.
      ExplainNet, Flashcode (compiles net in .c code for runtime),
      user-defined io in c possible. ExplainNet (to eliminate
      extra inputs), pruning, savebest,graph.instruments like
      correlation, hinton diagrams, rms error graphs etc..
    Operating system   : PC,Sun,IBM RS6000,Apple Macintosh,SGI,Dec,HP.
    System requirements: varies. PC:2MB extended memory+6MB Harddisk space.
                         Uses windows compatible memory driver (extended).
                         Uses extended memory.
    Approx. price      : call (depends on platform)
    Comments           : award winning documentation, one of the market
                         leaders in NN software.

 5. MATLAB Neural Network Toolbox (for use with Matlab 4.x)
 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++

      Contact: The MathWorks, Inc.     Phone: [phone]
               24 Prime Park Way       FAX: [phone]
               Natick, MA 01760 email: [e-mail]

   The Neural Network Toolbox is a powerful collection of
   MATLAB functions for the design, training, and simulation of
   neural networks. It supports a wide range of network architectures
   with an unlimited number of processing elements and
   interconnections (up to operating system constraints). Supported
   architectures and training methods include: supervised training of
   feedforward networks using the perceptron learning rule,
   Widrow-Hoff rule, several variations on backpropagation
   (including the fast Levenberg-Marquardt algorithm), and radial
   basis networks; supervised training of recurrent Elman networks;
   unsupervised training of associative networks including
   competitive and feature map layers; Kohonen networks,
   self-organizing maps, and learning vector quantization. The
   Neural Network Toolbox contains a textbook-quality Users'
   Guide, uses tutorials, reference materials and sample applications
   with code examples to explain the design and use of each network
   architecture and paradigm. The Toolbox is delivered as MATLAB
   M-files, enabling users to see the algorithms and implementations,
   as well as to make changes or create new functions to address a
   specific application.

   (Comment by Richard Andrew Miles Outerbridge,
   [e-mail]:) Matlab is spreading like
   hotcakes (and the educational discounts are very impressive). The
   newest release of Matlab (4.0) ansrwers the question "if you could
   only program in one language what would it be?". The neural
   network toolkit is worth getting for the manual alone. Matlab is
   available with lots of other toolkits (signal processing,
   optimization, etc.) but I don't use them much - the main package
   is more than enough. The nice thing about the Matlab approach is
   that you can easily interface the neural network stuff with
   anything else you are doing.

 6. Propagator
 +++++++++++++

     Contact: ARD Corporation,
              9151 Rumsey Road, Columbia, MD  21045, USA
              [e-mail]
     Easy to use neural network training package.  A GUI implementation of
     backpropagation networks with five layers (32,000 nodes per layer).
     Features dynamic performance graphs, training with a validation set,
     and C/C++ source code generation.
     For Sun (Solaris 1.x & 2.x, $499),
         PC  (Windows 3.x, $199)
         Mac (System 7.x, $199)
     Floating point coprocessor required, Educational Discount,
     Money Back Guarantee, Muliti User Discount
     Windows Demo on:
       nic.funet.fi        /pub/msdos/windows/demo
       oak.oakland.edu     /pub/msdos/neural_nets
           gatordem.zip    pkzip 2.04g archive file
           gatordem.txt    readme text file

 7. NeuroForecaster
 ++++++++++++++++++

      Name:    NeuroForecaster(TM)/Genetica 3.1
      Contact: Accel Infotech (S) Pte Ltd; 648 Geylang Road;
               Republic of Singapore 1438; Phone: [phone]; Fax: [phone]
               [e-mail]
      For IBM PC 386/486 with mouse, or compatibles MS Windows* 3.1,
      MS DOS 5.0 or above 4 MB RAM, 5 MB available harddisk space min;
      3.5 inch floppy drive, VGA monitor or above, Math coprocessor recommended.
        Neuroforecaster 3.1 for Windows is priced at US$999 per single user
      license.  Please email us ([e-mail]) for order form.
        More information about NeuroForecaster(TM)/Genetical may be found in
      ftp.nus.sg incoming/accel.
        NeuroForecaster is a user-friendly neural network program specifically
      designed for building sophisticated and powerful forecasting and
      decision-support systems (Time-Series Forecasting, Cross-Sectional
      Classification, Indicator Analysis)
      Features:
      * GENETICA Net Builder Option for automatic network optimization
      * 12 Neuro-Fuzzy Network Models
      * Multitasking & Background Training Mode
      * Unlimited Network Capacity
      * Rescaled Range Analysis & Hurst Exponent to Unveil Hidden Market
        Cycles & Check for Predictability
      * Correlation Analysis to Compute Correlation Factors to Analyze the
        Significance of Indicators
      * Weight Histogram to Monitor the Progress of Learning
      * Accumulated Error Analysis to Analyze the Strength of Input Indicators
      Its user-friendly interface allows the users to build applications quickly,
      easily and interactively, analyze the data visually and see the results
      immediately.
      The following example applications are included in the package:
      * Credit Rating - for generating the credit rating of bank loan
        applications.
      * Stock market 6 monthly returns forecast
      * Stock selection based on company ratios
      * US$ to Deutschmark exchange rate forecast
      * US$ to Yen exchange rate forecast
      * US$ to SGD exchange rate forecast
      * Property price valuation
      * XOR - a classical problem to show the results are better than others
      * Chaos - Prediction of Mackey-Glass chaotic time series
      * SineWave - For demonstrating the power of Rescaled Range Analysis and
        significance of window size
      Techniques Implemented:
      * GENETICA Net Builder Option - network creation & optimization based on
        Darwinian evolution theory
      * Backprop Neural Networks - the most widely-used training algorithm
      * Fastprop Neural Networks - speeds up training of large problems
      * Radial Basis Function Networks - best for pattern classification problems
      * Neuro-Fuzzy Network
      * Rescaled Range Analysis - computes Hurst exponents to unveil hidden
        cycles & check for predictability
      * Correlation Analysis - to identify significant input indicators

 8. Products of NESTOR, Inc.
 +++++++++++++++++++++++++++

   530 Fifth Avenue; New York, NY 10036; USA; Tel.:
   001-212-398-7955

   Founders: Dr. Leon Cooper (having a Nobel Price) and Dr.
   Charles Elbaum (Brown University). Neural Network Models:
   Adaptive shape and pattern recognition (Restricted Coulomb
   Energy - RCE) developed by NESTOR is one of the most
   powerfull Neural Network Model used in a later products. The
   basis for NESTOR products is the Nestor Learning System -
   NLS. Later are developed: Character Learning System - CLS and
   Image Learning System - ILS. Nestor Development System -
   NDS is a development tool in Standard C - one of the most
   powerfull PC-Tools for simulation and development of Neural
   Networks. NLS is a multi-layer, feed forward system with low
   connectivity within each layer and no relaxation procedure used
   for determining an output response. This unique architecture
   allows the NLS to operate in real time without the need for
   special computers or custom hardware. NLS is composed of
   multiple neural networks, each specializing in a subset of
   information about the input patterns. The NLS integrates the
   responses of its several parallel networks to produce a system
   response that is far superior to that of other neural networks.
   Minimized connectivity within each layer results in rapid training
   and efficient memory utilization- ideal for current VLSI
   technology. Intel has made such a chip - NE1000.

 9. NeuroShell2/NeuroWindows
 +++++++++++++++++++++++++++

   NeuroShell 2 combines powerful neural network architectures, a
   Windows icon driven user interface, and sophisticated utilities for
   MS-Windows machines. Internal format is spreadsheet, and users
   can specify that NeuroShell 2 use their own spreadsheet when
   editing. Includes both Beginner's and Advanced systems, a
   Runtime capability, and a choice of 15 Backpropagation,
   Kohonen, PNN and GRNN architectures. Includes Rules, Symbol
   Translate, Graphics, File Import/Export modules (including
   MetaStock from Equis International) and NET-PERFECT to
   prevent overtraining. Options available: Market Technical
   Indicator Option ($295), Market Technical Indicator Option with
   Optimizer ($590), and Race Handicapping Option ($149).
   NeuroShell price: $495.

   NeuroWindows is a programmer's tool in a Dynamic Link Library
   (DLL) that can create as many as 128 interactive nets in an
   application, each with 32 slabs in a single network, and 32K
   neurons in a slab. Includes Backpropagation, Kohonen, PNN, and
   GRNN paradigms. NeuroWindows can mix supervised and
   unsupervised nets. The DLL may be called from Visual Basic,
   Visual C, Access Basic, C, Pascal, and VBA/Excel 5.
   NeuroWindows price: $369.

   Contact: Ward Systems Group, Inc.; Executive Park West; 5
   Hillcrest Drive; Frederick, MD 21702; USA; Phone: 301
   662-7950; FAX: [phone]. Contact us for a free demo
   diskette and Consumer's Guide to Neural Networks.

 10. NuTank
 ++++++++++

   NuTank stands for NeuralTank. It is educational and
   entertainment software. In this program one is given the shell of a
   2 dimentional robotic tank. The tank has various I/O devices like
   wheels, whiskers, optical sensors, smell, fuel level, sound and such.
   These I/O sensors are connected to Neurons. The player/designer
   uses more Neurons to interconnect the I/O devices. One can have
   any level of complexity desired (memory limited) and do
   subsumptive designs. More complex design take slightly more fuel,
   so life is not free. All movement costs fuel too. One can also tag
   neuron connections as "adaptable" that adapt their weights in
   acordance with the target neuron. This allows neurons to learn.
   The Neuron editor can handle 3 dimention arrays of neurons as
   single entities with very flexible interconect patterns.

   One can then design a scenario with walls, rocks, lights, fat (fuel)
   sources (that can be smelled) and many other such things. Robot
   tanks are then introduced into the Scenario and allowed interact
   or battle it out. The last one alive wins, or maybe one just watches
   the motion of the robots for fun. While the scenario is running it
   can be stopped, edited, zoom'd, and can track on any robot.

   The entire program is mouse and graphicly based. It uses DOS
   and VGA and is written in TurboC++. There will also be the
   ability to download designs to another computer and source code
   will be available for the core neural simulator. This will allow one
   to design neural systems and download them to real robots. The
   design tools can handle three dimentional networks so will work
   with video camera inputs and such. Eventualy I expect to do a port
   to UNIX and multi thread the sign. I also expect to do a Mac port
   and maybe NT or OS/2

   Copies of NuTank cost $50 each. Contact: Richard Keene; Keene
   Educational Software; [e-mail]

   NuTank shareware with the Save options disabled is available via
   anonymous ftp from the Internet, see the file
   /pub/incoming/nutank.readme on the host cher.media.mit.edu.

 11. Neuralyst
 +++++++++++++

   Name: Neuralyst Version 1.4; Company: Cheshire Engineering
   Corporation; Address: 650 Sierra Madre Villa, Suite 201,
   Pasedena CA 91107; Phone: [phone]; Fax: [phone];

   Basic capabilities: training of backpropogation neural nets.
   Operating system: Windows or Macintosh running Microsoft
   Excel Spreadsheet. Neuralyst is an add-in package for Excel.
   Approx. price: $195 for windows or Mac. Comments: A simple
   model that is easy to use. Integrates nicely into Microsoft Excel.
   Allows user to create, train, and run backprop ANN models
   entirely within an Excel spreadsheet. Provides macro functions
   that can be called from Excel macro's, allowing you to build a
   custom Window's interface using Excel's macro language and
   Visual Basic tools. The new version 1.4 includes a genetic
   algorithm to guide the training process. A good bargain to boot.
   (Comments by Duane Highley, a user and NOT the program
   developer. [e-mail])

------------------------------------------------------------------------

o A: Neural Network hardware?
o ===========================

[who will write some short comment on the most important
HW-packages and chips?]

The Number 1 of each volume of the journal "Neural Networks" has a
list of some dozens of suppliers of Neural Network support: Software,
Hardware, Support, Programming, Design and Service.

Here is a short list of companies:

 1. HNC, INC.
 ++++++++++++

      5501 Oberlin Drive
      San Diego
      California 92121
      [phone]
         and a second address at
      7799 Leesburg Pike, Suite 900
      Falls Church, Virginia
      22043
      [phone]
         Note: Australian Dist.: Unitronics
                                 Tel : [phone]
                                 Contact: Martin Keye
      HNC markets:
       'Image Document Entry Processing Terminal' - it recognises
       handwritten documents and converts the info to ASCII.
       'ExploreNet 3000' - a NN demonstrator
       'Anza/DP Plus'- a Neural Net board with 25MFlop or 12.5M peak
       interconnects per second.

 2. SAIC (Sience Application International Corporation)
 ++++++++++++++++++++++++++++++++++++++++++++++++++++++

      10260 Campus Point Drive
      MS 71, San Diego
      CA 92121
      [phone]
      Fax: [phone]

 3. Micro Devices
 ++++++++++++++++

      30 Skyline Drive
      Lake Mary
      FL 32746-6201
      [phone]
      MicroDevices makes   MD1220 - 'Neural Bit Slice'
      Each of the products mentioned sofar have very different usages.
      Although this sounds similar to Intel's product, the
      architectures are not.

 4. Intel Corp
 +++++++++++++

      2250 Mission College Blvd
      Santa Clara, Ca 95052-8125
      Attn ETANN, Mail Stop SC9-40
      [phone]
      Intel is making an experimental chip:
      80170NW - Electrically trainable Analog Neural Network (ETANN)
      It has 64 'neurons' on it - almost fully internally connectted
      and the chip can be put in an hierarchial architecture to do 2 Billion
      interconnects per second.
      Support software has already been made by
        California Scientific Software
        10141 Evening Star Dr #6
        Grass Valley, CA 95945-9051
        [phone]
      Their product is called 'BrainMaker'.

 5. NeuralWare, Inc
 ++++++++++++++++++

      Penn Center West
      Bldg IV Suite 227
      Pittsburgh
      PA 15276
      They only sell software/simulator but for many platforms.

 6. Tubb Research Limited
 ++++++++++++++++++++++++

      7a Lavant Street
      Peterfield
      Hampshire
      GU32 2EL
      United Kingdom
      Tel: [phone]

 7. Adaptive Solutions Inc
 +++++++++++++++++++++++++

      1400 NW Compton Drive
      Suite 340
      Beaverton, OR 97006
      U. S. A.
      Tel: [phone];   FAX: [phone]

 8. NeuroDynamX, Inc.
 ++++++++++++++++++++

      4730 Walnut St., Suite 101B
      Boulder, CO 80301
      Voice: [phone]   Fax: [phone]
      Internet: [e-mail]
      NDX sells a number neural network hardware products:
      NDX Neural Accelerators: a line of i860-based accelerator cards for
      the PC that give up to 45 million connections per second for use
      with the DynaMind neural network software.
      iNNTS: Intel's 80170NX (ETANN) Neural Network Training System. NDX's president
      was one of the co-designers of this chip.

 9. IC Tech
 ++++++++++

   NEURO-COMPUTING IC's:
   *  DANN050L (dendro-dendritic artificial neural network)
      + 50 neurons fully connected at the input
      + on-chip digital learning capability
      + 6 billion connections/sec peak speed
      + learns 7 x 7 template in < 50 nsec., recalls in < 400 nsec.
      + low power < 100 milli Watts
      + 64-pin package
   *  NCA717D  (neuro correlator array)
      + analog template matching in < 500 nsec.
      + analog input / digital output pins for real-time computation
      + vision applications in stereo and motion computation
      + 40-pin package
   NEURO COMPUTING BOARD:
   *  ICT1050
      + IBM PC compatible or higher
      + with on-board DANN050L
      + digital interface
      + custom configurations available
   Contact:
   IC Tech (Innovative Computing Technologies, Inc.)
   4138 Luff Court
   Okemos, MI 48864
   [phone]
   [e-mail]

And here is an incomplete overview over known Neural Computers with
their newest known reference.

\subsection*{Digital}
\subsubsection{Special Computers}

{\bf AAP-2}
Takumi Watanabe, Yoshi Sugiyama, Toshio Kondo, and Yoshihiro Kitamura.
Neural network simulation on a massively parallel cellular array
processor: AAP-2.
In International Joint Conference on Neural Networks, 1989.

{\bf ANNA}
B.E.Boser, E.Sackinger, J.Bromley, Y.leChun, and L.D.Jackel.\\
Hardware Requirements for Neural Network Pattern Classifiers.\\
In {\it IEEE Micro}, 12(1), pages 32-40, February 1992.

{\bf Analog Neural Computer}
Paul Mueller et al.
Design and performance of a prototype analog neural computer.
In Neurocomputing, 4(6):311-323, 1992.

{\bf APx -- Array Processor Accelerator}\\
F.Pazienti.\\
Neural networks simulation with array processors.
In {\it Advanced Computer Technology, Reliable Systems and Applications;
Proceedings of the 5th Annual Computer Conference}, pages 547-551.
IEEE Comput. Soc. Press, May 1991. ISBN: 0-8186-2141-9.

{\bf ASP -- Associative String Processor}\\
A.Krikelis.\\
A novel massively associative processing architecture for the
implementation artificial neural networks.\\
In {\it 1991 International Conference on Acoustics, Speech and
Signal Processing}, volume 2, pages 1057-1060. IEEE Comput. Soc. Press,
May 1991.

{\bf BSP400}
Jan N.H. Heemskerk, Jacob M.J. Murre, Jaap Hoekstra, Leon H.J.G.
Kemna, and Patrick T.W. Hudson.
The bsp400: A modular neurocomputer assembled from 400 low-cost
microprocessors.
In International Conference on Artificial Neural Networks. Elsevier
Science, 1991.

{\bf BLAST}\\
J.G.Elias, M.D.Fisher, and C.M.Monemi.\\
A multiprocessor machine for large-scale neural network simulation.
In {\it IJCNN91-Seattle: International Joint Conference on Neural
Networks}, volume 1, pages 469-474. IEEE Comput. Soc. Press, July 1991.
ISBN: 0-7883-0164-1.

{\bf CNAPS Neurocomputer}\\
H.McCartor\\
Back Propagation Implementation on the Adaptive Solutions CNAPS
Neurocomputer.\\
In {\it Advances in Neural Information Processing Systems}, 3, 1991.

{\bf GENES~IV and MANTRA~I}\\
Paolo Ienne and  Marc A. Viredaz\\
{GENES~IV}: A Bit-Serial Processing Element for a Multi-Model
   Neural-Network Accelerator\\
Proceedings of the International Conference on Application Specific Array
   Processors, Venezia, 1993.

{\bf MA16 -- Neural Signal Processor}
U.Ramacher, J.Beichter, and N.Bruls.\\
Architecture of a general-purpose neural signal processor.\\
In {\it IJCNN91-Seattle: International Joint Conference on Neural
Networks}, volume 1, pages 443-446. IEEE Comput. Soc. Press, July 1991.
ISBN: 0-7083-0164-1.

{\bf MANTRA I}\\
Marc A. Viredaz\\
{MANTRA~I}: An {SIMD} Processor Array for Neural Computation
Proceedings of the Euro-ARCH'93 Conference, {M\"unchen}, 1993.

{\bf Mindshape}
Jan N.H. Heemskerk, Jacob M.J. Murre Arend Melissant, Mirko Pelgrom,
and Patrick T.W. Hudson.
Mindshape: a neurocomputer concept based on a fractal architecture.
In International Conference on Artificial Neural Networks. Elsevier
Science, 1992.

{\bf mod 2}
Michael L. Mumford, David K. Andes, and Lynn R. Kern.
The mod 2 neurocomputer system design.
In IEEE Transactions on Neural Networks, 3(3):423-433, 1992.

{\bf NERV}\\
R.Hauser, H.Horner, R. Maenner, and M.Makhaniok.\\
Architectural Considerations for NERV - a General Purpose Neural
Network Simulation System.\\
In {\it Workshop on Parallel Processing: Logic, Organization and
Technology -- WOPPLOT 89}, pages 183-195. Springer Verlag, Mars 1989.
ISBN: 3-5405-5027-5.

{\bf NP -- Neural Processor}\\
D.A.Orrey, D.J.Myers, and J.M.Vincent.\\
A high performance digital processor for implementing large artificial
neural networks.\\
In {\it Proceedings of of the IEEE 1991 Custom Integrated Circuits
Conference}, pages 16.3/1-4. IEEE Comput. Soc. Press, May 1991.
ISBN: 0-7883-0015-7.

{\bf RAP -- Ring Array Processor }\\
N.Morgan, J.Beck, P.Kohn, J.Bilmes, E.Allman, and J.Beer.\\
The ring array processor: A multiprocessing peripheral for connectionist
applications. \\
In {\it Journal of Parallel and Distributed Computing}, pages
248-259, April 1992.

{\bf RENNS -- REconfigurable Neural Networks Server}\\
O.Landsverk, J.Greipsland, J.A.Mathisen, J.G.Solheim, and L.Utne.\\
RENNS - a Reconfigurable Computer System for Simulating Artificial
Neural Network Algorithms.\\
In {\it Parallel and Distributed Computing Systems, Proceedings of the
ISMM 5th International Conference}, pages 251-256. The International
Society for Mini and Microcomputers - ISMM, October 1992.
ISBN: 1-8808-4302-1.

{\bf SMART -- Sparse Matrix Adaptive and Recursive Transforms}\\
P.Bessiere, A.Chams, A.Guerin, J.Herault, C.Jutten, and J.C.Lawson.\\
From Hardware to Software: Designing a ``Neurostation''.\\
In {\it VLSI design of Neural Networks}, pages 311-335, June 1990.

{\bf SNAP -- Scalable Neurocomputer Array Processor}
E.Wojciechowski.\\
SNAP: A parallel processor for implementing real time neural networks.\\
In {\it Proceedings of the IEEE 1991 National Aerospace and Electronics
Conference; NAECON-91}, volume 2, pages 736-742. IEEE Comput.Soc.Press,
May 1991.

{\bf Toroidal Neural Network Processor}\\
S.Jones, K.Sammut, C.Nielsen, and J.Staunstrup.\\
Toroidal Neural Network: Architecture and Processor Granularity
Issues.\\
In {\it VLSI design of Neural Networks}, pages 229-254, June 1990.

{\bf SMART and SuperNode}
P. Bessi`ere, A. Chams, and P. Chol.
MENTAL : A virtual machine approach to artificial neural networks
programming. In NERVES, ESPRIT B.R.A. project no 3049, 1991.


\subsubsection{Standard Computers}

{\bf EMMA-2}\\
R.Battiti, L.M.Briano, R.Cecinati, A.M.Colla, and P.Guido.\\
An application oriented development environment for Neural Net models on
multiprocessor Emma-2.\\
In {\it Silicon Architectures for Neural Nets; Proceedings for the IFIP
WG.10.5 Workshop}, pages 31-43. North Holland, November 1991.
ISBN: 0-4448-9113-7.

{\bf iPSC/860 Hypercube}\\
D.Jackson, and D.Hammerstrom\\
Distributing Back Propagation Networks Over the Intel iPSC/860
Hypercube}\\
In {\it IJCNN91-Seattle: International Joint Conference on Neural
Networks}, volume 1, pages 569-574. IEEE Comput. Soc. Press, July 1991.
ISBN: 0-7083-0164-1.

{\bf SCAP -- Systolic/Cellular Array Processor}\\
Wei-Ling L., V.K.Prasanna, and K.W.Przytula.\\
Algorithmic Mapping of Neural Network Models onto Parallel SIMD
Machines.\\
In {\it IEEE Transactions on Computers}, 40(12), pages 1390-1401,
December 1991. ISSN: 0018-9340.

------------------------------------------------------------------------

o A: Databases for experimentation with NNs?
o ==========================================

 1. The neural-bench Benchmark collection
 ++++++++++++++++++++++++++++++++++++++++

   Accessible via anonymous FTP on ftp.cs.cmu.edu [128.2.206.173]
   in directory /afs/cs/project/connect/bench. In case of problems or if
   you want to donate data, email contact is
   "[e-mail]". The data sets in this repository
   include the 'nettalk' data, 'two spirals', protein structure
   prediction, vowel recognition, sonar signal classification, and a few
   others.

 2. Proben1
 ++++++++++

   Proben1 is a collection of 12 learning problems consisting of real
   data. The datafiles all share a single simple common format.
   Along with the data comes a technical report describing a set of
   rules and conventions for performing and reporting benchmark
   tests and their results. Accessible via anonymous FTP on
   ftp.cs.cmu.edu [128.2.206.173] as
   /afs/cs/project/connect/bench/contrib/prechelt/proben1.tar.gz. and
   also on ftp.ira.uka.de [129.13.10.90] as /pub/neuron/proben.tar.gz.
   The file is about 1.8 MB and unpacks into about 20 MB.

 3. UCI machine learning database
 ++++++++++++++++++++++++++++++++

   Accessible via anonymous FTP on ics.uci.edu [128.195.1.1] in
   directory /pub/machine-learning-databases".

 4. NIST special databases of the National Institute Of Standards
 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
   And Technology:
   +++++++++++++++

   Several large databases, each delivered on a CD-ROM. Here is a
   quick list.
    o NIST Binary Images of Printed Digits, Alphas, and Text
    o NIST Structured Forms Reference Set of Binary Images
    o NIST Binary Images of Handwritten Segmented
      Characters
    o NIST 8-bit Gray Scale Images of Fingerprint Image
      Groups
    o NIST Structured Forms Reference Set 2 of Binary Images
    o NIST Test Data 1: Binary Images of Hand-Printed
      Segmented Characters
    o NIST Machine-Print Database of Gray Scale and Binary
      Images
    o NIST 8-Bit Gray Scale Images of Mated Fingerprint Card
      Pairs
    o NIST Supplemental Fingerprint Card Data (SFCD) for
      NIST Special Database 9
    o NIST Binary Image Databases of Census Miniforms
      (MFDB)
    o NIST Mated Fingerprint Card Pairs 2 (MFCP 2)
    o NIST Scoring Package Release 1.0
    o NIST FORM-BASED HANDPRINT RECOGNITION
      SYSTEM
   Here are example descriptions of two of these databases:

   NIST special database 2: Structured Forms Reference Set
   -------------------------------------------------------
   (SFRS)
   ------

   The NIST database of structured forms contains 5,590 full page
   images of simulated tax forms completed using machine print.
   THERE IS NO REAL TAX DATA IN THIS DATABASE. The
   structured forms used in this database are 12 different forms from
   the 1988, IRS 1040 Package X. These include Forms 1040, 2106,
   2441, 4562, and 6251 together with Schedules A, B, C, D, E, F and
   SE. Eight of these forms contain two pages or form faces making
   a total of 20 form faces represented in the database. Each image is
   stored in bi-level black and white raster format. The images in
   this database appear to be real forms prepared by individuals but
   the images have been automatically derived and synthesized using
   a computer and contain no "real" tax data. The entry field values
   on the forms have been automatically generated by a computer in
   order to make the data available without the danger of distributing
   privileged tax information. In addition to the images the database
   includes 5,590 answer files, one for each image. Each answer file
   contains an ASCII representation of the data found in the entry
   fields on the corresponding image. Image format documentation
   and example software are also provided. The uncompressed
   database totals approximately 5.9 gigabytes of data.

   NIST special database 3: Binary Images of Handwritten
   -----------------------------------------------------
   Segmented Characters (HWSC)
   ---------------------------

   Contains 313,389 isolated character images segmented from the
   2,100 full-page images distributed with "NIST Special Database
   1". 223,125 digits, 44,951 upper-case, and 45,313 lower-case
   character images. Each character image has been centered in a
   separate 128 by 128 pixel region, error rate of the segmentation
   and assigned classification is less than 0.1%. The uncompressed
   database totals approximately 2.75 gigabytes of image data and
   includes image format documentation and example software.

   The system requirements for all databases are a 5.25" CD-ROM
   drive with software to read ISO-9660 format. Contact: Darrin L.
   Dimmick; [e-mail]; [phone]

   The prices of the databases are between US$ 250 and 1895 If you
   wish to order a database, please contact: Standard Reference
   Data; National Institute of Standards and Technology; 221/A323;
   Gaithersburg, MD 20899; Phone: [phone]; FAX:
   [phone]

   Samples of the data can be found by ftp on sequoyah.ncsl.nist.gov
   in directory /pub/data A more complete description of the available
   databases can be obtained from the same host as
   /pub/databases/catalog.txt

 5. CEDAR CD-ROM 1: Database of Handwritten Cities, States,
 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
   ZIP Codes, Digits, and Alphabetic Characters
   ++++++++++++++++++++++++++++++++++++++++++++

   The Center Of Excellence for Document Analysis and
   Recognition (CEDAR) State University of New York at Buffalo
   announces the availability of CEDAR CDROM 1: USPS Office of
   Advanced Technology The database contains handwritten words
   and ZIP Codes in high resolution grayscale (300 ppi 8-bit) as well
   as binary handwritten digits and alphabetic characters (300 ppi
   1-bit). This database is intended to encourage research in off-line
   handwriting recognition by providing access to handwriting
   samples digitized from envelopes in a working post office.

        Specifications of the database include:
        +    300 ppi 8-bit grayscale handwritten words (cities,
             states, ZIP Codes)
             o    5632 city words
             o    4938 state words
             o    9454 ZIP Codes
        +    300 ppi binary handwritten characters and digits:
             o    27,837 mixed alphas  and  numerics  segmented
                  from address blocks
             o    21,179 digits segmented from ZIP Codes
        +    every image supplied with  a  manually  determined
             truth value
        +    extracted from live mail in a  working  U.S.  Post
             Office
        +    word images in the test  set  supplied  with  dic-
             tionaries  of  postal  words that simulate partial
             recognition of the corresponding ZIP Code.
        +    digit images included in test  set  that  simulate
             automatic ZIP Code segmentation.  Results on these
             data can be projected to overall ZIP Code recogni-
             tion performance.
        +    image format documentation and software included

   System requirements are a 5.25" CD-ROM drive with software to
   read ISO-9660 format. For any further information, including
   how to order the database, please contact: Jonathan J. Hull,
   Associate Director, CEDAR, 226 Bell Hall State University of
   New York at Buffalo, Buffalo, NY 14260; [e-mail]
   (email)

 6. AI-CD-ROM (see under answer 13)
 ++++++++++++++++++++++++++++++++++

 7. Time series archive
 ++++++++++++++++++++++

   Various datasets of time series (to be used for prediction learning
   problems) are available for anonymous ftp from ftp.santafe.edu
   [192.12.12.1] in /pub/Time-Series". Problems are for example:
   fluctuations in a far-infrared laser; Physiological data of patients
   with sleep apnea; High frequency currency exchange rate data;
   Intensity of a white dwarf star; J.S. Bachs final (unfinished) fugue
   from "Die Kunst der Fuge"

   Some of the datasets were used in a prediction contest and are
   described in detail in the book "Time series prediction: Forecasting
   the future and understanding the past", edited by
   Weigend/Gershenfield, Proceedings Volume XV in the Santa Fe
   Institute Studies in the Sciences of Complexity series of Addison
   Wesley (1994).

------------------------------------------------------------------------

That's all folks.

Acknowledgements: Thanks to all the people who helped to get the stuff
                  above into the posting. I cannot name them all, because
                  I would make far too many errors then. :->

                  No?  Not good?  You want individual credit?
                  OK, OK. I'll try to name them all. But: no guarantee....

  THANKS FOR HELP TO:
(in alphabetical order of email adresses, I hope)

 o Gamze Erten <[e-mail]>
 o Steve Ward <[e-mail]>
 o Mohammad Bahrami <[e-mail]>
 o Allen Bonde <[e-mail]>
 o Accel Infotech Spore Pte Ltd <[e-mail]>
 o Alexander Linden <[e-mail]>
 o S.Taimi Ames <[e-mail]>
 o Axel Mulder <[e-mail]>
 o [e-mail]
 o Andy Gillanders <[e-mail]>
 o Davide Anguita <[e-mail]>
 o Avraam Pouliakis <[e-mail]>
 o Kim L. Blackwell <[e-mail]>
 o Paul Bakker <[e-mail]>
 o Stefan Bergdoll <[e-mail]>
 o Jamshed Bharucha <[e-mail]>
 o Yijun Cai <[e-mail]>
 o L. Leon Campbell <[e-mail]>
 o Craig Watson <[e-mail]>
 o Yaron Danon <[e-mail]>
 o David Ewing <[e-mail]>
 o David DeMers <[e-mail]>
 o Denni Rognvaldsson <[e-mail]>
 o Duane Highley <[e-mail]>
 o [e-mail]
 o Donald Tveter <[e-mail]>
 o Frank Schnorrenberg <[e-mail]>
 o Gary Lawrence Murphy <[e-mail]>
 o [e-mail]
 o Lee Giles <[e-mail]>
 o Glen Clark <opto![e-mail]>
 o Phil Goodman <[e-mail]>
 o [e-mail]
 o Joerg Heitkoetter <[e-mail]>
 o Ralf Hohenstein <[e-mail]>
 o Ed Rosenfeld <[e-mail]>
 o Jean-Denis Muller <[e-mail]>
 o Jeff Harpster <uu0979![e-mail]>
 o Jonathan Kamens <[e-mail]>
 o J.J. Merelo <[e-mail]>
 o Jon Gunnar Solheim <[e-mail]>
 o Josef Nelissen <[e-mail]>
 o Joey Rogers <[e-mail]>
 o Ken Karnofsky <[e-mail]>
 o [e-mail]
 o Luke Koops <[e-mail]>
 o William Mackeown <[e-mail]>
 o Mark Plumbley <[e-mail]>
 o Peter Marvit <[e-mail]>
 o [e-mail]
 o Yoshiro Miyata <[e-mail]>
 o Madhav Moganti <[e-mail]>
 o Jyrki Alakuijala <[e-mail]>
 o [e-mail]
 o Maciek Sitnik <[e-mail]>
 o R. Steven Rainwater <[e-mail]>
 o Paolo Ienne <[e-mail]>
 o Paul Keller <[e-mail]>
 o Michael Plonski <[e-mail]>
 o Lutz Prechelt <[e-mail]> [creator of FAQ]
 o Richard Andrew Miles Outerbridge <[e-mail]>
 o Richard Cornelius <[e-mail]>
 o Rob Cunningham <[e-mail]>
 o [e-mail]
 o Osamu Saito <[e-mail]>
 o Sheryl Cormicle <[e-mail]>
 o Ted Stockwell <[e-mail]>
 o Thomas G. Dietterich <[e-mail]>
 o [e-mail]
 o Ulrich Wendl <[e-mail]>
 o M. Verleysen <[e-mail]>
 o Sherif Hashem <[e-mail]>
 o Matthew P Wiener <[e-mail]>
 o Wesley Elsberry <[e-mail]>

Bye

  Lutz

Neural network FAQ / Lutz Prechelt, [e-mail]
--
Lutz Prechelt   (email: [e-mail])            | Whenever you
Institut fuer Programmstrukturen und Datenorganisation  | complicate things,
Universitaet Karlsruhe;  76128 Karlsruhe;  Germany      | they get
(Voice: ++49/721/608-4068, FAX: ++49/721/694092)        | less simple.