Archive-name: neural-net-faq
Last-modified: 1995/02/23
URL: http://wwwipd.ira.uka.de/~prechelt/FAQ/neural-net-faq.html
Maintainer: prechelt@ira.uka.de (Lutz Prechelt)

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

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

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
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server as well. Send an E-mail message to mail-server@rtfm.mit.edu with "help"

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

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

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

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

And now, in the end, we begin:

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

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

5. What does 'backprop' mean? What is 'overfitting'?
6. Why use a bias input? Why activation functions?
7. How many hidden units should I use?
8. How many learning methods for NNs exist? Which?
11. How are NNs related to statistical methods?

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

17. Freely available software packages for NN simulation?
18. Commercial software packages for NN simulation?
19. Neural Network hardware?

20. Databases for experimentation with NNs?

******************************

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

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

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

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

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

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

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

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

As opposed to requests, questions ask for a larger piece of information
or a more or less detailed explanation of something. To avoid lots of
redundant traffic it is important that the poster provides with the
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
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)

++++++++++

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).

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
should be announced in the original posting of the question or request
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:
redundancies, irrelevancies, verbosities, and errors should be
filtered out (as good as possible)
o the answers should be separated clearly
o the contributors of the individual answers should be identifiable
(unless they requested to remain anonymous [yes, that happens])
answers, as seen by the original poster
o A summary should, when posted, clearly be indicated to be one
by giving it a Subject line starting with "SUMMARY:"
Note that a good summary is pure gold for the rest of the newsgroup
community, so summary work will be most appreciated by all of us.
Good summaries are more valuable than any moderator ! :-)

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

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

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

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

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

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

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

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

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

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

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

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

A vague description is as follows:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

(this is the classic one) or one of the dozens of other books or articles on

'Overfitting' (often also called 'overtraining' or 'overlearning') is the
phenomenon that in most cases a network gets worse instead of better after a
certain point during training when it is trained to as low errors as possible.
This is because such long training may make the network 'memorize' the
training patterns, including all of their peculiarities. However, one is usually
interested in the generalization of the network, i.e., the error it exhibits on
examples NOT seen during training. Learning the peculiarities of the training
set makes the generalization worse. The network should only learn the general
structure of the examples.

There are various methods to fight overfitting. The two most important classes
of such methods are regularization methods (such as weight decay) and early
stopping. Regularization methods try to limit the complexity of the network
such that it is unable to learn peculiarities. Early stopping aims at stopping the
training at the point of optimal generalization. A description of the early
stopping method can for instance be found in section 3.3 of
/pub/papers/techreports/1994-21.ps.Z on ftp.ira.uka.de (anonymous ftp).

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

6. A: Why use a bias input? Why activation functions?
======================================================

One way of looking at the need for bias inputs is that the inputs to each unit in
the net define an N-dimensional space, and the unit draws a hyperplane
through that space, producing an "on" output on one side and an "off" output
on the other. (With sigmoid units the plane will not be sharp -- there will be
some gray area of intermediate values near the separating plane -- but ignore
this for now.)
The weights determine where this hyperplane is in the input space. Without a
bias input, this separating plane is constrained to pass through the origin of the
hyperspace defined by the inputs. For some problems that's OK, but in many
problems the plane would be much more useful somewhere else. If you have
many units in a layer, they share the same input space and without bias would
ALL be constrained to pass through the origin.

Activation functions are needed to introduce nonlinearity into the network.
Without nonlinearity, hidden units would not make nets more powerful than
just plain perceptrons (which do not have any hidden units, just input and
output units). The reason is that a composition of linear functions is again a
linear function. However, it is just the nonlinearity (i.e, the capability to
represent nonlinear functions) that makes multilayer networks so powerful.
Almost any nonlinear function does the job, although for backpropagation
learning it must be differentiable and it helps if the function is bounded; the
popular sigmoidal functions and gaussian functions are the most common
choices.

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

7. A: How many hidden units should I use?
==========================================

There is no way to determine a good network topology just from the number
of inputs and outputs. It depends critically on the number of training examples
and the complexity of the classification you are trying to learn. There are
problems with one input and one output that require millions of hidden units,
and problems with a million inputs and a million outputs that require only one
hidden unit, or none at all.
Some books and articles offer "rules of thumb" for choosing a topopology --
Ninputs plus Noutputs dividied by two, maybe with a square root in there
somewhere -- but such rules are total garbage. Other rules relate to the
number of examples available: Use at most so many hidden units that the
number of weights in the network times 10 is smaller than the number of
examples. Such rules are only concerned with overfitting and are unreliable as
well.

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

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

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

The main categorization of these methods is the distinction 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:
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)
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
c). Backpropagation (BP)
d). Cauchy Machine (CM)
f). Time Delay Neural Network (TDNN)
g). Associative Reward Penalty (ARP)
h). Avalanche Matched Filter (AMF)
i). Backpercolation (Perc)
j). Artmap
m). Extended Kalman Filter(EKF)

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

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

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

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

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

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

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

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

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

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

For more details see (for example):

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

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

11. A: How are NNs related to statistical methods?
===================================================

There is considerable overlap between the fields of neural networks and
statistics.
Statistics is concerned with data analysis. In neural network terminology,
statistical inference means learning to generalize from noisy data. Some neural
networks are not concerned with data analysis (e.g., those intended to model
biological systems) and therefore have little to do with statistics. Some neural
networks do not learn (e.g., Hopfield nets) and therefore have little to do with
statistics. Some neural networks can learn successfully only from noise-free
data (e.g., ART or the perceptron rule) and therefore would not be considered
statistical methods. But most neural networks that can learn to generalize
effectively from noisy data are similar or identical to statistical methods. For
example:
o Feedforward nets with no hidden layer (including functional-link
neural nets and higher-order neural nets) are basically generalized
linear models.
o Feedforward nets with one hidden layer are closely related to
projection pursuit regression.
o Probabilistic neural nets are identical to kernel discriminant analysis.
o Kohonen nets for adaptive vector quantization are very similar to
k-means cluster analysis.
o Hebbian learning is closely related to principal component analysis.
Some neural network areas that appear to have no close relatives in the
existing statistical literature are:
o Kohonen's self-organizing maps.
o Reinforcement learning.
o Stopped training (the purpose and effect of stopped training are similar
to shrinkage estimation, but the method is quite different).
Feedforward nets are a subset of the class of nonlinear regression and
discrimination models. Statisticians have studied the properties of this general
class but had not considered the specific case of feedforward neural nets before
such networks were popularized in the neural network field. Still, many
results from the statistical theory of nonlinear models apply directly to
feedforward nets, and the methods that are commonly used for fitting
nonlinear models, such as various Levenberg-Marquardt and conjugate
gradient algorithms, can be used to train feedforward nets.

While neural nets are often defined in terms of their algorithms or
implementations, statistical methods are usually defined in terms of their
results. The arithmetic mean, for example, can be computed by a (very simple)
backprop net, by applying the usual formula SUM(x_i)/n, or by various other
methods. What you get is still an arithmetic mean regardless of how you
compute it. So a statistician would consider standard backprop, Quickprop,
and Levenberg-Marquardt as different algorithms for implementing the same
statistical model such as a feedforward net. On the other hand, different
training criteria, such as least squares and cross entropy, are viewed by
statisticians as fundamentally different estimation methods with different
statistical properties.

It is sometimes claimed that neural networks, unlike statistical models, require
no distributional assumptions. In fact, neural networks involve exactly the
same sort of distributional assumptions as statistical models, but statisticians
study the consequences and importance of these assumptions while most neural
networkers ignore them. For example, least-squares training methods are
widely used by statisticians and neural networkers. Statisticians realize that
least-squares training involves implicit distributional assumptions in that
least-squares estimates have certain optimality properties for noise that is
normally distributed with equal variance for all training cases and that is
independent between different cases. These optimality properties are
consequences of the fact that least-squares estimation is maximum likelihood
under those conditions. Similarly, cross-entropy is maximum likelihood for
noise with a Bernoulli distribution. If you study the distributional
assumptions, then you can recognize and deal with violations of the
assumptions. For example, if you have normally distributed noise but some
training cases have greater noise variance than others, then you may be able to
use weighted least squares instead of ordinary least squares to obtain more
efficient estimates.

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

12. A: Good introductory literature about Neural Networks?
==========================================================

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

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

Hertz, J., Krogh, A., and Palmer, R. (1991). Introduction to the Theory of
Neural Computation. Addison-Wesley: Redwood City, California. ISBN
0-201-50395-6 (hardbound) and 0-201-51560-1 (paperbound) Comments:
"My first impression is that this one is by far the best book on the topic. And
it's below $30 for the paperback."; "Well written, theoretical (but not overwhelming)"; It provides a good balance of model development, computational algorithms, and applications. The mathematical derivations are especially well done"; "Nice mathematical analysis on the mechanism of different learning algorithms"; "It is NOT for mathematical beginner. If you don't have a good grasp of higher level math, this book can be really tough to get through." Masters,Timothy (1994). Practical Neural Network Recipes in C++. Academic Press, ISBN 0-12-479040-2, US$45 incl. disks. "Lots of very good practical
advice which most other books lack."

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

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

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

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

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

Freeman, James (1994). Simulating Neural Networks with Mathematica,
NNs. The mathematica code for the programs in the book is also available
through the internet: Send mail to MathSource@wri.com or try
http://www.wri.com/ on the World Wide Web.

good book", "comprises a nice historical overview and a chapter about NN
hardware. Well structured prose. Makes important concepts clear."

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

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

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

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

Rao, V.B & H.V. (1993). C++ Neural Networks and Fuzzy Logic. MIS:Press,
ISBN 1-55828-298-x, US $45 incl. disks. "Probably not 'leading edge' stuff but detailed enough to get your hands dirty!" Wasserman, P. D. (1989). Neural Computing: Theory & Practice. Van Nostrand Reinhold: New York. (ISBN 0-442-20743-3) Comments: "Wasserman flatly enumerates some common architectures from an engineer's perspective ('how it works') without ever addressing the underlying fundamentals ('why it works') - important basic concepts such as clustering, principal components or gradient descent are not treated. It's also full of errors, and unhelpful diagrams drawn with what appears to be PCB board layout software from the '70s. For anyone who wants to do active research in the field I consider it quite inadequate"; "Okay, but too shallow"; "Quite easy to understand"; "The best bedtime reading for Neural Networks. I have given this book to numerous collegues who want to know NN basics, but who never plan to implement anything. An excellent book to give your manager." Wasserman, P.D. (1993). Advanced Methods in Neural Computing. Van Nostrand Reinhold: New York (ISBN: 0-442-00461-3). Comments: Several neural network topics are discussed e.g. Probalistic Neural Networks, Backpropagation and beyond, neural control, Radial Basis Function Networks, Neural Engineering. Furthermore, several subjects related to neural networks are mentioned e.g. genetic algorithms, fuzzy logic, chaos. Just the functionality of these subjects is described; enough to get you started. Lots of references are given to more elaborate descriptions. Easy to read, no extensive mathematical background necessary. 2.) The classics: +++++++++++++++++ Kohonen, T. (1984). Self-organization and Associative Memory. Springer-Verlag: New York. (2nd Edition: 1988; 3rd edition: 1989). Comments: "The section on Pattern mathematics is excellent." Rumelhart, D. E. and McClelland, J. L. (1986). Parallel Distributed Processing: Explorations in the Microstructure of Cognition (volumes 1 & 2). The MIT Press. Comments: "As a computer scientist I found the two Rumelhart and McClelland books really heavy going and definitely not the sort of thing to read if you are a beginner."; "It's quite readable, and affordable (about$65 for both volumes)."; "THE Connectionist bible".

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

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

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

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

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

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

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

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

Cichocki, A. and Unbehauen, R. (1994). Neural Networks for Optimization
and Signal Processing. John Wiley & Sons, West Sussex, England, 1993, ISBN
0-471-930105 (hardbound), 526 pages, $57.95. "Partly a textbook and partly a research monograph; introduces the basic concepts, techniques, and models related to neural networks and optimization, excluding rigorous mathematical details. Accessible to a wide readership with a differential calculus background. The main coverage of the book is on recurrent neural networks with continuous state variables. The book title would be more appropriate without mentioning signal processing. Well edited, good illustrations." Khanna, T. (1990). Foundations of Neural Networks. Addison-Wesley: New York. Comments: "Not so bad (with a page of erroneous formulas (if I remember well), and #hidden layers isn't well described)."; "Khanna's intention in writing his book with math analysis should be commended but he made several mistakes in the math part". Kung, S.Y. (1993). Digital Neural Networks, Prentice Hall, Englewood Cliffs, NJ. Levine, D. S. (1990). Introduction to Neural and Cognitive Modeling. Lawrence Erlbaum: Hillsdale, N.J. Comments: "Highly recommended". Lippmann, R. P. (April 1987). An introduction to computing with neural nets. IEEE Acoustics, Speech, and Signal Processing Magazine. vol. 2, no. 4, pp 4-22. Comments: "Much acclaimed as an overview of neural networks, but rather inaccurate on several points. The categorization into binary and continuous- valued input neural networks is rather arbitrary, and may work confusing for the unexperienced reader. Not all networks discussed are of equal importance." Maren, A., Harston, C. and Pap, R., (1990). Handbook of Neural Computing Applications. Academic Press. ISBN: 0-12-471260-6. (451 pages) Comments: "They cover a broad area"; "Introductory with suggested applications implementation". Pao, Y. H. (1989). Adaptive Pattern Recognition and Neural Networks Addison-Wesley Publishing Company, Inc. (ISBN 0-201-12584-6) Comments: "An excellent book that ties together classical approaches to pattern recognition with Neural Nets. Most other NN books do not even mention conventional approaches." Rumelhart, D. E., Hinton, G. E. and Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, vol 323 (9 October), pp. 533-536. Comments: "Gives a very good potted explanation of backprop NN's. It gives sufficient detail to write your own NN simulation." Simpson, P. K. (1990). Artificial Neural Systems: Foundations, Paradigms, Applications and Implementations. Pergamon Press: New York. Comments: "Contains a very useful 37 page bibliography. A large number of paradigms are presented. On the negative side the book is very shallow. Best used as a complement to other books". Zeidenberg. M. (1990). Neural Networks in Artificial Intelligence. Ellis Horwood, Ltd., Chichester. Comments: "Gives the AI point of view". Zornetzer, S. F., Davis, J. L. and Lau, C. (1990). An Introduction to Neural and Electronic Networks. Academic Press. (ISBN 0-12-781881-2) Comments: "Covers quite a broad range of topics (collection of articles/papers )."; "Provides a primer-like introduction and overview for a broad audience, and employs a strong interdisciplinary emphasis". ------------------------------------------------------------------------ 13. A: 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.: 10 issues/year (vol. 1 in 1988) Cost/Yr: Free with INNS or JNNS or ENNS membership ($45?),
Individual $65, Institution$175
ISSN #:  0893-6080
Remark:  Official Journal of International Neural Network Society (INNS),
European Neural Network Society (ENNS) and Japanese Neural
Network Society (JNNS).
Contains Original Contributions, Invited Review Articles, Letters
to Editor, Book Reviews, Editorials, Announcements, Software Surveys.

Title:   Neural Computation
Publish: MIT Press
Address: MIT Press Journals, 55 Hayward Street Cambridge,
MA 02142-9949, USA, Phone: (617) 253-2889
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 "plonski@aero.org")

Title:   IEEE Transactions on Neural Networks
Publish: Institute of Electrical and Electronics Engineers (IEEE)
Address: IEEE Service Cemter, 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ,
08855-1331 USA. Tel: (201) 981-0060
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., 1060 Main Street, River Edge, NJ 07666. Tel: (201) 487 9655; Europe: World Scientific Publishing Co. Ltd., 57 Shelton Street, London WC2H 9HE, England. Tel: (0171) 836 0888; Asia: World Scientific Publishing Co. Pte. Ltd., 1022 Hougang Avenue 1 #05-3520, Singapore 1953, Rep. of Singapore Tel: 382 5663. Freq.: Quarterly (Vol. 1 in 1990) Cost/Yr: Individual$122, Institution $255 (plus$15-$25 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. Contributions include research papers, reviews, and Letters to the Editor - communications under 3,000 words in length, which are published within six months of receipt. Other contributions are typically published within nine months. The journal presents a fresh undogmatic attitude towards this multidisciplinary field and aims to be a forum for novel ideas and improved understanding of collective and cooperative phenomena with computational capabilities. Papers should be submitted to World Scientific's UK office. Once a paper is accepted for publication, authors are invited to e-mail the LaTeX source file of their paper in order to expedite publication. Title: International Journal of Neurocomputing Publish: Elsevier Science Publishers, Journal Dept.; PO Box 211; 1000 AE Amsterdam, The Netherlands Freq.: Quarterly (vol. 1 in 1989) Editor: V.D. Sanchez A.; German Aerospace Research Establishment; Institute for Robotics and System Dynamics, 82230 Wessling, Germany. Current events and software news editor: Dr. F. Murtagh, ESA, Karl-Schwarzschild Strasse 2, D-85748, Garching, Germany, phone +49-89-32006298, fax +49-89-32006480, email fmurtagh@eso.org Title: Neural Processing Letters Publish: D facto publications Address: 45 rue Masui; B-1210 Brussels, Belgium Phone: (32) 2 245 43 63; Fax: (32) 2 245 46 94 Freq: 6 issues/year (vol. 1 in September 1994) Cost/Yr: BEF 4400 (about$140)
ISSN #:  1370-4621
Remark:  The aim of the journal is to rapidly publish new ideas, original
developments and work in progress.  Neural Processing Letters
covers all aspects of the Artificial Neural Networks field.
Publication delay is about 3 months.
FTP server available:
ftp://ftp.dice.ucl.ac.be/pub/neural-nets/NPL.
WWW server available:
http://www.dice.ucl.ac.be/neural-nets/NPL/NPL.html

Title:   Neural Network News
Publish: AIWeek Inc.
Address: Neural Network News, 2555 Cumberland Parkway, Suite 299,
Atlanta, GA 30339 USA. Tel: (404) 434-2187
Freq.:   Monthly (beginning September 1989)
Cost/Yr: USA and Canada $249, Elsewhere$299

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 kehoe@csufres.CSUFresno.EDU) 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 "anjos@sci.kun.nl") Title: Sixth Generation Systems (formerly Neurocomputers) Publish: Gallifrey Publishing Address: Gallifrey Publishing, PO Box 155, Vicksburg, Michigan, 49097, USA Tel: (616) 649-3772, 649-3592 fax Freq. Monthly (1st issue January, 1987) ISSN #: 0893-1585 Editor: Derek F. Stubbs Cost/Yr:$79 (USA, Canada), US$95 (elsewhere) Remark: Runs eight to 16 pages monthly. In 1995 will go to floppy disc-based publishing with databases +, "the equivalent to 50 pages per issue are planned." Often focuses on specific topics: e.g., August, 1994 contains two articles: "Economics, Times Series and the Market," and "Finite Particle Analysis - [part] II." Stubbs also directs the company Advanced Forecasting Technologies. (Remark by Ed Rosenfeld: ier@aol.com) 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 "saito@nttica.NTT.JP") Title: Neural Networks Today Remark: I found this title in a bulletin board of october last year. It was a message of Tim Pattison, timpatt@augean.OZ (Note: remark provided by J.R.M. Smits "anjos@sci.kun.nl") Title: Computer Simulations in Brain Science Title: Internation Journal of Neuroscience Title: Neural Network Computation Remark: Possibly the same as "Neural Computation" Title: Neural Computing and Applications Freq.: Quarterly Publish: Springer Verlag Cost/yr: 120 Pounds Remark: Is the journal of the Neural Computing Applications Forum. Publishes original research and other information in the field of practical applications of neural computing. B. NN Related Journals: +++++++++++++++++++++++ Title: Complex Systems Publish: Complex Systems Publications Address: Complex Systems Publications, Inc., P.O. Box 6149, Champaign, IL 61821-8149, USA Freq.: 6 times per year (1st volume is 1987) ISSN #: 0891-2513 Cost/Yr: Individual$75, Institution $225 Remark: Journal COMPLEX SYSTEMS devotes to rapid publication of research on science, mathematics, and engineering of systems with simple components but complex overall behavior. Send mail to "jcs@complex.ccsr.uiuc.edu" 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 dietrich@bingvaxu.cc.binghamton.edu cfields@nmsu.edu Title: The Behavioral and Brain Sciences Publish: Cambridge University Press Remark: (Expensive as hell, I'm sure.) This is a delightful journal that encourages discussion on a variety of controversial topics. I have especially enjoyed reading some papers in there by Dana Ballard and Stephen Grossberg (separate papers, not collaborations) a few years back. They have a really neat concept: they get a paper, then invite a number of noted scientists in the field to praise it or trash it. They print these commentaries, and give the author(s) a chance to make a rebuttal or concurrence. Sometimes, as I'm sure you can imagine, things get pretty lively. I'm reasonably sure they are still at it--I think I saw them make a call for reviewers a few months ago. Their reviewers are called something like Behavioral and Brain Associates, and I believe they have to be nominated by current associates, and should be fairly well established in the field. That's probably more than I really know about it but maybe if you post it someone who knows more about it will correct any errors I have made. The main thing is that I liked the articles I read. (Note: remarks by Don Wunsch ) Title: International Journal of Applied Intelligence Publish: Kluwer Academic Publishers Remark: first issue in 1990(?) Title: Bulletin of Mathematica Biology Title: Intelligence Title: Journal of Mathematical Biology Title: Journal of Complex System Title: AI Expert Publish: Miller Freeman Publishing Co., for subscription call ++415-267-7672. Remark: Regularly includes ANN related articles, product announcements, and application reports. Listings of ANN programs are available on AI Expert affiliated BBS's Title: International Journal of Modern Physics C Publish: USA: World Scientific Publishing Co., 1060 Main Street, River Edge, NJ 07666. Tel: (201) 487 9655; Europe: World Scientific Publishing Co. Ltd., 57 Shelton Street, London WC2H 9HE, England. Tel: (0171) 836 0888; Asia: World Scientific Publishing Co. Pte. Ltd., 1022 Hougang Avenue 1 #05-3520, Singapore 1953, Rep. of Singapore Tel: 382 5663. Freq: bi-monthly Eds: H. Herrmann, R. Brower, G.C. Fox and S Nose Title: Machine Learning Publish: Kluwer Academic Publishers Address: Kluwer Academic Publishers P.O. Box 358 Accord Station Hingham, MA 02018-0358 USA Freq.: Monthly (8 issues per year; increasing to 12 in 1993) Cost/Yr: Individual$140 (1992); Member of AAAI or CSCSI $88 Remark: Description: Machine Learning is an international forum for research on computational approaches to learning. The journal publishes articles reporting substantive research results on a wide range of learning methods applied to a variety of task domains. The ideal paper will make a theoretical contribution supported by a computer implementation. The journal has published many key papers in learning theory, reinforcement learning, and decision tree methods. Recently it has published a special issue on connectionist approaches to symbolic reasoning. The journal regularly publishes issues devoted to genetic algorithms as well. Title: INTELLIGENCE - The Future of Computing Published by: Intelligence Address: INTELLIGENCE, P.O. Box 20008, New York, NY 10025-1510, USA, 212-222-1123 voice & fax; email: ier@aol.com, CIS: 72400,1013 Freq. Monthly plus four special reports each year (1st issue: May, 1984) ISSN #: 1042-4296 Editor: Edward Rosenfeld Cost/Yr:$395 (USA), US$450 (elsewhere) Remark: Has absorbed several other newsletters, like Synapse/Connection and Critical Technology Trends (formerly AI Trends). Covers NN, genetic algorithms, fuzzy systems, wavelets, chaos and other advanced computing approaches, as well as molecular computing and nanotechnology. Title: Journal of Physics A: Mathematical and General Publish: Inst. of Physics, Bristol Freq: 24 issues per year. Remark: Statistical mechanics aspects of neural networks (mostly Hopfield models). Title: Physical Review A: Atomic, Molecular and Optical Physics Publish: The American Physical Society (Am. Inst. of Physics) Freq: Monthly Remark: Statistical mechanics of neural networks. Title: Information Sciences Publish: North Holland (Elsevier Science) Freq.: Monthly ISSN: 0020-0255 Editor: Paul P. Wang; Department of Electrical Engineering; Duke University; Durham, NC 27706, USA C. Journals loosely related to NNs: +++++++++++++++++++++++++++++++++++ Title: JOURNAL OF COMPLEXITY Remark: (Must rank alongside Wolfram's Complex Systems) Title: IEEE ASSP Magazine Remark: (April 1987 had the Lippmann intro. which everyone likes to cite) Title: ARTIFICIAL INTELLIGENCE Remark: (Vol 40, September 1989 had the survey paper by Hinton) Title: COGNITIVE SCIENCE Remark: (the Boltzmann machine paper by Ackley et al appeared here in Vol 9, 1983) Title: COGNITION Remark: (Vol 28, March 1988 contained the Fodor and Pylyshyn critique of connectionism) Title: COGNITIVE PSYCHOLOGY Remark: (no comment!) Title: JOURNAL OF MATHEMATICAL PSYCHOLOGY Remark: (several good book reviews) ------------------------------------------------------------------------ 14. A: 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. Proceedings appear several months after the conference as a book from Morgan Kaufman, San Mateo, CA. 2. International Joint Conference on Neural Networks (IJCNN) formerly co-sponsored by INNS and IEEE, no longer held. 3. Annual Conference on Neural Networks (ACNN) 4. International Conference on Artificial Neural Networks (ICANN) Annually in Europe. First was 1991. Major conference of European Neur. Netw. Soc. (ENNS) 5. WCNN. Sponsored by INNS. 6. European Symposium on Artificial Neural Networks (ESANN). Anually since 1993 in Brussels, Belgium; late April; conference on the fundamental aspects of artificial neural networks: theory, mathematics, biology, relations between neural networks and other disciplines, statistics, learning, algorithms, models and architectures, self-organization, signal processing, approximation of functions, evolutive learning, etc. Contact: Michel Verleysen, D facto conference services, 45 rue Masui, B-1210 Brussels, Belgium, phone: +32 2 245 43 63, fax: + 32 2 245 46 94, e-mail: esann@dice.ucl.ac.be 7. Artificial Neural Networks in Engineering (ANNIE) Anually since 1991 in St. Louis, Missouri; held in November. (Topics: NN architectures, pattern recognition, neuro-control, neuro-engineering systems. Contact: ANNIE; Engineering Management Department; 223 Engineering Management Building; University of Missouri-Rolla; Rolla, MO 65401; FAX: (314) 341-6567) 8. many many more.... B. Other Conferences ++++++++++++++++++++ 1. International Joint Conference on Artificial Intelligence (IJCAI) 2. Intern. Conf. on Acustics, Speech and Signal Processing (ICASSP) 3. Intern. Conf. on Pattern Recognition. Held every other year. Has a connectionist subconference. Information: General Chair Walter G. Kropatsch <krw@prip.tuwien.ac.at> 4. Annual Conference of the Cognitive Science Society 5. [Vision Conferences?] C. Pointers to Conferences ++++++++++++++++++++++++++ 1. The journal "Neural Networks" has a list of conferences, workshops and meetings in each issue. This is quite interdisciplinary. 2. There is a regular posting on comp.ai.neural-nets from Paultje Bakker: "Upcoming Neural Network Conferences", which lists names, dates, locations, contacts, and deadlines. It is also available for anonymous ftp from ftp.cs.uq.oz.au as /pub/pdp/conferences ------------------------------------------------------------------------ 15. A: Neural Network Associations? =================================== 1. International Neural Network Society (INNS). +++++++++++++++++++++++++++++++++++++++++++++++ INNS membership includes subscription to "Neural Networks", the official journal of the society. Membership is$55 for non-students and
$45 for students per year. Address: INNS Membership, P.O. Box 491166, Ft. Washington, MD 20749. 2. International Student Society for Neural Networks (ISSNNets). ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ Membership is$5 per year. Address: ISSNNet, Inc., P.O. Box 15661,
Boston, MA 02215 USA

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

Address: WINNERS, c/o Judith Dayhoff, 11141 Georgia Ave., Suite
206, Wheaton, MD 20902. Phone: 301-933-9000.

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

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

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

Address: Japanese Neural Network Society; Department of
Engineering, Tamagawa University; 6-1-1, Tamagawa Gakuen,
Machida City, Tokyo; 194 JAPAN; Phone: +81 427 28 3457, Fax: +81
427 28 3597

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

(the French Student Association for Neural Networks); Membership is
100 FF per year; Activities : newsletter, conference (every year), list of
members, electronic forum; Journal 'Valgo' (ISSN 1243-4825);
Contact : acth@loria.fr

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

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

16. A: 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)" To subscribe, send
email to neuron-request@cattell.psych.upenn.edu 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
srctran@world.std.com (Gregory Aharonian) about Neural Network
patents.

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

Modem: 409-737-5222; Sysop: Wesley R. Elsberry; 4160 Pirates'
Beach, Galveston, TX 77554; welsberr@orca.tamu.edu. 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

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

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

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

Network Cybernetics Corporation produces the "AI CD-ROM". It is
an ISO-9660 format CD-ROM and contains a large assortment of
software related to artificial intelligence, artificial life, virtual reality,
and other topics. Programs for OS/2, MS-DOS, Macintosh, UNIX, and
other operating systems are included. Research papers, tutorials, and
other text files are included in ASCII, RTF, and other universal
formats. The files have been collected from AI bulletin boards, Internet
archive sites, University computer deptartments, and other government
and civilian AI research organizations. Network Cybernetics
Corporation intends to release annual revisions to the AI CD-ROM to
keep it up to date with current developments in the field. The AI
CD-ROM includes collections of files that address many specific
AI/AL topics including Neural Networks (Source code and executables
for many different platforms including Unix, DOS, and Macintosh.
ANN development tools, example networks, sample data, tutorials. A
complete collection of Neural Digest is included as well.) The AI
CD-ROM may be ordered directly by check, money order, bank draft,
or credit card from: Network Cybernetics Corporation; 4201 Wingren
Road Suite 202; Irving, TX 75062-2763; Tel 214/650-2002; Fax
214/650-1929; The cost is $129 per disc + shipping ($5/disc domestic
or $10/disc foreign) (See the comp.ai FAQ for further details) 7. World Wide Web +++++++++++++++++ In World-Wide-Web (WWW, for example via the xmosaic program) you can read neural network information for instance by opening one of the following universal resource locators (URLs): http://www.neuronet.ph.kcl.ac.uk (NEuroNet, King's College, London), http://www.eeb.ele.tue.nl (Eindhoven, Netherlands), http://www.msrc.pnl.gov:2080/docs/cie/neural/neural.homepage.html (Richland, Washington), http://www.cosy.sbg.ac.at/~rschwaig/rschwaig/projects.html (Salzburg, Austria), http://http2.sils.umich.edu/Public/nirg/nirg1.html (Michigan). http://rtm.science.unitn.it/ Reactive Memory Search (Tabu Search) page (Trento, Italy). Many others are available too, changing daily. 8. Neurosciences Internet Resource Guide ++++++++++++++++++++++++++++++++++++++++ This document aims to be a guide to existing, free, Internet-accessible resources helpful to neuroscientists of all stripes. An ASCII text version (86K) is available in the Clearinghouse of Subject-Oriented Internet Resource Guides as follows: anonymous FTP, Gopher, WWW Hypertext 9. INTCON mailing list ++++++++++++++++++++++ INTCON (Intelligent Control) is a moderated mailing list set up to provide a forum for communication and exchange of ideas among researchers in neuro-control, fuzzy logic control, reinforcement learning and other related subjects grouped under the topic of intelligent control. Send your subscribe requests to intcon-request@phoenix.ee.unsw.edu.au ------------------------------------------------------------------------ 17. A: Freely available software packages for NN simulation? ============================================================ 1. Rochester Connectionist Simulator ++++++++++++++++++++++++++++++++++++ A quite versatile simulator program for arbitrary types of neural nets. Comes with a backprop package and a X11/Sunview interface. Available via anonymous FTP from cs.rochester.edu [192.5.53.209] in directory pub/simulator as the files README (8 KB), rcs_v4.2.justdoc.tar.Z (1.6 MB, Documentation), rcs_v4.2.justsrc.tar.Z (1.4 MB, Source code), 2. UCLA-SFINX +++++++++++++ ftp retina.cs.ucla.edu [131.179.16.6]; Login name: sfinxftp; Password: joshua; directory: pub; files : README; sfinx_v2.0.tar.Z; Email info request : sfinx@retina.cs.ucla.edu 3. NeurDS +++++++++ simulator for DEC systems supporting VT100 terminal. available for anonymous ftp from gatekeeper.dec.com [16.1.0.2] in directory: pub/DEC as the file NeurDS031.tar.Z (111 Kb) 4. PlaNet5.7 (formerly known as SunNet) +++++++++++++++++++++++++++++++++++++++ A popular connectionist simulator with versions to run under X Windows, and non-graphics terminals created by Yoshiro Miyata (Chukyo Univ., Japan). 60-page User's Guide in Postscript. Send any questions to miyata@sccs.chukyo-u.ac.jp Available for anonymous ftp from ftp.ira.uka.de as /pub/neuron/PlaNet5.7.tar.Z (800 kb) or from boulder.colorado.edu [128.138.240.1] as /pub/generic-sources/PlaNet5.7.tar.Z 5. GENESIS ++++++++++ GENESIS 1.4.2 (GEneral NEural SImulation System) is a general purpose simulation platform which was developed to support the simulation of neural systems ranging from complex models of single neurons to simulations of large networks made up of more abstract neuronal components. Most current GENESIS applications involve realistic simulations of biological neural systems. Although the software can also model more abstract networks, other simulators are more suitable for backpropagation and similar connectionist modeling. Available for ftp with the following procedure: Use 'telnet' to genesis.bbb.caltech.edu and login as the user "genesis" (no password). If you answer all the questions, an 'ftp' account will automatically be created for you. You can then 'ftp' back to the machine and download the software (about 3 MB). Contact: genesis@cns.caltech.edu. Further information via WWW at http://www.bbb.caltech.edu/GENESIS/. 6. Mactivation ++++++++++++++ A neural network simulator for the Apple Macintosh. Available for ftp from ftp.cs.colorado.edu [128.138.243.151] as /pub/cs/misc/Mactivation-3.3.sea.hqx 7. Cascade Correlation Simulator ++++++++++++++++++++++++++++++++ A simulator for Scott Fahlman's Cascade Correlation algorithm. Available for ftp from ftp.cs.cmu.edu [128.2.206.173] in directory /afs/cs/project/connect/code as the file cascor-v1.0.4.shar (218 KB) There is also a version of recurrent cascade correlation in the same directory in file rcc1.c (108 KB). 8. Quickprop ++++++++++++ A variation of the back-propagation algorithm developed by Scott Fahlman. A simulator is available in the same directory as the cascade correlation simulator above in file nevprop1.16.shar (137 KB) (see also the description of NEVPROP below) 9. DartNet ++++++++++ DartNet is a Macintosh-based backpropagation simulator, developed at Dartmouth by Jamshed Bharucha and Sean Nolan as a pedagogical tool. It makes use of the Mac's graphical interface, and provides a number of tools for building, editing, training, testing and examining networks. This program is available by anonymous ftp from dartvax.dartmouth.edu [129.170.16.4] as /pub/mac/dartnet.sit.hqx (124 KB). 10. SNNS ++++++++ "Stuttgart Neural Network Simulator" from the University of Stuttgart, Germany. A luxurious simulator for many types of nets; with X11 interface: Graphical 2D and 3D topology editor/visualizer, training visualisation, multiple pattern set handling etc. Currently supports backpropagation (vanilla, online, with momentum term and flat spot elimination, batch, time delay), counterpropagation, quickprop, backpercolation 1, generalized radial basis functions (RBF), RProp, ART1, ART2, ARTMAP, Cascade Correlation, Recurrent Cascade Correlation, Dynamic LVQ, Backpropagation through time (for recurrent networks), batch backpropagation through time (for recurrent networks), Quickpropagation through time (for recurrent networks), Hopfield networks, Jordan and Elman networks, autoassociative memory, self-organizing maps, time-delay networks (TDNN), and is user-extendable (user-defined activation functions, output functions, site functions, learning procedures). Works on SunOS, Solaris, IRIX, Ultrix, AIX, HP/UX, and Linux. Available for ftp from ftp.informatik.uni-stuttgart.de [129.69.211.2] in directory /pub/SNNS as SNNSv3.2.tar.Z (2 MB, Source code) and SNNSv3.2.Manual.ps.Z (1.4 MB, Documentation). There are also various other files in this directory (e.g. the source version of the manual, a Sun Sparc executable, older versions of the software, some papers, and the software in several smaller parts). It may be best to first have a look at the file SNNSv3.2.Readme (10 kb). This file contains a somewhat more elaborate short description of the simulator. 11. Aspirin/MIGRAINES +++++++++++++++++++++ Aspirin/MIGRAINES 6.0 consists of a code generator that builds neural network simulations by reading a network description (written in a language called "Aspirin") and generates a C simulation. An interface (called "MIGRAINES") is provided to export data from the neural network to visualization tools. The system has been ported to a large number of platforms. The goal of Aspirin is to provide a common extendible front-end language and parser for different network paradigms. The MIGRAINES interface is a terminal based interface that allows you to open Unix pipes to data in the neural network. Users can display the data using either public or commercial graphics/analysis tools. Example filters are included that convert data exported through MIGRAINES to formats readable by Gnuplot 3.0, Matlab, Mathematica, and xgobi. The software is available from two FTP sites: from CMU's simulator collection on pt.cs.cmu.edu [128.2.254.155] in /afs/cs/project/connect/code/am6.tar.Z and from UCLA's cognitive science machine ftp.cognet.ucla.edu [128.97.50.19] in /pub/alexis/am6.tar.Z (2 MB). 12. Adaptive Logic Network kit ++++++++++++++++++++++++++++++ This package differs from the traditional nets in that it uses logic functions rather than floating point; for many tasks, ALN's can show many orders of magnitude gain in training and performance speed. Anonymous ftp from menaik.cs.ualberta.ca [129.128.4.241] in directory /pub/atree. See the files README (7 KB), atree2.tar.Z (145 kb, Unix source code and examples), atree2.ps.Z (76 kb, documentation), a27exe.exe (412 kb, MS-Windows 3.x executable), atre27.exe (572 kb, MS-Windows 3.x source code). 13. NeuralShell +++++++++++++++ Formerly available from FTP site quanta.eng.ohio-state.edu [128.146.35.1] as /pub/NeuralShell/NeuralShell.tar". Currently (April 94) not available and undergoing a major reconstruction. Not to be confused with NeuroShell by Ward System Group (see below under commercial software). 14. PDP +++++++ The PDP simulator package is available via anonymous FTP at nic.funet.fi [128.214.6.100] as /pub/sci/neural/sims/pdp.tar.Z (202 kb). The simulator is also available with the book "Explorations in Parallel Distributed Processing: A Handbook of Models, Programs, and Exercises" by McClelland and Rumelhart. MIT Press, 1988. Comment: "This book is often referred to as PDP vol III which is a very misleading practice! The book comes with software on an IBM disk but includes a makefile for compiling on UNIX systems. The version of PDP available at ftp.funet.fi seems identical to the one with the book except for a bug in bp.c which occurs when you try to run a script of PDP commands using the DO command. This can be found and fixed easily." 15. Xerion ++++++++++ Xerion runs on SGI and Sun machines and uses X Windows for graphics. The software contains modules that implement Back Propagation, Recurrent Back Propagation, Boltzmann Machine, Mean Field Theory, Free Energy Manipulation, Hard and Soft Competitive Learning, and Kohonen Networks. Sample networks built for each of the modules are also included. Contact: xerion@ai.toronto.edu. Xerion is available via anonymous ftp from ftp.cs.toronto.edu [128.100.1.105] in directory /pub/xerion as xerion-3.1.ps.Z (153 kB) and xerion-3.1.tar.Z (1.3 MB) plus several concrete simulators built with xerion (about 40 kB each). 16. Neocognitron simulator ++++++++++++++++++++++++++ The simulator is written in C and comes with a list of references which are necessary to read to understand the specifics of the implementation. The unsupervised version is coded without (!) C-cell inhibition. Available for anonymous ftp from unix.hensa.ac.uk [129.12.21.7] in /pub/neocognitron.tar.Z (130 kB). 17. Multi-Module Neural Computing Environment (MUME) ++++++++++++++++++++++++++++++++++++++++++++++++++++ MUME is a simulation environment for multi-modules neural computing. It provides an object oriented facility for the simulation and training of multiple nets with various architectures and learning algorithms. MUME includes a library of network architectures including feedforward, simple recurrent, and continuously running recurrent neural networks. Each architecture is supported by a variety of learning algorithms. MUME can be used for large scale neural network simulations as it provides support for learning in multi-net environments. It also provide pre- and post-processing facilities. The modules are provided in a library. Several "front-ends" or clients are also available. X-Window support by editor/visualization tool Xmume. MUME can be used to include non-neural computing modules (decision trees, ...) in applications. MUME is available anonymous ftp on mickey.sedal.su.oz.au [129.78.24.170] after signing and sending a licence: /pub/license.ps (67 kb). Contact: Marwan Jabri, SEDAL, Sydney University Electrical Engineering, NSW 2006 Australia, marwan@sedal.su.oz.au 18. LVQ_PAK, SOM_PAK ++++++++++++++++++++ These are packages for Learning Vector Quantization and Self-Organizing Maps, respectively. They have been built by the LVQ/SOM Programming Team of the Helsinki University of Technology, Laboratory of Computer and Information Science, Rakentajanaukio 2 C, SF-02150 Espoo, FINLAND There are versions for Unix and MS-DOS available from cochlea.hut.fi [130.233.168.48] as /pub/lvq_pak/lvq_pak-2.1.tar.Z (340 kB, Unix sources), /pub/lvq_pak/lvq_p2r1.exe (310 kB, MS-DOS self-extract archive), /pub/som_pak/som_pak-1.2.tar.Z (251 kB, Unix sources), /pub/som_pak/som_p1r2.exe (215 kB, MS-DOS self-extract archive). (further programs to be used with SOM_PAK and LVQ_PAK can be found in /pub/utils). 19. SESAME ++++++++++ ("Software Environment for the Simulation of Adaptive Modular Systems") SESAME is a prototypical software implementation which facilitates o Object-oriented building blocks approach. o Contains a large set of C++ classes useful for neural nets, neurocontrol and pattern recognition. No C++ classes can be used as stand alone, though! o C++ classes include CartPole, nondynamic two-robot arms, Lunar Lander, Backpropagation, Feature Maps, Radial Basis Functions, TimeWindows, Fuzzy Set Coding, Potential Fields, Pandemonium, and diverse utility building blocks. o A kernel which is the framework for the C++ classes and allows run-time manipulation, construction, and integration of arbitrary complex and hybrid experiments. o Currently no graphic interface for construction, only for visualization. o Platform is SUN4, XWindows Unfortunately no reasonable good introduction has been written until now. We hope to have something soon. For now we provide papers (eg. NIPS-92), a reference manual (>220 pages), source code (ca. 35.000 lines of code), and a SUN4-executable by ftp only. Sesame and its description is available in various files for anonymous ftp on ftp ftp.gmd.de in the directories /gmd/as/sesame and /gmd/as/paper. Questions to sesame-request@gmd.de; there is only very limited support available. 20. Nevada Backpropagation (NevProp) ++++++++++++++++++++++++++++++++++++ NevProp is a free, easy-to-use feedforward backpropagation (multilayer perceptron) program. It uses an interactive character-based interface, and is distributed as C source code that should compile and run on most platforms. (Precompiled executables are available for Macintosh and DOS.) The original version was Quickprop 1.0 by Scott Fahlman, as translated from Common Lisp by Terry Regier. We added early-stopped training based on a held-out subset of data, c index (ROC curve area) calculation, the ability to force gradient descent (per-epoch or per-pattern), and additional options. FEATURES (NevProp version 1.16): UNLIMITED (except by machine memory) number of input PATTERNS; UNLIMITED number of input, hidden, and output UNITS; Arbitrary CONNECTIONS among the various layers' units; Clock-time or user-specified RANDOM SEED for initial random weights; Choice of regular GRADIENT DESCENT or QUICKPROP; Choice of PER-EPOCH or PER-PATTERN (stochastic) weight updating; GENERALIZATION to a test dataset; AUTOMATICALLY STOPPED TRAINING based on generalization; RETENTION of best-generalizing weights and predictions; Simple but useful GRAPHIC display to show smoothness of generalization; SAVING of results to a file while working interactively; SAVING of weights file and reloading for continued training; PREDICTION-only on datasets by applying an existing weights file; In addition to RMS error, the concordance, or c index is displayed. The c index (area under the ROC curve) shows the correctness of the RELATIVE ordering of predictions AMONG the cases; ie, it is a measure of discriminative power of the model. AVAILABILITY: The most updated version of NevProp will be made available by anonymous ftp from the University of Nevada, Reno: On ftp.scs.unr.edu [134.197.10.130] in the directory "pub/goodman/nevpropdir", e.g. README.FIRST (45 kb) or nevprop1.16.shar (138 kb). VERSION 2 to be released in Spring of 1994 -- some of the new features: more flexible file formatting (including access to external data files; option to prerandomize data order; randomized stochastic gradient descent; option to rescale predictor (input) variables); linear output units as an alternative to sigmoidal units for use with continuous-valued dependent variables (output targets); cross-entropy (maximum likelihood) criterion function as an alternative to square error for use with categorical dependent variables (classification/symbolic/nominal targets); and interactive interrupt to change settings on-the-fly. Limited support is available from Phil Goodman (goodman@unr.edu), University of Nevada Center for Biomedical Research. 21. Fuzzy ARTmap ++++++++++++++++ This is just a small example program. Available for anonymous ftp from park.bu.edu [128.176.121.56] /pub/fuzzy-artmap.tar.Z (44 kB). 22. PYGMALION +++++++++++++ This is a prototype that stems from an ESPRIT project. It implements back-propagation, self organising map, and Hopfield nets. Avaliable for ftp from ftp.funet.fi [128.214.248.6] as /pub/sci/neural/sims/pygmalion.tar.Z (1534 kb). (Original site is imag.imag.fr: archive/pygmalion/pygmalion.tar.Z). 23. Basis-of-AI-backprop ++++++++++++++++++++++++ Earlier versions have been posted in comp.sources.misc and people around the world have used them and liked them. This package is free for ordinary users but shareware for businesses and government agencies ($200/copy, but then for this you get the professional version
as well). I do support this package via email. Some of the highlights
are:
o in C for UNIX and DOS and DOS binaries
o gradient descent, delta-bar-delta and quickprop
o extra fast 16-bit fixed point weight version as well as a
conventional floating point version
o recurrent networks
o numerous sample problems
Available for ftp from ftp.mcs.com in directory /mcsnet.users/drt. Or
see the WWW page http://www.mcs.com/~drt/home.html. The
expanded professional version is $30/copy for ordinary individuals including academics and$200/copy for businesses and government
agencies (improved user interface, more activation functions, networks
decay, SuperSAB). More details can be found in the documentation for
the student version. Contact: Don Tveter; 5228 N. Nashville Ave.;
Chicago, Illinois 60656; drt@mcs.com

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

MBP (Matrix Back Propagation) is a very efficient implementation of
the back-propagation algorithm for current-generation workstations.
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 with respect to conventional
implementations. The software is available by anonymous ftp from
risc6000.dibe.unige.it [130.251.89.154] as /pub/MBPv1.1.tar.Z (Unix
version), /pub/MBPv11.zip.Z (MS-DOS version), /pub/mpbv11.ps
(anguita@dibe.unige.it).

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

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

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

BIOSIM is a biologically oriented neural network simulator. Public
domain, runs on Unix (less powerful PC-version is available, too), easy
to install, bilingual (german and english), has a GUI (Graphical User
facilities, offers controlling interfaces, batch version is available, a
DEMO is provided. REQUIREMENTS (Unix version): X11 Rel. 3 and
above, Motif Rel 1.0 and above, 12 MB of physical memory,
recommended are 24 MB and more, 20 MB disc space.
REQUIREMENTS (PC version): PC-compatible with MS Windows
3.0 and above, 4 MB of physical memory, recommended are 8 MB and
more, 1 MB disc space. Four neuron models are implemented in
BIOSIM: a simple model only switching ion channels on and off, the
original Hodgkin-Huxley model, the SWIM model (a modified HH
model) and the Golowasch-Buchholz model. Dendrites consist of a
chain of segments without bifurcation. A neural network can be created
by using the interactive network editor which is part of BIOSIM.
Parameters can be changed via context sensitive menus and the results
of the simulation can be visualized in observation windows for neurons
and synapses. Stochastic processes such as noise can be included. In
addition, biologically orientied learning and forgetting processes are
modeled, e.g. sensitization, habituation, conditioning, hebbian learning
and competitive learning. Three synaptic types are predefined (an
excitatatory synapse type, an inhibitory synapse type and an electrical
synapse). Additional synaptic types can be created interactively as
desired. Available for ftp from ftp.uni-kl.de in directory
/pub/bio/neurobio: Get /pub/bio/neurobio/biosim.readme (2 kb) and
/pub/bio/neurobio/biosim.tar.Z (2.6 MB) for the Unix version or
/pub/bio/neurobio/biosimpc.zip (150 kb) for the PC version. Contact:
Stefan Bergdoll; Department of Software Engineering (ZXA/US);
BASF Inc.; D-67056 Ludwigshafen; Germany;
bergdoll@zxa.basf-ag.de; phone 0621-60-21372; fax 0621-60-43735

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

The Brain is an advanced neural network simulator for PCs that is
simple enough to be used by non-technical people, yet sophisticated
enough for serious research work. It is based upon the backpropagation
learning algorithm. Three sample networks are included. The
documentation included provides you with an introduction and
overview of the concepts and applications of neural networks as well as
outlining the features and capabilities of The Brain. The Brain requires
512K memory and MS-DOS or PC-DOS version 3.20 or later
(versions for other OS's and machines are available). A 386 (with
maths coprocessor) or higher is recommended for serious use of The
Brain. Shareware payment required. Demo version is restricted to
number of units the network can handle due to memory contraints on
PC's. Registered version allows use of extra memory. External
documentation included: 39Kb, 20 Pages. Source included: No (Source
comes with registration). Available via anonymous ftp from
ftp.tu-clausthal.de as /pub/msdos/science/brain12.zip (78 kb) and from
ftp.technion.ac.il as /pub/contrib/dos/brain12.zip (78 kb) Contact:
David Perkovic; DP Computing; PO Box 712; Noarlunga Center SA
5168; Australia; Email: dip@mod.dsto.gov.au (preferred) or
dpc@mep.com or perkovic@cleese.apana.org.au

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

FuNeGen is a MLP based software program to generate fuzzy rule
based classifiers. A limited version (maximum of 7 inputs and 3
membership functions for each input) for PCs is available for
in directory /pub/neurofuzzy. For further information see the file

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

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

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

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

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

If you are using a small computer (PC, Mac, etc.) you may want to have a look
at the Central Neural System Electronic Bulletin Board (see answer 13).
Modem: 409-737-5312; Sysop: Wesley R. Elsberry; 4160 Pirates' Beach,
Galveston, TX, USA; welsberr@orca.tamu.edu. 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.

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

18. A: Commercial software packages for NN simulation?
======================================================

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

Name: nn/xnn
Company: Neureka ANS
5037 Solheimsviken
NORWAY
Phone:   +47-55544163 / +47-55201548
Email:   arnemo@eik.ii.uib.no
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
Phone,Fax: 916 478 9040, 916 478 9041
Email:  calsci!mittmann@gvgpsa.gvg.tek.com (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: 916 478 9040 Fax: 916 478 9041 Tech Support: 916 478 9035 Mail: 10024 newtown rd, Nevada City, CA, 95959, USA 30 day money back guarantee, and unlimited free technical support. BrainMaker package includes: The book Introduction to Neural Networks BrainMaker Users Guide and reference manual 300 pages , fully indexed, with tutorials, and sample networks Netmaker Netmaker makes building and training Neural Networks easy, by importing and automatically creating BrainMaker's Neural Network files. Netmaker imports Lotus, Excel, dBase, and ASCII files. BrainMaker Full menu and dialog box interface, runs Backprop at 750,000 cps on a 33Mhz 486. ---Features ("P" means is avaliable in professional version only): Pull-down Menus, Dialog Boxes, Programmable Output Files, Editing in BrainMaker, Network Progress Display (P), Fact Annotation, supports many printers, NetPlotter, Graphics Built In (P), Dynamic Data Exchange (P), Binary Data Mode, Batch Use Mode (P), EMS and XMS Memory (P), Save Network Periodically, Fastest Algorithms, 512 Neurons per Layer (P: 32,000), up to 8 layers, Specify Parameters by Layer (P), Recurrence Networks (P), Prune Connections and Neurons (P), Add Hidden Neurons In Training, Custom Neuron Functions, Testing While Training, Stop training when...-function (P), Heavy Weights (P), Hypersonic Training, Sensitivity Analysis (P), Neuron Sensitivity (P), Global Network Analysis (P), Contour Analysis (P), Data Correlator (P), Error Statistics Report, Print or Edit Weight Matrices, Competitor (P), Run Time System (P), Chip Support for Intel, American Neurologics, Micro Devices, Genetic Training Option (P), NetMaker, NetChecker, Shuffle, Data Import from Lotus, dBASE, Excel, ASCII, binary, Finacial Data (P), Data Manipulation, Cyclic Analysis (P), User's Guide quick start booklet, Introduction to Neural Networks 324 pp book 3. SAS Software/ Neural Net add-on ++++++++++++++++++++++++++++++++++ Name: SAS Software Company: SAS Institute, Inc. Address: SAS Campus Drive, Cary, NC 27513, USA Phone,Fax: (919) 677-8000 Email: saswss@unx.sas.com (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: (412) 787-8222 FAX: (412) 787-8220 Distributor for Europe: Scientific Computers GmbH. Franzstr. 107, 52064 Aachen Germany Tel. (49) +241-26041 Fax. (49) +241-44983 Email. info@scientific.de 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: 508-653-1415 24 Prime Park Way FAX: 508-653-2997 Natick, MA 01760 email: info@mathworks.com The Neural Network Toolbox is a powerful collection of MATLAB functions for the design, training, and simulation of neural networks. It supports a wide range of network architectures with an unlimited number of processing elements and interconnections (up to operating system constraints). Supported architectures and training methods include: supervised training of feedforward networks using the perceptron learning rule, Widrow-Hoff rule, several variations on backpropagation (including the fast Levenberg-Marquardt algorithm), and radial basis networks; supervised training of recurrent Elman networks; unsupervised training of associative networks including competitive and feature map layers; Kohonen networks, self-organizing maps, and learning vector quantization. The Neural Network Toolbox contains a textbook-quality Users' Guide, uses tutorials, reference materials and sample applications with code examples to explain the design and use of each network architecture and paradigm. The Toolbox is delivered as MATLAB M-files, enabling users to see the algorithms and implementations, as well as to make changes or create new functions to address a specific application. (Comment by Richard Andrew Miles Outerbridge, RAMO@UVPHYS.PHYS.UVIC.CA:) 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 propagator@ard.com Easy to use neural network training package. A GUI implementation of backpropagation networks with five layers (32,000 nodes per layer). Features dynamic performance graphs, training with a validation set, and C/C++ source code generation. For Sun (Solaris 1.x & 2.x,$499),
PC  (Windows 3.x, $199) Mac (System 7.x,$199)
Floating point coprocessor required, Educational Discount,
Money Back Guarantee, Muliti User Discount
Windows Demo on:
nic.funet.fi        /pub/msdos/windows/demo
oak.oakland.edu     /pub/msdos/neural_nets
gatordem.zip    pkzip 2.04g archive file

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

Name:    NeuroForecaster(TM)/Genetica 3.1
Contact: Accel Infotech (S) Pte Ltd; 648 Geylang Road;
Republic of Singapore 1438; Phone: +65-7446863; Fax: +65-7492467
accel@solomon.technet.sg
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$1199 per single user license. Please email us (accel@solomon.technet.sg) for order form. More information about NeuroForecaster(TM)/Genetical may be found in ftp://ftp.technet.sg/Technet/user/accel/nfga40.exe NeuroForecaster is a user-friendly neural network program specifically designed for building sophisticated and powerful forecasting and decision-support systems (Time-Series Forecasting, Cross-Sectional Classification, Indicator Analysis) Features: * GENETICA Net Builder Option for automatic network optimization * 12 Neuro-Fuzzy Network Models * Multitasking & Background Training Mode * Unlimited Network Capacity * Rescaled Range Analysis & Hurst Exponent to Unveil Hidden Market Cycles & Check for Predictability * Correlation Analysis to Compute Correlation Factors to Analyze the Significance of Indicators * Weight Histogram to Monitor the Progress of Learning * Accumulated Error Analysis to Analyze the Strength of Input Indicators Its user-friendly interface allows the users to build applications quickly, easily and interactively, analyze the data visually and see the results immediately. The following example applications are included in the package: * Credit Rating - for generating the credit rating of bank loan applications. * Stock market 6 monthly returns forecast * Stock selection based on company ratios * US$ to Deutschmark exchange rate forecast
* US$to Yen exchange rate forecast * US$ to SGD exchange rate forecast
* Property price valuation
* XOR - a classical problem to show the results are better than others
* Chaos - Prediction of Mackey-Glass chaotic time series
* SineWave - For demonstrating the power of Rescaled Range Analysis and
significance of window size
Techniques Implemented:
* GENETICA Net Builder Option - network creation & optimization based on
Darwinian evolution theory
* Backprop Neural Networks - the most widely-used training algorithm
* Fastprop Neural Networks - speeds up training of large problems
* Radial Basis Function Networks - best for pattern classification problems
* Neuro-Fuzzy Network
* Rescaled Range Analysis - computes Hurst exponents to unveil hidden
cycles & check for predictability
* Correlation Analysis - to identify significant input indicators

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

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

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

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

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

NeuroWindows is a programmer's tool in a Dynamic Link Library
(DLL) that can create as many as 128 interactive nets in an application,
each with 32 slabs in a single network, and 32K neurons in a slab.
Includes Backpropagation, Kohonen, PNN, and GRNN paradigms.
NeuroWindows can mix supervised and unsupervised nets. The DLL
may be called from Visual Basic, Visual C, Access Basic, C, Pascal,
and VBA/Excel 5. NeuroWindows price: $369. Contact: Ward Systems Group, Inc.; Executive Park West; 5 Hillcrest Drive; Frederick, MD 21702; USA; Phone: 301 662-7950; FAX: 301 662-5666. Contact us for a free demo diskette and Consumer's Guide to Neural Networks. 10. NuTank ++++++++++ NuTank stands for NeuralTank. It is educational and entertainment software. In this program one is given the shell of a 2 dimentional robotic tank. The tank has various I/O devices like wheels, whiskers, optical sensors, smell, fuel level, sound and such. These I/O sensors are connected to Neurons. The player/designer uses more Neurons to interconnect the I/O devices. One can have any level of complexity desired (memory limited) and do subsumptive designs. More complex design take slightly more fuel, so life is not free. All movement costs fuel too. One can also tag neuron connections as "adaptable" that adapt their weights in acordance with the target neuron. This allows neurons to learn. The Neuron editor can handle 3 dimention arrays of neurons as single entities with very flexible interconect patterns. One can then design a scenario with walls, rocks, lights, fat (fuel) sources (that can be smelled) and many other such things. Robot tanks are then introduced into the Scenario and allowed interact or battle it out. The last one alive wins, or maybe one just watches the motion of the robots for fun. While the scenario is running it can be stopped, edited, zoom'd, and can track on any robot. The entire program is mouse and graphicly based. It uses DOS and VGA and is written in TurboC++. There will also be the ability to download designs to another computer and source code will be available for the core neural simulator. This will allow one to design neural systems and download them to real robots. The design tools can handle three dimentional networks so will work with video camera inputs and such. Eventualy I expect to do a port to UNIX and multi thread the sign. I also expect to do a Mac port and maybe NT or OS/2 Copies of NuTank cost$50 each. Contact: Richard Keene; Keene
Educational Software; Dick.Keene@Central.Sun.COM

NuTank shareware with the Save options disabled is available via
anonymous ftp from the Internet, see the file

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

Name: Neuralyst Version 1.4; Company: Cheshire Engineering
91107; Phone: 818-351-0209; Fax: 818-351-8645;

Basic capabilities: training of backpropogation neural nets. Operating
system: Windows or Macintosh running Microsoft Excel Spreadsheet.
Neuralyst is an add-in package for Excel. Approx. price: $195 for windows or Mac. Comments: A simple model that is easy to use. Integrates nicely into Microsoft Excel. Allows user to create, train, and run backprop ANN models entirely within an Excel spreadsheet. Provides macro functions that can be called from Excel macro's, allowing you to build a custom Window's interface using Excel's macro language and Visual Basic tools. The new version 1.4 includes a genetic algorithm to guide the training process. A good bargain to boot. (Comments by Duane Highley, a user and NOT the program developer. dhighley@ozarks.sgcl.lib.mo.us) 12. NeuFuz4 +++++++++++ Name: NeuFuz4 Company: National Semiconductor Corporation Address: 2900 Semiconductor Drive, Santa Clara, CA, 95052, or: Industriestrasse 10, D-8080 Fuerstenfeldbruck, Germany, or: Sumitomo Chemical Engineering Center, Bldg. 7F 1-7-1, Nakase, Mihama-Ku, Chiba-City, Ciba Prefecture 261, JAPAN, or: 15th Floor, Straight Block, Ocean Centre, 5 Canton Road, Tsim Sha Tsui East, Kowloon, Hong Kong, Phone: (800) 272-9959 (Americas), : 011-49-8141-103-0 Germany : 0l1-81-3-3299-7001 Japan : (852) 737-1600 Hong Kong Email: neufuz@esd.nsc.com (Neural net inquiries only) URL: http://www.commerce.net/directories/participants/ns/home.html Basic capabilities: Uses backpropagation techniques to initially select fuzzy rules and membership functions. The result is a fuzzy associative memory (FAM) which implements an approximation of the training data. Operating Systems: 486DX-25 or higher with math co-processor DOS 5.0 or higher with Windows 3.1, mouse, VGA or better, minimum 4 MB RAM, and parallel port. Approx. price : depends on version - see below. Comments : Not for the serious Neural Network researcher, but good for a person who has little understanding of Neural Nets - and wants to keep it that way. The systems are aimed at low end controls applications in automotive, industrial, and appliance areas. NeuFuz is a neural-fuzzy technology which uses backpropagation techniques to initially select fuzzy rules and membership functions. Initial stages of design using NeuFuz technology are performed using training data and backpropagation. The result is a fuzzy associative memory (FAM) which implements an approximation of the training data. By implementing a FAM, rather than a multi-layer perceptron, the designer has a solution which can be understood and tuned to a particular application using Fuzzy Logic design techniques. There are several different versions, some with COP8 Code Generator (COP8 is National's family of 8-bit microcontrollers) and COP8 in-circuit emulator (debug module). 13. Cortex-Pro ++++++++++++++ Cortex-Pro information is on WWW at: http://www.neuronet.ph.kcl.ac.uk/neuronet/software/cortex/www1.html. You can download a working demo from there. Contact: Michael Reiss ( http://www.mth.kcl.ac.uk/~mreiss/mick.html) email: <m.reiss@kcl.ac.uk>. 14. PARTEK ++++++++++ PARTEK is a powerful, integrated environment for visual and quantitative data analysis and pattern recognition. Drawing from a wide variety of disciplines including Artificial Neural Networks, Fuzzy Logic, Genetic Algorithms, and Statistics, PARTEK integrates data analysis and modeling tools into an easy to use "point and click" system. The following modules are available from PARTEK; functions from different modules are integrated with each other whereever possible: 1. The PARTEK/AVB - The Analytical/Visual Base. (TM) * Analytical Spreadsheet (TM) The Analytical Spreadsheet is a powerful and easy to use data analysis, transformations, and visualization tool. Some features include: - import native format ascii/binary data - recognition and resolution of missing data - complete set of common mathematical & statistical functions - contingency table analysis / correspondence analysis - univariate histogram analysis - extensive set of smoothing and normalization transformations - easily and quickly plot color-coded 1-D curves and histograms, 2-D, 3-D, and N-D mapped scatterplots, highlighting selected patterns - Command Line (Tcl) and Graphical Interface * Pattern Visualization System (TM) The Pattern Visualization System offers the most powerful tools for visual analysis of the patterns in your data. Some features include: - automatically maps N-D data down to 3-D for visualization of *all* of your variables at once - hard copy color Postscript output - a variety of color-coding, highlighting, and labeling options allow you to generate meaningful graphics * Data Filters Filter out selected rows and/or columns of your data for flexible and efficient cross-validation, jackknifing, bootstrapping, feature set evaluation, and more. * Random # Generators Generate random numbers from any of the following parameterized distributions: - uniform, normal, exponential, gamma, binomial, poisson * Many distance/similarity metrics Choose the appropriate distance metric for your data: - euclidean, mahalanobis, minkowski, maximum value, absolute value, shape coefficient, cosine coefficient, pearson correlation, rank correlation, kendall's tau, canberra, and bray-curtis * Tcl/Tk command line interface 2. The PARTEK/DSA - Data Structure Analysis Module * Principal Components Analysis and Regression Also known as Eigenvector Projection or Karhunen-Loeve Expansions, PCA removes redundant information from your data. - component analysis, correlate PC's with original variables - choice of covariance, correlation, or product dispersion matrices - choice of eigenvector, y-score, and z-score projections - view SCREE and log-eigenvalue plots * Cluster Analysis Does the data form groups? How many? How compact? Cluster Analysis is the tool to answer these questions. - choose between several distance metrics - optionally weight individual patterns - manually or auto-select the cluster number and initial centers - dump cluster counts, mean, cluster to cluster distances, cluster variances, and cluster labeled data to a matrix viewer or the Analytical Spreadsheet for further analysis - visualize n-dimensional clustering - assess goodness of partion using several internal and external criteria metrics * N-Dimensional Histogram Analysis Among the most inportant questions a researcher needs to know when analyzing patterns is whether or not the patterns can distinguish different classes of data. N-D Histogram Analysis is one tool to answer this question. - measures histogram overlap in n-dimensional space - automatically find the best subset of features - rank the overlap of your best feature combinations * Non-Linear Mapping NLM is an iterative algorithm for visually analyzing the structure of n-dimensional data. NLM produces a non-linear mapping of data which preserves interpoint distances of n-dimensional data while reducing to a lower dimensionality - thus preserving the structure of the data. - visually analyze structure of n-dimensional data - track progress with error curves - orthogonal, PCA, and random initialization 3. The PARTEK/CP - Classification and Prediction Module. * Multi-Layer Perceptron The most popular among the neural pattern recognition tools is the MLP. PARTEK takes the MLP to a new dimension, by allowing the network to learn by adapting ALL of its parameters to solve a problem. - adapts output bias, neuron activation steepness, and neuron dynamic range, as well as weights and input biases - auto-scaling at input and output - no need to rescale your data - choose between sigmoid, gaussian, linear, or mixture of neurons - learning rate, momentum can be set independently for each parameter - variety of learning methods and network initializations - view color-coded network, error, etc as network trains, tests, runs * Learning Vector Quantization Because LVQ is a multiple prototype classifier, it adapts to identify multiple sub-groups within classes - LVQ1, LVQ2, and LVQ3 training methods - 3 different functions for adapting learning rate - choose between several distance metrics - fuzzy and crisp classifications - set number of prototypes individually for each class * Bayesian Classifier Bayes methods are the statistical decision theory approach to classification. This classifier uses statistical properties of your data to develop a classification model. PARTEK is available on HP, IBM, Silicon Graphics, and SUN workstations. For more information, send email to "info@partek.com" or call (314)926-2329. ------------------------------------------------------------------------ 19. A: Neural Network hardware? =============================== [who will write some short comment on the most important HW-packages and chips?] The Number 1 of each volume of the journal "Neural Networks" has a list of some dozens of suppliers of Neural Network support: Software, Hardware, Support, Programming, Design and Service. Here is a short list of companies: 1. HNC, INC. ++++++++++++ 5501 Oberlin Drive San Diego California 92121 (619) 546-8877 and a second address at 7799 Leesburg Pike, Suite 900 Falls Church, Virginia 22043 (703) 847-6808 Note: Australian Dist.: Unitronics Tel : (09) 4701443 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 (619) 546 6148 Fax: (619) 546 6736 3. Micro Devices ++++++++++++++++ 30 Skyline Drive Lake Mary FL 32746-6201 (407) 333-4379 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 (408) 765-9235 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 (916) 477-7481 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: +44 730 60256 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: (303) 442-3539 Fax: (303) 442-2854 Internet: techsupport@ndx.com NDX sells a number neural network hardware products: NDX Neural Accelerators: a line of i860-based accelerator cards for the PC that give up to 45 million connections per second for use with the DynaMind neural network software. iNNTS: Intel's 80170NX (ETANN) Neural Network Training System. NDX's president was one of the co-designers of this chip. 9. IC Tech ++++++++++ NEURO-COMPUTING IC's: * DANN050L (dendro-dendritic artificial neural network) + 50 neurons fully connected at the input + on-chip digital learning capability + 6 billion connections/sec peak speed + learns 7 x 7 template in < 50 nsec., recalls in < 400 nsec. + low power < 100 milli Watts + 64-pin package * NCA717D (neuro correlator array) + analog template matching in < 500 nsec. + analog input / digital output pins for real-time computation + vision applications in stereo and motion computation + 40-pin package NEURO COMPUTING BOARD: * ICT1050 + IBM PC compatible or higher + with on-board DANN050L + digital interface + custom configurations available Contact: IC Tech (Innovative Computing Technologies, Inc.) 4138 Luff Court Okemos, MI 48864 (517) 349-4544 ictech@mcimail.com And here is an incomplete overview over known Neural Computers with their newest known reference. \subsection*{Digital} \subsubsection{Special Computers} {\bf AAP-2} Takumi Watanabe, Yoshi Sugiyama, Toshio Kondo, and Yoshihiro Kitamura. Neural network simulation on a massively parallel cellular array processor: AAP-2. In International Joint Conference on Neural Networks, 1989. {\bf ANNA} B.E.Boser, E.Sackinger, J.Bromley, Y.leChun, and L.D.Jackel.\\ Hardware Requirements for Neural Network Pattern Classifiers.\\ In {\it IEEE Micro}, 12(1), pages 32-40, February 1992. {\bf Analog Neural Computer} Paul Mueller et al. Design and performance of a prototype analog neural computer. In Neurocomputing, 4(6):311-323, 1992. {\bf APx -- Array Processor Accelerator}\\ F.Pazienti.\\ Neural networks simulation with array processors. In {\it Advanced Computer Technology, Reliable Systems and Applications; Proceedings of the 5th Annual Computer Conference}, pages 547-551. IEEE Comput. Soc. Press, May 1991. ISBN: 0-8186-2141-9. {\bf ASP -- Associative String Processor}\\ A.Krikelis.\\ A novel massively associative processing architecture for the implementation artificial neural networks.\\ In {\it 1991 International Conference on Acoustics, Speech and Signal Processing}, volume 2, pages 1057-1060. IEEE Comput. Soc. Press, May 1991. {\bf BSP400} Jan N.H. Heemskerk, Jacob M.J. Murre, Jaap Hoekstra, Leon H.J.G. Kemna, and Patrick T.W. Hudson. The bsp400: A modular neurocomputer assembled from 400 low-cost microprocessors. In International Conference on Artificial Neural Networks. Elsevier Science, 1991. {\bf BLAST}\\ J.G.Elias, M.D.Fisher, and C.M.Monemi.\\ A multiprocessor machine for large-scale neural network simulation. In {\it IJCNN91-Seattle: International Joint Conference on Neural Networks}, volume 1, pages 469-474. IEEE Comput. Soc. Press, July 1991. ISBN: 0-7883-0164-1. {\bf CNAPS Neurocomputer}\\ H.McCartor\\ Back Propagation Implementation on the Adaptive Solutions CNAPS Neurocomputer.\\ In {\it Advances in Neural Information Processing Systems}, 3, 1991. {\bf GENES~IV and MANTRA~I}\\ Paolo Ienne and Marc A. Viredaz\\ {GENES~IV}: A Bit-Serial Processing Element for a Multi-Model Neural-Network Accelerator\\ Proceedings of the International Conference on Application Specific Array Processors, Venezia, 1993. {\bf MA16 -- Neural Signal Processor} U.Ramacher, J.Beichter, and N.Bruls.\\ Architecture of a general-purpose neural signal processor.\\ In {\it IJCNN91-Seattle: International Joint Conference on Neural Networks}, volume 1, pages 443-446. IEEE Comput. Soc. Press, July 1991. ISBN: 0-7083-0164-1. {\bf MANTRA I}\\ Marc A. Viredaz\\ {MANTRA~I}: An {SIMD} Processor Array for Neural Computation Proceedings of the Euro-ARCH'93 Conference, {M\"unchen}, 1993. {\bf Mindshape} Jan N.H. Heemskerk, Jacob M.J. Murre Arend Melissant, Mirko Pelgrom, and Patrick T.W. Hudson. Mindshape: a neurocomputer concept based on a fractal architecture. In International Conference on Artificial Neural Networks. Elsevier Science, 1992. {\bf mod 2} Michael L. Mumford, David K. Andes, and Lynn R. Kern. The mod 2 neurocomputer system design. In IEEE Transactions on Neural Networks, 3(3):423-433, 1992. {\bf NERV}\\ R.Hauser, H.Horner, R. Maenner, and M.Makhaniok.\\ Architectural Considerations for NERV - a General Purpose Neural Network Simulation System.\\ In {\it Workshop on Parallel Processing: Logic, Organization and Technology -- WOPPLOT 89}, pages 183-195. Springer Verlag, Mars 1989. ISBN: 3-5405-5027-5. {\bf NP -- Neural Processor}\\ D.A.Orrey, D.J.Myers, and J.M.Vincent.\\ A high performance digital processor for implementing large artificial neural networks.\\ In {\it Proceedings of of the IEEE 1991 Custom Integrated Circuits Conference}, pages 16.3/1-4. IEEE Comput. Soc. Press, May 1991. ISBN: 0-7883-0015-7. {\bf RAP -- Ring Array Processor }\\ N.Morgan, J.Beck, P.Kohn, J.Bilmes, E.Allman, and J.Beer.\\ The ring array processor: A multiprocessing peripheral for connectionist applications. \\ In {\it Journal of Parallel and Distributed Computing}, pages 248-259, April 1992. {\bf RENNS -- REconfigurable Neural Networks Server}\\ O.Landsverk, J.Greipsland, J.A.Mathisen, J.G.Solheim, and L.Utne.\\ RENNS - a Reconfigurable Computer System for Simulating Artificial Neural Network Algorithms.\\ In {\it Parallel and Distributed Computing Systems, Proceedings of the ISMM 5th International Conference}, pages 251-256. The International Society for Mini and Microcomputers - ISMM, October 1992. ISBN: 1-8808-4302-1. {\bf SMART -- Sparse Matrix Adaptive and Recursive Transforms}\\ P.Bessiere, A.Chams, A.Guerin, J.Herault, C.Jutten, and J.C.Lawson.\\ From Hardware to Software: Designing a Neurostation''.\\ In {\it VLSI design of Neural Networks}, pages 311-335, June 1990. {\bf SNAP -- Scalable Neurocomputer Array Processor} E.Wojciechowski.\\ SNAP: A parallel processor for implementing real time neural networks.\\ In {\it Proceedings of the IEEE 1991 National Aerospace and Electronics Conference; NAECON-91}, volume 2, pages 736-742. IEEE Comput.Soc.Press, May 1991. {\bf Toroidal Neural Network Processor}\\ S.Jones, K.Sammut, C.Nielsen, and J.Staunstrup.\\ Toroidal Neural Network: Architecture and Processor Granularity Issues.\\ In {\it VLSI design of Neural Networks}, pages 229-254, June 1990. {\bf SMART and SuperNode} P. Bessiere, A. Chams, and P. Chol. MENTAL : A virtual machine approach to artificial neural networks programming. In NERVES, ESPRIT B.R.A. project no 3049, 1991. \subsubsection{Standard Computers} {\bf EMMA-2}\\ R.Battiti, L.M.Briano, R.Cecinati, A.M.Colla, and P.Guido.\\ An application oriented development environment for Neural Net models on multiprocessor Emma-2.\\ In {\it Silicon Architectures for Neural Nets; Proceedings for the IFIP WG.10.5 Workshop}, pages 31-43. North Holland, November 1991. ISBN: 0-4448-9113-7. {\bf iPSC/860 Hypercube}\\ D.Jackson, and D.Hammerstrom\\ Distributing Back Propagation Networks Over the Intel iPSC/860 Hypercube}\\ In {\it IJCNN91-Seattle: International Joint Conference on Neural Networks}, volume 1, pages 569-574. IEEE Comput. Soc. Press, July 1991. ISBN: 0-7083-0164-1. {\bf SCAP -- Systolic/Cellular Array Processor}\\ Wei-Ling L., V.K.Prasanna, and K.W.Przytula.\\ Algorithmic Mapping of Neural Network Models onto Parallel SIMD Machines.\\ In {\it IEEE Transactions on Computers}, 40(12), pages 1390-1401, December 1991. ISSN: 0018-9340. ------------------------------------------------------------------------ 20. A: Databases for experimentation with NNs? ============================================== 1. The neural-bench Benchmark collection ++++++++++++++++++++++++++++++++++++++++ Accessible via anonymous FTP on ftp.cs.cmu.edu [128.2.206.173] in directory /afs/cs/project/connect/bench. In case of problems or if you want to donate data, email contact is "neural-bench@cs.cmu.edu". The data sets in this repository include the 'nettalk' data, 'two spirals', protein structure prediction, vowel recognition, sonar signal classification, and a few others. 2. Proben1 ++++++++++ Proben1 is a collection of 12 learning problems consisting of real data. The datafiles all share a single simple common format. Along with the data comes a technical report describing a set of rules and conventions for performing and reporting benchmark tests and their results. Accessible via anonymous FTP on ftp.cs.cmu.edu [128.2.206.173] as /afs/cs/project/connect/bench/contrib/prechelt/proben1.tar.gz. and also on ftp.ira.uka.de [129.13.10.90] as /pub/neuron/proben.tar.gz. The file is about 1.8 MB and unpacks into about 20 MB. 3. UCI machine learning database ++++++++++++++++++++++++++++++++ Accessible via anonymous FTP on ics.uci.edu [128.195.1.1] in directory /pub/machine-learning-databases". 4. NIST special databases of the National Institute Of Standards And ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ Technology: +++++++++++ Several large databases, each delivered on a CD-ROM. Here is a quick list. o NIST Binary Images of Printed Digits, Alphas, and Text o NIST Structured Forms Reference Set of Binary Images o NIST Binary Images of Handwritten Segmented Characters o NIST 8-bit Gray Scale Images of Fingerprint Image Groups o NIST Structured Forms Reference Set 2 of Binary Images o NIST Test Data 1: Binary Images of Hand-Printed Segmented Characters o NIST Machine-Print Database of Gray Scale and Binary Images o NIST 8-Bit Gray Scale Images of Mated Fingerprint Card Pairs o NIST Supplemental Fingerprint Card Data (SFCD) for NIST Special Database 9 o NIST Binary Image Databases of Census Miniforms (MFDB) o NIST Mated Fingerprint Card Pairs 2 (MFCP 2) o NIST Scoring Package Release 1.0 o NIST FORM-BASED HANDPRINT RECOGNITION SYSTEM Here are example descriptions of two of these databases: NIST special database 2: Structured Forms Reference Set (SFRS) -------------------------------------------------------------- The NIST database of structured forms contains 5,590 full page images of simulated tax forms completed using machine print. THERE IS NO REAL TAX DATA IN THIS DATABASE. The structured forms used in this database are 12 different forms from the 1988, IRS 1040 Package X. These include Forms 1040, 2106, 2441, 4562, and 6251 together with Schedules A, B, C, D, E, F and SE. Eight of these forms contain two pages or form faces making a total of 20 form faces represented in the database. Each image is stored in bi-level black and white raster format. The images in this database appear to be real forms prepared by individuals but the images have been automatically derived and synthesized using a computer and contain no "real" tax data. The entry field values on the forms have been automatically generated by a computer in order to make the data available without the danger of distributing privileged tax information. In addition to the images the database includes 5,590 answer files, one for each image. Each answer file contains an ASCII representation of the data found in the entry fields on the corresponding image. Image format documentation and example software are also provided. The uncompressed database totals approximately 5.9 gigabytes of data. NIST special database 3: Binary Images of Handwritten Segmented --------------------------------------------------------------- Characters (HWSC) ----------------- Contains 313,389 isolated character images segmented from the 2,100 full-page images distributed with "NIST Special Database 1". 223,125 digits, 44,951 upper-case, and 45,313 lower-case character images. Each character image has been centered in a separate 128 by 128 pixel region, error rate of the segmentation and assigned classification is less than 0.1%. The uncompressed database totals approximately 2.75 gigabytes of image data and includes image format documentation and example software. The system requirements for all databases are a 5.25" CD-ROM drive with software to read ISO-9660 format. Contact: Darrin L. Dimmick; dld@magi.ncsl.nist.gov; (301)975-4147 The prices of the databases are between US$ 250 and 1895 If you wish
Institute of Standards and Technology; 221/A323; Gaithersburg, MD
20899; Phone: (301)975-2208; FAX: (301)926-0416

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

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

The Center Of Excellence for Document Analysis and Recognition
(CEDAR) State University of New York at Buffalo announces the
availability of CEDAR CDROM 1: USPS Office of Advanced
Technology The database contains handwritten words and ZIP Codes in
high resolution grayscale (300 ppi 8-bit) as well as binary handwritten
digits and alphabetic characters (300 ppi 1-bit). This database is
intended to encourage research in off-line handwriting recognition by
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
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
CEDAR, 226 Bell Hall State University of New York at Buffalo,
Buffalo, NY 14260; hull@cs.buffalo.edu (email)

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

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

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

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

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

That's all folks.

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

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

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

o Gamze Erten <ictech@mcimail.com>
o Steve Ward <71561.2370@CompuServe.COM>
o Allen Bonde <ab04@harvey.gte.com>
o Accel Infotech Spore Pte Ltd <accel@solomon.technet.sg>
o Alexander Linden <al@jargon.gmd.de>
o S.Taimi Ames <ames@reed.edu>
o Axel Mulder <amulder@move.kines.sfu.ca>
o anderson@atc.boeing.com
o Andy Gillanders <andy@grace.demon.co.uk>
o Davide Anguita <anguita@ICSI.Berkeley.EDU>
o Kim L. Blackwell <avrama@helix.nih.gov>
o Paul Bakker <bakker@cs.uq.oz.au>
o Stefan Bergdoll <bergdoll@zxd.basf-ag.de>
o Jamshed Bharucha <bharucha@casbs.Stanford.EDU>
o Yijun Cai <caiy@mercury.cs.uregina.ca>
o L. Leon Campbell <campbell@brahms.udel.edu>
o Craig Watson <craig@magi.ncsl.nist.gov>
o Yaron Danon <danony@goya.its.rpi.edu>
o David Ewing <dave@ndx.com>
o David DeMers <demers@cs.ucsd.edu>
o Denni Rognvaldsson <denni@thep.lu.se>
o Duane Highley <dhighley@ozarks.sgcl.lib.mo.us>
o Dick.Keene@Central.Sun.COM
o DJ Meyer <djm@partek.com>
o Donald Tveter <drt@mcs.com>
o Athanasios Episcopos <EPISCOPO@icarus.som.clarkson.edu>
o Frank Schnorrenberg <fs0997@easttexas.tamu.edu>
o Gary Lawrence Murphy <garym@maya.isis.org>
o gaudiano@park.bu.edu
o Lee Giles <giles@research.nj.nec.com>
o Glen Clark <opto!glen@gatech.edu>
o Phil Goodman <goodman@unr.edu>
o guy@minster.york.ac.uk
o Joerg Heitkoetter <heitkoet@lusty.informatik.uni-dortmund.de>
o Ralf Hohenstein <hohenst@math.uni-muenster.de>
o Ed Rosenfeld <IER@aol.com>
o Jean-Denis Muller <jdmuller@vnet.ibm.com>
o Jeff Harpster <uu0979!jeff@uu9.psi.com>
o Jonathan Kamens <jik@MIT.Edu>
o J.J. Merelo <jmerelo@kal-el.ugr.es>
o Jon Gunnar Solheim <jon@kongle.idt.unit.no>
o Josef Nelissen <jonas@beor.informatik.rwth-aachen.de>
o Joey Rogers <jrogers@buster.eng.ua.edu>
o Subhash Kak <kak@gate.ee.lsu.edu>
o Ken Karnofsky <karnofsky@mathworks.com>
o Kjetil.Noervaag@idt.unit.no
o Luke Koops <koops@gaul.csd.uwo.ca>
o William Mackeown <mackeown@compsci.bristol.ac.uk>
o Mark Plumbley <mark@dcs.kcl.ac.uk>
o Peter Marvit <marvit@cattell.psych.upenn.edu>
o masud@worldbank.org
o Yoshiro Miyata <miyata@sccs.chukyo-u.ac.jp>
o Jyrki Alakuijala <more@ee.oulu.fi>
o Michael Reiss <m.reiss@kcl.ac.uk>
o mrs@kithrup.com
o Maciek Sitnik <msitnik@plearn.edu.pl>
o R. Steven Rainwater <ncc@ncc.jvnc.net>
o Paolo Ienne <Paolo.Ienne@di.epfl.ch>
o Paul Keller <pe_keller@ccmail.pnl.gov>
o Michael Plonski <plonski@aero.org>
o Lutz Prechelt <prechelt@ira.uka.de> [creator of FAQ]
o Richard Andrew Miles Outerbridge <ramo@uvphys.phys.uvic.ca>
o Robin L. Getz <rgetz@esd.nsc.com>
o Richard Cornelius <richc@rsf.atd.ucar.edu>
o Rob Cunningham <rkc@xn.ll.mit.edu>
o Robert.Kocjancic@IJS.si
o Osamu Saito <saito@nttica.ntt.jp>
o Warren Sarle <saswss@unx.sas.com>
o Scott Fahlman <sef+@cs.cmu.edu>
o <seibert@ll.mit.edu>
o Sheryl Cormicle <sherylc@umich.edu>
o Ted Stockwell <ted@aps1.spa.umn.edu>
o Serge Waterschoot <swater@minf.vub.ac.be>
o Thomas G. Dietterich <tgd@research.cs.orst.edu>
o Thomas.Vogel@cl.cam.ac.uk
o Ulrich Wendl <uli@unido.informatik.uni-dortmund.de>
o M. Verleysen <verleysen@dice.ucl.ac.be>
o Sherif Hashem <vg197@neutrino.pnl.gov>
o Matthew P Wiener <weemba@sagi.wistar.upenn.edu>
o Wesley Elsberry <welsberr@orca.tamu.edu>

Bye

Lutz

Neural network FAQ / Lutz Prechelt, prechelt@ira.uka.de
--
Lutz Prechelt   (http://wwwipd.ira.uka.de/~prechelt/)   | 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.

`