Archive-name: neural-net-faq
Last-modified: 1995/02/23
Maintainer: (Lutz Prechelt)

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

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

This is a monthly posting to the Usenet newsgroup (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. 


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To find the answer of question number 'x', search for the string
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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? 
 9. What about Genetic Algorithms? 
 10. What about Fuzzy Logic? 
 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? 

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

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

   The newsgroup 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

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

    2. Questions

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

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

    3. Answers

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

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

    4. Summaries

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

      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
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      Some care should be invested into a summary: 
       o simple concatenation of all the answers is not enough: instead,
         redundancies, irrelevancies, verbosities, and errors should be
         filtered out (as good as possible) 
       o the answers should be separated clearly 
       o the contributors of the individual answers should be identifiable
         (unless they requested to remain anonymous [yes, that happens])
       o the summary should start with the "quintessence" of the
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       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, has
      not had many problems with this effect in the past, so let's just go and


 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

   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


 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
    o Neuro-physiologists use neural networks to describe and explore
      medium-level brain function (e.g. memory, sensory system, motorics). 
    o Physicists use neural networks to model phenomena in statistical
      mechanics and for a lot of other tasks. 
    o Biologists use Neural Networks to interpret nucleotide sequences. 
    o Philosophers and some other people may also be interested in Neural
      Networks for various reasons. 


 5. A: What does 'backprop' mean? 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

   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

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

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

   '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/ on (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


 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


 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:
           a). Additive Grossberg (AG)
           b). Shunting Grossberg (SG)
           c). Binary Adaptive Resonance Theory (ART1)
           d). Analog Adaptive Resonance Theory (ART2, ART2a)
           e). Discrete Hopfield (DH)
           f). Continuous Hopfield (CH)
           g). Discrete Bidirectional Associative Memory (BAM)
           h). Temporal Associative Memory (TAM)
           i). Adaptive Bidirectional Associative Memory (ABAM)
           j). Kohonen Self-organizing Map/Topology-preserving map (SOM/TPM)
           k). Competitive learning
        2). Feedforward-only Nets:
           a). Learning Matrix (LM)
           b). Driver-Reinforcement Learning (DR)
           c). Linear Associative Memory (LAM)
           d). Optimal Linear Associative Memory (OLAM)
           e). Sparse Distributed Associative Memory (SDM)
           f). Fuzzy Associative Memory (FAM)
           g). Counterprogation (CPN)

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


 9. A: What about Genetic Algorithms?

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

     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 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 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

   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, but
   there is also some work (and discussion) about combining fuzzy logic with
   Neural Network approaches in

   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 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
    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

   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,
   Addison-Wesley, ISBN: 0-201-56629-X. Helps the reader make his own
   NNs. The mathematica code for the programs in the book is also available
   through the internet: Send mail to or try on the World Wide Web.

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

   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,

   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 "")

   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

   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: 
            WWW server available: 

   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
   Remark:  Commericial Newsletter

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

   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:

   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 "")

   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 
            "" 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

   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:, 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:

   Remark:  (Must rank alongside Wolfram's Complex Systems)

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

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

   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)

   Remark:  (no comment!)

   Remark:  (several good book reviews)


 14. A: The most important conferences concerned with Neural

   [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: 
    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 <> 
    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 from Paultje Bakker:
      "Upcoming Neural Network Conferences", which lists names, dates,
      locations, contacts, and deadlines. It is also available for anonymous ftp
      from 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 : 

    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
      readers also find the messages in that newsgroup in the form of digests.

    2. Usenet groups (Oha!) and

      There is a periodic posting on sent by (Gregory Aharonian) about Neural Network

    3. Central Neural System Electronic Bulletin Board

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

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

    5. USENET newsgroup

      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 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): (NEuroNet, King's College, London), (Eindhoven, Netherlands),
      (Richland, Washington), (Salzburg,
      (Michigan). Reactive Memory Search (Tabu
      Search) page (Trento, Italy). Many others are available too, changing

    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 


 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 [] 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), 


      ftp []; Login name: sfinxftp; Password:
      joshua; directory: pub; files : README; sfinx_v2.0.tar.Z; Email info
      request : 

    3. NeurDS

      simulator for DEC systems supporting VT100 terminal. available for
      anonymous ftp from [] 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 Available for anonymous ftp
      from as /pub/neuron/PlaNet5.7.tar.Z (800 kb) or from [] as 

    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 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: Further
      information via WWW at 

    6. Mactivation

      A neural network simulator for the Apple Macintosh. Available for ftp
      from [] as 

    7. Cascade Correlation Simulator

      A simulator for Scott Fahlman's Cascade Correlation algorithm.
      Available for ftp from [] 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 [] as /pub/mac/dartnet.sit.hqx (124

    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 [] in directory
      /pub/SNNS as SNNSv3.2.tar.Z (2 MB, Source code) and (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
      [] in /afs/cs/project/connect/code/am6.tar.Z and from
      UCLA's cognitive science machine []
      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 [] in
      directory /pub/atree. See the files README (7 KB), atree2.tar.Z (145
      kb, Unix source code and examples), (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
      [] 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 [] 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 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

    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
      is available via anonymous ftp from []
      in directory /pub/xerion as (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 [] 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 [] after signing
      and sending a licence: /pub/ (67 kb). Contact: Marwan Jabri,
      SEDAL, Sydney University Electrical Engineering, NSW 2006

    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 []
      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
       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
       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 in the directories /gmd/as/sesame and /gmd/as/paper.
      Questions to; 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
      (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 [] 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 (, University of
      Nevada Center for Biomedical Research. 

    21. Fuzzy ARTmap

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


      This is a prototype that stems from an ESPRIT project. It implements
      back-propagation, self organising map, and Hopfield nets. Avaliable
      for ftp from [] as 
      /pub/sci/neural/sims/pygmalion.tar.Z (1534 kb). (Original site is 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
       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 in directory /mcsnet.users/drt. Or
      see the WWW page The
      expanded professional version is $30/copy for ordinary individuals
      including academics and $200/copy for businesses and government
      agencies (improved user interface, more activation functions, networks
      can be read into your own programs, dynamic node creation, weight
      decay, SuperSAB). More details can be found in the documentation for
      the student version. Contact: Don Tveter; 5228 N. Nashville Ave.;
      Chicago, Illinois 60656; 

    24. Matrix Backpropagation

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

    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 as 
      /pub/pc/win3/programr/ (545 kB). 

    26. BIOSIM

      BIOSIM is a biologically oriented neural network simulator. Public
      domain, runs on Unix (less powerful PC-version is available, too), easy
      to install, bilingual (german and english), has a GUI (Graphical User
      Interface), designed for research and teaching, provides online help
      facilities, offers controlling interfaces, batch version is available, a
      DEMO is provided. REQUIREMENTS (Unix version): X11 Rel. 3 and
      above, Motif Rel 1.0 and above, 12 MB of physical memory,
      recommended are 24 MB and more, 20 MB disc space.
      REQUIREMENTS (PC version): PC-compatible with MS Windows
      3.0 and above, 4 MB of physical memory, recommended are 8 MB and
      more, 1 MB disc space. Four neuron models are implemented in
      BIOSIM: a simple model only switching ion channels on and off, the
      original Hodgkin-Huxley model, the SWIM model (a modified HH
      model) and the Golowasch-Buchholz model. Dendrites consist of a
      chain of segments without bifurcation. A neural network can be created
      by using the interactive network editor which is part of BIOSIM.
      Parameters can be changed via context sensitive menus and the results
      of the simulation can be visualized in observation windows for neurons
      and synapses. Stochastic processes such as noise can be included. In
      addition, biologically orientied learning and forgetting processes are
      modeled, e.g. sensitization, habituation, conditioning, hebbian learning
      and competitive learning. Three synaptic types are predefined (an
      excitatatory synapse type, an inhibitory synapse type and an electrical
      synapse). Additional synaptic types can be created interactively as
      desired. Available for ftp from in directory
      /pub/bio/neurobio: Get /pub/bio/neurobio/biosim.readme (2 kb) and 
      /pub/bio/neurobio/biosim.tar.Z (2.6 MB) for the Unix version or 
      /pub/bio/neurobio/biosimpc.readme (2 kb) and 
      /pub/bio/neurobio/ (150 kb) for the PC version. Contact:
      Stefan Bergdoll; Department of Software Engineering (ZXA/US);
      BASF Inc.; D-67056 Ludwigshafen; Germany;; 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 as /pub/msdos/science/ (78 kb) and from as /pub/contrib/dos/ (78 kb) Contact:
      David Perkovic; DP Computing; PO Box 712; Noarlunga Center SA
      5168; Australia; Email: (preferred) or or 

    28. FuNeGen 1.0

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

    29. NeuDL -- Neural-Network Description Language

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

    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 ( as 
      /SimTel/msdos/neurlnet/ (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; 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 [] 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
        Address: Klaus Hansens vei 31B
                 5037 Solheimsviken
          Phone:   +47-55544163 / +47-55201548
        Basic capabilities:
         Neural network development tool. nn is a language for specification of
         neural network simulators. Produces C-code and executables for the
         specified models, therefore ideal for application development. xnn is
         a graphical front-end to nn and the simulation code produced by nn.
         Gives graphical representations in a number of formats of any
         variables during simulation run-time. Comes with a number of
         pre-implemented models, including: Backprop (several variants), Self
         Organizing Maps, LVQ1, LVQ2, Radial Basis Function Networks,
         Generalized Regression Neural Networks, Jordan nets, Elman nets,
         Hopfield, etc.
        Operating system: nn: UNIX or MS-DOS, xnn: UNIX/X-windows
        System requirements: 10 Mb HD, 2 Mb RAM
        Approx. price: USD 2000,-

    2. BrainMaker

              Name: BrainMaker, BrainMaker Pro
           Company: California Scientific Software
           Address: 10024 Newtown rd, Nevada City, CA, 95959 USA
         Phone,Fax: 916 478 9040, 916 478 9041
             Email:  calsci! (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 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.
             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: (Neural net inquiries only)

       Basic capabilities:
         Feedforward nets with numerous training methods
         and loss functions, plus statistical analogs of
         counterpropagation and various unsupervised
       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
         Tel.   (49) +241-26041
         Fax.   (49) +241-44983

       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:

      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
        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:
        pkzip 2.04g archive file
              gatordem.txt    readme text file

    7. NeuroForecaster

         Name:    NeuroForecaster(TM)/Genetica 3.1
         Contact: Accel Infotech (S) Pte Ltd; 648 Geylang Road;
                  Republic of Singapore 1438; Phone: +65-7446863; Fax: +65-7492467
         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$1199 per single user
         license.  Please email us ( for order form. 
           More information about NeuroForecaster(TM)/Genetical may be found in 
           NeuroForecaster is a user-friendly neural network program specifically
         designed for building sophisticated and powerful forecasting and
         decision-support systems (Time-Series Forecasting, Cross-Sectional
         Classification, Indicator Analysis)
         * GENETICA Net Builder Option for automatic network optimization
         * 12 Neuro-Fuzzy Network Models      
         * Multitasking & Background Training Mode            
         * Unlimited Network Capacity         
         * Rescaled Range Analysis & Hurst Exponent to Unveil Hidden Market
           Cycles & Check for Predictability  
         * Correlation Analysis to Compute Correlation Factors to Analyze the 
           Significance of Indicators                 
         * Weight Histogram to Monitor the Progress of Learning
         * Accumulated Error Analysis to Analyze the Strength of Input Indicators     
         Its user-friendly interface allows the users to build applications quickly, 
         easily and interactively, analyze the data visually and see the results 
         The following example applications are included in the package:
         * Credit Rating - for generating the credit rating of bank loan
         * Stock market 6 monthly returns forecast
         * Stock selection based on company ratios
         * US$ 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.:

      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 
      /pub/incoming/nutank.readme on the host 

    11. Neuralyst

      Name: Neuralyst Version 1.4; Company: Cheshire Engineering
      Corporation; Address: 650 Sierra Madre Villa, Suite 201, Pasedena CA
      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. 

    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: (Neural net
      inquiries only) URL: 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:
      You can download a working demo from there. Contact: Michael Reiss
      ( email:

    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
       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
                   - 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 
                   - 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 ""
      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
         (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
         PA 15276
         They only sell software/simulator but for many platforms.

    6. Tubb Research Limited

         7a Lavant Street
         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
         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

      *  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 
      *  ICT1050 
         + IBM PC compatible or higher
         + with on-board DANN050L
         + digital interface
         + custom configurations available
      IC Tech (Innovative Computing Technologies, Inc.)
      4138 Luff Court
      Okemos, MI 48864
      (517) 349-4544

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

   \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}\\
   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 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
   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}\\
   Back Propagation Implementation on the Adaptive Solutions CNAPS
   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}
   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
   In {\it VLSI design of Neural Networks}, pages 229-254, June 1990.

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

   \subsubsection{Standard Computers}

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

   {\bf iPSC/860 Hypercube}\\
   D.Jackson, and D.Hammerstrom\\
   Distributing Back Propagation Networks Over the Intel iPSC/860
   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
   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 [] in
      directory /afs/cs/project/connect/bench. In case of problems or if you
      want to donate data, email contact is "". 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 [] as 
      /afs/cs/project/connect/bench/contrib/prechelt/proben1.tar.gz. and also
      on [] 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 [] in directory

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

      Several large databases, each delivered on a CD-ROM. Here is a quick
       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
       o NIST Machine-Print Database of Gray Scale and Binary
       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 
      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;; (301)975-4147

      The prices of the databases are between US$ 250 and 1895 If you wish
      to order a database, please contact: Standard Reference Data; National
      Institute of Standards and Technology; 221/A323; Gaithersburg, MD
      20899; Phone: (301)975-2208; FAX: (301)926-0416

      Samples of the data can be found by ftp on in
      directory /pub/data A more complete description of the available
      databases can be obtained from the same host as 

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

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

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

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

   (in alphabetical order of email adresses, I hope)

    o Gamze Erten <> 
    o Steve Ward <71561.2370@CompuServe.COM> 
    o Mohammad Bahrami <> 
    o Allen Bonde <> 
    o Accel Infotech Spore Pte Ltd <> 
    o Alexander Linden <> 
    o S.Taimi Ames <> 
    o Axel Mulder <> 
    o Andy Gillanders <> 
    o Davide Anguita <anguita@ICSI.Berkeley.EDU> 
    o Avraam Pouliakis <> 
    o Kim L. Blackwell <> 
    o Paul Bakker <> 
    o Stefan Bergdoll <> 
    o Jamshed Bharucha <bharucha@casbs.Stanford.EDU> 
    o Yijun Cai <> 
    o L. Leon Campbell <> 
    o Craig Watson <> 
    o Yaron Danon <> 
    o David Ewing <> 
    o David DeMers <> 
    o Denni Rognvaldsson <> 
    o Duane Highley <> 
    o Dick.Keene@Central.Sun.COM 
    o DJ Meyer <> 
    o Donald Tveter <> 
    o Athanasios Episcopos <> 
    o Frank Schnorrenberg <> 
    o Gary Lawrence Murphy <> 
    o Lee Giles <> 
    o Glen Clark <opto!> 
    o Phil Goodman <> 
    o Joerg Heitkoetter <> 
    o Ralf Hohenstein <> 
    o Ed Rosenfeld <> 
    o Jean-Denis Muller <> 
    o Jeff Harpster <uu0979!> 
    o Jonathan Kamens <jik@MIT.Edu> 
    o J.J. Merelo <> 
    o Jon Gunnar Solheim <> 
    o Josef Nelissen <> 
    o Joey Rogers <> 
    o Subhash Kak <> 
    o Ken Karnofsky <> 
    o Luke Koops <> 
    o William Mackeown <> 
    o Mark Plumbley <> 
    o Peter Marvit <> 
    o Yoshiro Miyata <> 
    o Madhav Moganti <> 
    o Jyrki Alakuijala <> 
    o Michael Reiss <> 
    o Maciek Sitnik <> 
    o R. Steven Rainwater <> 
    o Paolo Ienne <> 
    o Paul Keller <> 
    o Michael Plonski <> 
    o Lutz Prechelt <> [creator of FAQ] 
    o Richard Andrew Miles Outerbridge <> 
    o Robin L. Getz <> 
    o Richard Cornelius <> 
    o Rob Cunningham <> 
    o Osamu Saito <> 
    o Warren Sarle <> 
    o Scott Fahlman <> 
    o <> 
    o Sheryl Cormicle <> 
    o Ted Stockwell <> 
    o Serge Waterschoot <> 
    o Thomas G. Dietterich <> 
    o Ulrich Wendl <> 
    o M. Verleysen <> 
    o Sherif Hashem <> 
    o Matthew P Wiener <> 
    o Wesley Elsberry <> 



   Neural network FAQ / Lutz Prechelt,
Lutz 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.