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Subject: FAQ in comp.ai.neural-nets -- monthly posting
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Last-modified: 94/03/21

(FAQ means "Frequently Asked Questions")

  ------------------------------------------------------------------------
        Anybody who is willing to contribute any question or
        information, please email me; if it is relevant,
        I will incorporate it. But: PLEASE format your contribution
        appropriately so that I can just drop it in.

        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 rtfm.mit.edu (and on some other hosts as well).
Look in the anonymous ftp directory "/pub/usenet/news.answers",
the filename is as given in the 'Archive-name:' header above.
If you do not have anonymous ftp access, 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
comp.ai.neural-nets: To read comp.ai.neural-nets (or post articles to it)
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.

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> (if present at all), search
>> for the string "-A<x>.)" (so the answer to question 12 is at "-A12.)")

And now, in the end, we begin:

============================== Questions ==============================

(the short forms and non-continous numbering is intended)
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 ?

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

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

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

19.) Databases for experimentation with NNs ?

============================== Answers ==============================

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

-A1.)  What is this newsgroup for ?

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, question 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:
  a) simple concatenation of all the answers is not enough:
     instead, redundancies, irrelevancies, verbosities, and errors
     should be filtered out (as good as possible)
  b) the answers should be separated clearly
  c) the contributors of the individual answers should be identifiable
     (unless they requested to remain anonymous [yes, that happens])
  d) the summary should start with the "quintessence" of the answers,
     as seen by the original poster
  e) 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...   :->

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

-A2.)  What is a neural network (NN) ?

  [anybody there to write something better?
   buzzwords: artificial vs. natural/biological; units and
   connections; value passing; inputs and outputs; storage in structure
   and weights; only local information; highly parallel operation ]

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.

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

-A3.)  What can you do with a Neural Network and what not ?

  [preliminary]

In principle, NNs can compute any computable function, i.e. they can
do everything a normal digital computer can do.
Especially can anything that can be represented as a mapping between
vector spaces 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 a high error rate, 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.

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

-A4.)  Who is concerned with Neural Networks ?

Neural Networks are interesting for quite a lot of very dissimilar people:

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

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

-A6.)  What does 'backprop' mean ?

[anybody to write something similarly short,
 but easier to understand for a beginner ? ]

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.

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  :->

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

-A7.)  How many learning methods for NNs exist ?  Which ?

There are many many learning methods for NNs by now. Nobody can know
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)

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

-A8.)  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.

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.

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

-A9.)  What about Fuzzy Logic ?

[preliminary]
[Who will write an introduction?]

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.

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

-A10.) Good introductory literature about Neural Networks ?

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

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."


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".

Haykin, S. (1994). Neural Networks, a Comprehensive Foundation.
Macmillan, New York, NY.

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( ==> no good)"; "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".

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".

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".

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

-A11.) Any journals and magazines about Neural 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.:      6 issues/year (vol. 1 in 1988)
Cost/Yr: Free with INNS membership ($45?), Individual $65, Institution $175
ISSN #:     0893-6080
Remark:  Official Journal of International Neural Network Society (INNS).
     Contains Original Contributions, Invited Review Articles, Letters
     to Editor, Invited Book Reviews, Editorials, Announcements and INNS
     News, Software Surveys.  This is probably the most popular NN journal.
     (Note: Remarks supplied by Mike Plonski "[e-mail]")
-------
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 Transaction 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:     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 brandt kehoe "[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:      International Journal of Neurocomputing
Publish: Elsevier Science Publishers
Freq.:      Quarterly (vol. 1 in 1989)
Remark:  Review has been reported to be fast (less than 3 months)
-----
Title:     Neurocomputers
Publish: Gallifrey Publishing
Address: Gallifrey Publishing, PO Box 155, Vicksburg, Michigan, 49097, USA
     Tel: [phone]
Freq.     Monthly (1st issue 1987?)
ISSN #:     0893-1585
Editor:     Derek F. Stubbs
Cost/Yr: $32 (USA, Canada), $48 (elsewhere)
Remark:     I only have one exemplar so I cannot give you much detail about
         the contents. It is a very small one (12 pages) but it has a lot
         of (short) information in it about e.g. conferences, books,
         (new) ideas etc.  I don't think it is very expensive but I'm not sure.
     (Note: remark provided by J.R.M. Smits "[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 the rapid publication of research
     on the 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 <[e-mail]>)
-----
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:   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
==================================

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

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

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

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

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

COGNITIVE PSYCHOLOGY
(no comment!)

JOURNAL OF MATHEMATICAL PSYCHOLOGY
(several good book reviews)

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

-A12.) The most important conferences concerned with 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)
   2. International Joint Conference on Neural Networks (IJCNN)
      co-sponsored by INNS and IEEE
   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. 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])

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 long 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.

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

-A13.) Neural Network Associations ?

[Is this data still correct ?  Who will send me some update ?]

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.  Telephone: [phone].

4. European Neural Network Society (ENNS)

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...
   Address : ACTH - Le Castelnau R2
             23 avenue de la Galline
             34170 Castelnau-le-Lez
             FRANCE
   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


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

-A14.) Other sources of information about NNs ?

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)"
   Moderated by Peter Marvit.
   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 A13) 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:
      [... some stuff deleted...]
       - Neural Networks: Source code and executables for many different
   platforms including Unix, DOS, and Macintosh.  ANN development tools,
   example networks, sample data, and tutorials are included.  A complete
   collection of Neural Digest is included as well.
      [... lots of stuff deleted...]
   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. http://www.eeb.ele.tue.nl
   In World-Wide-Web (WWW, for example via the xmosaic program) you
   can read neural network information by opening the universal resource
   locator (URL) http://www.eeb.ele.tue.nl
   It contains a hypertext version of this FAQ and other NN-related
   information.

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:
        host:   una.hh.lib.umich.edu
        path:   /inetdirsstacks
        file:   neurosci:cormbonario
   gopher:
        via U. Minnesota list of gophers
        menu:   North America/USA/Michigan/Clearinghouse.../
                        All Guides/Neurosciences
   WWW:
        gopher://una.hh.lib.umich.edu/00/inetdirsstacks/
                neurosci:cormbonario
   We are also creating a **hypertext version** of the guide:
   WWW:
        http://http2.sils.umich.edu/Public/nirg/nirg1.html

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

-A15.) Freely available software packages for NN simulation ?


[This is a bit chaotic and needs reorganization.
 A bit more information about what the various programs can do,
 on which platform they run, and how big they are would also be nice.
 And some important packages are still missing (?)
 Who volunteers for that ?]

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.
   anonymous FTP from cs.rochester.edu (192.5.53.209)
   directory :  pub/simulator
   files:               README                   (8 KB)
     (documentation:)   rcs_v4.2.justdoc.tar.Z   (1.6 MB)
     (source code:)     rcs_v4.2.justsrc.tar.Z   (1.4 MB)

2. UCLA-SFINX
   ftp 131.179.16.6  (retina.cs.ucla.edu)
   Name: sfinxftp
   Password: joshua
   directory: pub/
   files : README
           sfinx_v2.0.tar.Z
   Email info request : [e-mail]

3. NeurDS
   request from [e-mail]
   simulator for DEC systems supporting VT100 terminal.
   OR
   anonymous ftp gatekeeper.dec.com [16.1.0.2]
   directory: pub/DEC
   file: NeurDS031.tar.Z ( please check may be NeurDSO31.tar.Z )

4. PlaNet5.7 (also known as SunNet)
   ftp 133.15.240.3  (tutserver.tut.ac.jp)
   pub/misc/PlaNet5.7.tar.Z
     or
   ftp 128.138.240.1 (boulder.colorado.edu)
   pub/generic-sources/PlaNet5.7.tar.Z  (also the old PlaNet5.6.tar.Z)
   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]

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.
   May be obtained via 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
   anonymous ftp from bruno.cs.colorado.edu [128.138.243.151]
   directory: /pub/cs/misc
   file: Mactivation-3.3.sea.hqx

7. <defunct>

8. Cascade Correlation Simulator
   A simulator based on Scott Fahlman's Cascade Correlation algorithm.
   Anonymous ftp from ftp.cs.cmu.edu [128.2.206.173]
   directory  /afs/cs/project/connect/code
   file       cascor1a.shar    (206 KB)
   There is also a version of recurrent cascade correlation in the same
   directory in file  rcc1.c (107 KB).

9. 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
   nevprop116.shar (137 KB)
   (see also the description of NEVPROP below)

10. 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).

11. 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,
   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),
   and is user-extendable.

   ftp: ftp.informatik.uni-stuttgart.de [129.69.211.2]
   directory /pub/SNNS
   file    SNNSv3.0.tar.Z   OR   SNNSv3.0.tar.Za[a-d]   ( 826 KB)
   manual  SNNSv2.1.Manual.ps.Z                         (1270 KB)
           SNNSv2.1.Readme                (8052 Bytes)

12. 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. This replaces the NeWS1.1 graphical interface
   in version 4.0 of the Aspirin/MIGRAINES software. The
   new interface is not a simple to use as the version 4.0
   interface but is much more portable and flexible.
     The MIGRAINES interface allows users to output
   neural network weight and node vectors to disk or to
   other Unix processes. 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:
   CMU's simulator collection on "pt.cs.cmu.edu" (128.2.254.155)
   in /afs/cs/project/connect/code/am6.tar.Z".
   and UCLA's cognitive science machine "ftp.cognet.ucla.edu" (128.97.50.19)
   in alexis/am6.tar.Z
   The compressed tar file is a little less than 2 megabytes.

13. Adaptive Logic Network kit
   Available from menaik.cs.ualberta.ca.  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]
   README                         /pub/atree/atree.readme   (7 KB)
   unix source code and examples: /pub/atree/atree2.tar.Z   (145 KB)
   Postscript documentation:      /pub/atree/atree2.ps.Z    ( 76 KB)
   MS-Windows 3.x executable:     /pub/atree/a27exe.exe     (412 KB)
   MS-Windows 3.x source code:    /pub/atree/atre27.exe     (572 KB)

14. NeuralShell
   Available from FTP site quanta.eng.ohio-state.edu
   (128.146.35.1) in directory "pub/NeuralShell", filename
   "NeuralShell.tar".

15. PDP
   The PDP simulator package is available via anonymous FTP at
   nic.funet.fi  (128.214.6.100) in /pub/sci/neural/sims/pdp.tar.Z (0.2 MB)
   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 nic.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."

16. Xerion
   Xerion is available via anonymous ftp from
   ftp.cs.toronto.edu in the directory /pub/xerion.
   xerion-3.1.ps.Z (153 kB) and xerion-3.1.tar.Z (1322 kB) plus
   several concrete simulators built with xerion (about 40 kB each).
   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]

17. Neocognitron simulator
  An implementation is available for anonymous ftp at
    [128.194.15.32] tamsun.tamu.edu as
                    /pub/neocognitron.Z.tar     or
    [129.12.21.7]   unix.hensa.ac.uk as
                    /pub/uunet/pub/ai/neural/neocognitron.tar.Z
  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.

18. 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.
  Version 0.73 of MUME has been deposited for anonymous ftp on
  mickey.sedal.su.oz.au (129.78.24.170) in directory /mume.
  Contact:
    Marwan Jabri, SEDAL, Sydney University Electrical Engineering,
    NSW 2006 Australia,  [e-mail]

19. 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) in
  /pub/lvq_pak/lvq_pak-2.1.tar.Z   (340 kB, Unix)
  /pub/lvq_pak/lvq_p2r1.exe        (310 kB, MS-DOS self-extract archive)
  /pub/som_pak/som_pak-1.1.tar.Z   (246 kB, Unix)
  /pub/som_pak/som_p1r1.exe        (215 kB, MS-DOS self-extract archive)

20. SESAME
  (Software Environment for the Simulation of Adaptive Modular Systems)
  SESAME  is a  prototypical software implementation  which facilitates
  * Object-oriented building blocks approach.
  * 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!
  * 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.
  * A kernel which is the framework for the C++ classes and allows run-time
    manipulation,  construction,  and integration  of arbitrary complex and
    hybrid experiments.
  * Currently no graphic interface for construction, only for visualization.
  * 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 for anonymous ftp on
     ftp ftp.gmd.de [129.26.8.90] in the directories
     gmd/as/sesame   and   gmd/as/paper
  Questions please to [e-mail]
  There is only very limited support available. Currently we can not handle
  many users.

21. 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...
   o UNLIMITED (except by machine memory) number of input PATTERNS;
   o UNLIMITED number of input, hidden, and output UNITS;
   o Arbitrary CONNECTIONS among the various layers' units;
   o Clock-time or user-specified RANDOM SEED for initial random weights;
   o Choice of regular GRADIENT DESCENT or QUICKPROP;
   o Choice of PER-EPOCH or PER-PATTERN (stochastic) weight updating;
   o GENERALIZATION to a test dataset;
   o AUTOMATICALLY STOPPED TRAINING based on generalization;
   o RETENTION of best-generalizing weights and predictions;
   o Simple but useful GRAPHIC display to show smoothness of generalization;
   o SAVING of results to a file while working interactively;
   o SAVING of weights file and reloading for continued training;
   o PREDICTION-only on datasets by applying an existing weights file;
   o 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:
   "ftp.scs.unr.edu" in the directory "pub/goodman/nevpropdir".
  *** PLANS FOR NEXT RELEASE:
   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.
   (If you'd like to beta test prerelease version, contact [e-mail])
  *** SUPPORT:
   Limited support is available from Phil Goodman ([e-mail]),
   University of Nevada Center for Biomedical Research.

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

23. PYGMALION
   This is a prototype that stems from an ESPRIT project. It implements
   back-propagation, self organising map, and Hopfield nets.
   On imag.imag.fr [129.88.32.1] in directory archive/pygmalion:
   pygmalion.tar.Z (1534 kb)

24. Basis-of-AI-backprop:
   Here are some of the details of a set of back-propagation programs I
   have been working on.  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:
   * in C for UNIX and DOS and DOS binaries
   * gradient descent, delta-bar-delta and quickprop
   * extra fast 16-bit fixed point weight version as well as a conventional
     floating point version
   * recurrent networks
   * numerous sample problems
   To get this version simply ftp to ftp.mcs.com where you will land in the
   directory /work/public/mcsnet.users. Then cd to drt and read readme.1st.
   The expanded professional version is $30/copy for ordinary
   individuals including academics and $200/copy for businesses and
   government agencies.  Prices and contents subject to change without
   notice.  Some of the highlights are an improved user interface, more
   activation functions, networks can be read into your own programs,
   dynamic node creation, weight decay, SuperSAB
   Contact: Don Tveter; 5228 N. Nashville Ave.; Chicago, Illinois 60656
            [e-mail]

25. Matrix Backpropagation
   MBP (Matrix Back Propagation) is an 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:/pub/ [130.251.89.154]
   as MBPv1.1.tar.Z (unix version) and MBPv11.zip (DOS version).  The
   documentation is included in the distribution as the postscript file
   mbpv11.ps. For more information, contact Davide Anguita
   <[e-mail]> or <[e-mail]>.

26. 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 wuarcive.wustl.edu in pub/MSDOS_UPLOADS/win3
   and pub/MSDOS_UPLOADS/win; the file name is WINNN09.ZIP (542 kB).

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 14)
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.

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

-A16.) Commercial software packages for NN simulation ?

[preliminary]
[who will write some short comment on each of the most
 important packages ?]

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

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
  All Software has a 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 Neural 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.

  Feature                 BrainMaker  Professional    Benefit

  User Interface
  Pull-down Menus, Dialog Boxes   {    {    easy to learn and use; all parameters
                                            saved in a file you can edit.
  Programmable Output Files       {    {    exports data in your format to
                                            spreadsheets, graphics packages, etc.
  Editing in BrainMaker           {    {    quickly edit data, display, network
                                            connections, and more.
  Network Progress Display             {    monitors training with a simple
                                            graphic display.
  Fact Annotation                 {    {    attaches your comments to examples
                                            for display and printing.
  Printer Support                 {    {    HP LaserJet, DeskJet, InkJet,
                                            IBM Proprinter, Epson, etc.
  NetPlotter                      T    {    see how the input correlates with
                                            your output.
  Graphics Built In                    {    shows trends, cycles, network
                                            responses, statistics, etc.;
                                            see it on screen, plotter, or printer.
  Dynamic Data Exchange                {    puts your network in other windows
                                            programs

  Performance
  Binary Mode                     T    {    uses binary files for greater speed.
  Batch Mode                           {    add networks to your existing
                                            programs, train while you're away.
  EMS and XMS Memory                   {    up to 8192 independent variables.
  Save Network Periodically       {    {    saves results to a file in case of
                                            power failure.
  Fastest Algorithms              {    {    750,000 connections-per-second
                                            (486/50).
  Neurons per Layer             512  32,000 more inputs: model complex data
                                            with ease.
  Number of Layers                8    8    extra hidden layers can help tackle
                                            bigger problems.

  Training
  Specify Parameters by Layer          {    fine-tunes performance inside the netw
  Recurrence Networks                  {    Puts feedback in your network,
                                            automates historical input.
  Prune Connections and Neurons        {    improves accuracy by trimming away
                                            excess "fat".
  Add Hidden Neurons In Training  {    {    finds best size network quickly;
                                            fully automated with Professional.
  Custom Neuron Functions         {    {    optimizes training to suit any need.
  Testing While Training          {    {    trains for best performance on new
                                            data.
  Stop training when...                {    lets you decide when network has
                                            learned well.
  Heavy Weights                        {    helps networks train.
  Hypersonic Training             T    {    trains faster with this proprietary
                                            algorithm.

  Analysis, Advanced Functions
  Sensitivity Analysis                 {    shows you which inputs determined
                                            your results.
  Neuron Sensitivity                   {    shows you the total effect of one
                                            input on your results.
  Global Network Analysis              {    shows how the networks reacts to
                                            your inputs overall
  Contour Analysis                     {    shows peaks and valleys of the output
                                            when two inputs change
  Data Correlator                      {    finds important data and optimum
                                            time delays.
  Error Statistics Report         {    {    check your network error rate during
                                            training.
  Print or Edit Weight Matrices   {    {    examine, customize network internals.
  Competitor                           {    ranks horses, teams, stocks, etc.
                                            in finish order.
  Run Time System                      {    C source code - make programs with
                                            your network for resale.
  Chip Support                    {    {    Intel, American Neurologics,
                                            Micro Devices.
  Genetic Training Option              G    trains variations of your design
                                            and shows you which was the best.

  Network Data Management Functions
  NetMaker                        {    {    spreadsheet-like data manipulation
                                            and network file creation.
  NetChecker                      {    {    checks your files for errors and
                                            inconsistencies.
  Shuffle                         {    {    mixes up the order of examples for
                                            better training.
  Binary                          T    {    converts files to binary for quicker
                                            training.
  MinMax                          {    {    finds min / max / standard deviation
                                            of data for fine-tuned results.
  Data Importation                {    {    reads data from Lotus, dBASE,
                                            Excel, ASCII, binary.
  Finacial Data                        {    reads MetaStock, and Computrack
  Data Manipulation               {    {    finds indicators, oscillators,
                                            running averages, etc.
  Cyclic Analysis                      {    checks data for periodic or cyclic
                                            behavior.
  Data Types                      {    {    uses symbolic, text, picture,
                                            and numeric data.

  Documentation & User Support
  User's Guide                    {    {    an application development guide
                                            and quick-start booklet.
  Introduction to Neural Networks {    {    324 pp, gets you up to date in this
                                            exciting field.


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]
  (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 true 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$999 per single user
   license. For a limited period only Genetica is bundled free-of-charge.
   Please email us ([e-mail]) for order form.
     More information about NeuroForecaster(TM)/Genetical may be found in
   ftp.nus.sg incoming/accel.
     NeuroForecaster is a user-friendly neural network program specifically
   designed for building sophisticated and powerful forecasting and
   decision-support systems (Time-Series Forecasting, Cross-Sectional
   Classification, Indicator Analysis)
   Features:
   * GENETICA Net Builder Option for automatic network creation and optimization
   * 12 Neuro-Fuzzy Network Models
   * Multitasking & Background Training Mode
   * Unlimited Network Capacity
   * Rescaled Range Analysis & Hurst Exponent to Unveil Hidden Market Cycles
     & Check for Predictability
   * Correlation Analysis to Compute Correlation Factors to Analyze the
     Significance of Indicators
   * Weight Histogram to Monitor the Progress of Learning
   * Accumulated Error Analysis to Analyze the Strength of Input Indicators
   Its user-friendly interface allows the users to build applications quickly,
   easily and interactively, analyze the data visually and see the results
   immediately.
   The following example applications are included in the package:
   * Credit Rating - for generating the credit rating of bank loan applications
   * Stock market 6 monthly returns forecast
   * Stock selection based on company ratios
   * US$ 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 even on slow
     machines
   * Radial Basis Function Networks - best for pattern classification problems
   * Neuro-Fuzzy Network - combines the power of neuro and fuzzy computing
     technologies
   * Rescaled Range Analysis - computes Hurst exponents to unveil hidden cycles &
     check for predictability
   * Correlation Analysis - to identify significant input indicators

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

-A17.) Neural Network hardware ?

[preliminary]
[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 list of companies contributed by [e-mail]:

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: 503 - 690 - 1236   FAX: 503 - 690 - 1249

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.


And here is an incomplete list of Neurocomputers
(provided by [e-mail] (Jon Gunnar Solheim)):

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 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 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.
(The report archived on neuroprose}


\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.

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

-A19.) Databases for experimentation with NNs ?

[are there any more ?]

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

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


   NIST special database 4:
     8-Bit Gray Scale Images of Fingerprint Image Groups (FIGS)

     The NIST database of fingerprint images contains 2000 8-bit gray scale
     fingerprint image pairs. Each image is 512 by 512 pixels with 32 rows
     of white space at the bottom and classified using one of the five
     following classes: A=Arch, L=Left Loop, R=Right Loop, T=Tented Arch,
     W=Whirl. The database is evenly distributed over each of the five
     classifications with 400 fingerprint pairs from each class. The images
     are compressed using a modified JPEG lossless compression algorithm
     and require approximately 636 Megabytes of storage compressed and 1.1
     Gigabytes uncompressed (1.6 : 1 compression ratio). The database also
     includes format documentation and example software.

   More short overview:
   Special Database 1 - NIST Binary Images of Printed Digits, Alphas, and Text
   Special Database 2 - NIST Structured Forms Reference Set of Binary Images
   Special Database 3 - NIST Binary Images of Handwritten Segmented Characters
   Special Database 4 - NIST 8-bit Gray Scale Images of Fingerprint Image Groups
   Special Database 6 - NIST Structured Forms Reference Set 2 of Binary Images
   Special Database 7 - NIST Test Data 1: Binary Images of Handprinted Segmented
                        Characters
   Special Software 1 - NIST Scoring Package Release 1.0

   Special Database 1 - $895.00
   Special Database 2 - $250.00
   Special Database 3 - $895.00
   Special Database 4 - $250.00
   Special Database 6 - $250.00
   Special Database 7 - $1,000.00
   Special Software 1 - $1,150.00

   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]

   If you wish to order the database, please contact:
     Standard Reference Data
     National Institute of Standards and Technology
     221/A323
     Gaithersburg, MD 20899
     [phone]  or  [phone] (FAX)

4. 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)

5. AI-CD-ROM (see above under "other sources of information about NNs")

6. Time series archive
   Various datasets of time series (to be used for prediction learning
   problems) are available for anonymous ftp at
   ftp.santafe.edu in pub/Time-Series.
   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)

Allen Bonde <[e-mail]>
Accel Infotech Spore Pte Ltd <[e-mail]>
Alexander Linden <[e-mail]>
S.Taimi Ames <[e-mail]>
Axel Mulder <[e-mail]>
[e-mail]
Davide Anguita <[e-mail]>
Avraam Pouliakis <[e-mail]>
Kim L. Blackwell <[e-mail]>
Paul Bakker <[e-mail]>
Jamshed Bharucha <[e-mail]>
Yijun Cai <[e-mail]>
L. Leon Campbell <[e-mail]>
Yaron Danon <[e-mail]>
David Ewing <[e-mail]>
David DeMers <[e-mail]>
Denni Rognvaldsson <[e-mail]>
Donald Tveter <[e-mail]>
Frank Schnorrenberg <[e-mail]>
Gary Lawrence Murphy <[e-mail]>
[e-mail]
Lee Giles <[e-mail]>
Glen Clark <opto![e-mail]>
Phil Goodman <[e-mail]>
[e-mail]
Joerg Heitkoetter <[e-mail]>
Ralf Hohenstein <[e-mail]>
Jean-Denis Muller <[e-mail]>
Jeff Harpster <uu0979![e-mail]>
Jonathan Kamens <[e-mail]>
JJ Merelo <[e-mail]>
Jon Gunnar Solheim <[e-mail]>
Josef Nelissen <[e-mail]>
[e-mail]
Luke Koops <[e-mail]>
William Mackeown <[e-mail]>
Peter Marvit <[e-mail]>
[e-mail]
Yoshiro Miyata <[e-mail]>
Madhav Moganti <[e-mail]>
Jyrki Alakuijala <[e-mail]>
[e-mail]
Maciek Sitnik <[e-mail]>
R. Steven Rainwater <[e-mail]>
Michael Plonski <[e-mail]>
Lutz Prechelt <[e-mail]> [creator of FAQ]
Richard Andrew Miles Outerbridge <[e-mail]>
Richard Cornelius <[e-mail]>
Rob Cunningham <[e-mail]>
[e-mail]
Osamu Saito <[e-mail]>
Sheryl Cormicle <[e-mail]>
Ted Stockwell <[e-mail]>
Thomas G. Dietterich <[e-mail]>
[e-mail]
Ulrich Wendl <[e-mail]>
Matthew P Wiener <[e-mail]>
Wesley Elsberry <[e-mail]>

Bye

  Lutz

--
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.