Path: svin09.info.win.tue.nl!tuegate.tue.nl!sun4nl!mcsun!Germany.EU.net!ira.uka.de!uka!prechelt From: prechelt@ira.uka.de (Lutz Prechelt) Newsgroups: comp.ai.neural-nets,comp.answers,news.answers Subject: FAQ in comp.ai.neural-nets -- monthly posting Keywords: questions, answers, terminology, bibliography Message-ID: Date: 28 Feb 93 03:17:08 GMT Expires: 4 Apr 1993 03:18:08 GMT Reply-To: prechelt@ira.uka.de (Lutz Prechelt) Followup-To: comp.ai.neural-nets Organization: University of Karlsruhe, Germany Lines: 1788 Approved: news-answers-request@MIT.Edu Supersedes: NNTP-Posting-Host: i41s18.ira.uka.de Originator: prechelt@i41s18 Xref: svin09.info.win.tue.nl comp.ai.neural-nets:8286 comp.answers:158 news.answers:5920 Archive-name: neural-net-faq Last-modified: 93/02/19 (FAQ means "Frequently Asked Questions") ------------------------------------------------------------------------ Anybody who is willing to contribute any question or information, please email me; if it is relevant, I will incorporate it. But: Please format your contribution appropriately so that I can just drop it in. The monthly posting departs at the 28th of every month. ------------------------------------------------------------------------ This is a monthly posting to the Usenet newsgroup comp.ai.neural-nets (and news.answers, where it should be findable at ANY time) Its purpose is to provide basic information for individuals who are new to the field of neural networks or are just beginning to read this group. It shall help to avoid lengthy discussion of questions that usually arise for beginners of one or the other kind. >>>>> SO, PLEASE, SEARCH THIS POSTING FIRST IF YOU HAVE A QUESTION <<<<< and >>>>> DON'T POST ANSWERS TO FAQs: POINT THE ASKER TO THIS POSTING <<<<< This posting is archived in the periodic posting archive on "pit-manager.mit.edu" [18.172.1.27] (and on some other hosts as well). Look in the anonymous ftp directory "/pub/usenet/news.answers", the filename is as given in the 'Archive-name:' header above. If you do not have anonymous ftp access, you can access the archives by mail server as well. Send an E-mail message to mail-server@pit-manager.mit.edu with "help" and "index" in the body on separate lines for more information. The monthly posting is not meant to discuss any topic exhaustively. Disclaimer: This posting is provided 'as is'. No warranty whatsoever is expressed or implied, especially, no warranty that the information contained herein is correct or useful in any way, although both is intended. >> To find the answer of question number (if present at all), search >> for the string "-A.)" (so the answer to question 12 is at "-A12.)") And now, in the end, we begin: ============================== Questions ============================== (the short forms and non-continous numbering is intended) 1.) What is this newsgroup for ? How shall it be used ? 2.) What is a neural network (NN) ? 3.) What can you do with a Neural Network and what not ? 4.) Who is concerned with Neural Networks ? 6.) What does 'backprop' mean ? 7.) How many learning methods for NNs exist ? Which ? 8.) What about Genetic Algorithms ? 9.) What about Fuzzy Logic ? 10.) Good introductory literature about Neural Networks ? 11.) Any journals and magazines about Neural Networks ? 12.) The most important conferences concerned with Neural Networks ? 13.) Neural Network Associations ? 14.) Other sources of information about NNs ? 15.) Freely available software packages for NN simulation ? 16.) Commercial software packages for NN simulation ? 17.) Neural Network hardware ? 19.) Databases for experimentation with NNs ? ============================== Answers ============================== ------------------------------------------------------------------------ -A1.) What is this newsgroup for ? The newsgroup comp.ai.neural-nets is inteded as a forum for people who want to use or explore the capabilities of 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. 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 email, I'll summarize" at the end of the posting. The Subject line of the posting should then be something like "Request: X" 2. Questions As opposed to requests, questions are concerned with something so specific that general interest cannot readily be assumed. If the poster thinks that the topic is of some general interest, he/she should announce a summary (see above). 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. 4. Summaries In all cases of requests or questions the answers for which can be assumed to be of some general interest, the poster of the request or question shall summarize the ansers he/she received. Such a summary should be announced in the original posting of the question or request with a phrase like "Please answer by email, I'll summarize" In such a case answers should NOT be posted to the newsgroup but instead be mailed to the poster who collects and reviews them. After about 10 to 20 days from the original posting, its poster should make the summary of answers and post it to the net. Some care should be invested into a summary: a) simple concatenation of all the answers is not enough; instead redundancies, irrelevancies, verbosities and errors must be filtered out (as good as possible), b) the answers shall be separated clearly c) the contributors of the individual answers shall be identifiable (unless they requested to remain anonymous [yes, that happens]) d) the summary shall start with the "quintessence" of the answers, as seen by the original poster e) A summary should, when posted, clearly be indicated to be one by giving it a Subject line starting with "Summary:" Note that a good summary is pure gold for the rest of the newsgroup community, so summary work will be most appreciated by all of us. (Good summaries are more valuable than any moderator ! :-> ) 5. Announcements Some articles never need any public reaction. These are called announcements (for instance for a workshop, conference or the availability of some technical report or software system). Announcements should be clearly indicated to be such by giving them a subject line of the form "Announcement: this-and-that" 6. Reports Sometimes people spontaneously want to report something to the newsgroup. This might be special experiences with some software, results of own experiments or conceptual work, or especially interesting information from somewhere else. Reports should be clearly indicated to be such by giving them a subject line of the form "Report: this-and-that" 7. Discussions An especially valuable possibility of Usenet is of course that of discussing a certain topic with hundreds of potential participants. All traffic in the newsgroup that can not be subsumed under one of the above categories should belong to a discussion. If somebody explicitly wants to start a discussion, he/she can do so by giving the posting a subject line of the form "Start discussion: this-and-that" (People who react on this, please remove the "Start discussion: " label from the subject line of your replies) It is quite difficult to keep a discussion from drifting into chaos, but, unfortunately, as many many other newsgroups show there seems to be no secure way to avoid this. On the other hand, comp.ai.neural-nets has not had many problems with this effect in the past, so let's just go and hope... :-> ------------------------------------------------------------------------ -A2.) What is a neural network (NN) ? [anybody there to write something better? buzzwords: artificial vs. natural/biological; units and connections; value passing; inputs and outputs; storage in structure and weights; only local information; highly parallel operation ] First of all, when we are talking about a neural network, we *should* usually better say "artificial neural network" (ANN), because that is what we mean most of the time. Biological neural networks are much more complicated in their elementary structures than the mathematical models we use for ANNs. A vague description is as follows: An ANN is a network of many very simple processors ("units"), each possibly having a (small amount of) local memory. The units are connected by unidirectional communication channels ("connections"), which carry numeric (as opposed to symbolic) data. The units operate only on their local data and on the inputs they receive via the connections. The design motivation is what distinguishes neural networks from other mathematical techniques: A neural network is a processing device, either an algorithm, or actual hardware, whose design was motivated by the design and functioning of human brains and components thereof. Most neural networks have some sort of "training" rule whereby the weights of connections are adjusted on the basis of presented patterns. In other words, neural networks "learn" from examples, just like children learn to recognize dogs from examples of dogs, and exhibit some structural capability for generalization. Neural networks normally have great potential for parallelism, since the computations of the components are independent of each other. ------------------------------------------------------------------------ -A3.) What can you do with a Neural Network and what not ? [preliminary] In principle, NNs can compute any computable function, i.e. they can do everything a normal digital computer can do. Especially can anything that can be represented as a mapping between vector spaces be approximated to arbitrary precision by feedforward NNs (which is the most often used type). In practice, NNs are especially useful for mapping problems which are tolerant of a high error rate, have lots of example data available, but to which hard and fast rules can not easily be applied. NNs are especially bad for problems that are concerned with manipulation of symbols and for problems that need short-term memory. ------------------------------------------------------------------------ -A4.) Who is concerned with Neural Networks ? Neural Networks are interesting for quite a lot of very dissimilar people: - Computer scientists want to find out about the properties of non-symbolic information processing with neural nets and about learning systems in general. - Engineers of many kinds want to exploit the capabilities of neural networks on many areas (e.g. signal processing) to solve their application problems. - Cognitive scientists view neural networks as a possible apparatus to describe models of thinking and conscience (High-level brain function). - Neuro-physiologists use neural networks to describe and explore medium-level brain function (e.g. memory, sensory system, motorics). - Physicists use neural networks to model phenomena in statistical mechanics and for a lot of other tasks. - Biologists use Neural Networks to interpret nucleotide sequences. - Philosophers and some other people may also be interested in Neural Networks for various reasons. ------------------------------------------------------------------------ -A6.) What does 'backprop' mean ? [anybody to write something similarly short, but easier to understand for a beginner ? ] It is an abbreviation for 'backpropagation of error' which is the most widely used learning method for neural networks today. Although it has many disadvantages, which could be summarized in the sentence "You are almost not knowing what you are actually doing when using backpropagation" :-) it has pretty much success on practical applications and is relatively easy to apply. It is for the training of layered (i.e., nodes are grouped in layers) feedforward (i.e., the arcs joining nodes are unidirectional, and there are no cycles) nets. Back-propagation needs a teacher that knows the correct output for any input ("supervised learning") and uses gradient descent on the error (as provided by the teacher) to train the weights. The activation function is (usually) a sigmoidal (i.e., bounded above and below, but differentiable) function of a weighted sum of the nodes inputs. The use of a gradient descent algorithm to train its weights makes it slow to train; but being a feedforward algorithm, it is quite rapid during the recall phase. Literature: Rumelhart, D. E. and McClelland, J. L. (1986): Parallel Distributed Processing: Explorations in the Microstructure of Cognition (volume 1, pp 318-362). The MIT Press. (this is the classic one) or one of the dozens of other books or articles on backpropagation :-> ------------------------------------------------------------------------ -A7.) How many learning methods for NNs exist ? Which ? There are many many learning methods for NNs by now. Nobody can know exactly how many. New ones (at least variations of existing ones) are invented every week. Below is a collection of some of the most well known methods; not claiming to be complete. The main categorization of these methods is the distiction of supervised from unsupervised learning: - In supervised learning, there is a "teacher" who in the learning phase "tells" the net how well it performs ("reinforcement learning") or what the correct behavior would have been ("fully supervised learning"). - In unsupervised learning the net is autonomous: it just looks at the data it is presented with, finds out about some of the properties of the data set and learns to reflect these properties in its output. What exactly these properties are, that the network can learn to recognise, depends on the particular network model and learning method. Many of these learning methods are closely connected with a certain (class of) network topology. Now here is the list, just giving some names: 1. UNSUPERVISED LEARNING (i.e. without a "teacher"): 1). Feedback Nets: a). Additive Grossberg (AG) b). Shunting Grossberg (SG) c). Binary Adaptive Resonance Theory (ART1) d). Analog Adaptive Resonance Theory (ART2, ART2a) e). Discrete Hopfield (DH) f). Continuous Hopfield (CH) g). Discrete Bidirectional Associative Memory (BAM) h). Temporal Associative Memory (TAM) i). Adaptive Bidirectional Associative Memory (ABAM) j). Kohonen Self-organizing Map (SOM) k). Kohonen Topology-preserving Map (TPM) 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) 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) ------------------------------------------------------------------------ -A8.) What about Genetic Algorithms ? [preliminary] [Who will write a better introduction?] There are a number of definitions of GA (Genetic Algorithm). A possible one is A GA is an optimization program that starts with some encoded procedure, (Creation of Life :-> ) mutates it stochastically, (Get cancer or so :-> ) and uses a selection process (Darwinism) to prefer the mutants with high fitness and perhaps a recombination process (Make babies :-> ) to combine properties of (preferably) the succesful mutants. There is a newsgroup that is dedicated to Genetic Algorithms called comp.ai.genetic. Some GA discussion also tends to happen in comp.ai.neural-nets. Another loosely relevant group is comp.theory.self-org-sys. There is a GA mailing list which you can subscribe to by sending a request to GA-List-Request@AIC.NRL.NAVY.MIL You can also try anonymous ftp to ftp.aic.nrl.navy.mil in the /pub/galist directory. There are papers and some software. For more details see (for example): "Genetic Algorithms in Search Optimisation and Machine Learning" by David Goldberg (Addison-Wesley 1989, 0-201-15767-5) or "Handbook of Genetic Algorithms" edited by Lawrence Davis (Van Nostrand Reinhold 1991 0-442-00173-8) or "Classifier Systems and Genetic Algorithms" L.B. Booker, D.E. Goldberg and J.H. Holland, Techreport No. 8 (April 87), Cognitive Science and Machine Intelligence Laboratory, University of Michigan also reprinted in : Artificial Intelligence, Volume 40 (1989), pages 185-234 ------------------------------------------------------------------------ -A9.) What about Fuzzy Logic ? [preliminary] [Who will write an introduction?] Fuzzy Logic is an area of research based on the work of L.A. Zadeh. It is a departure from classical two-valued sets and logic, that uses "soft" linguistic (e.g. large, hot, tall) system variables and a continuous range of truth values in the interval [0,1], rather than strict binary (True or False) decisions and assignments. Fuzzy logic is used where a system is difficult to model, is controlled by a human operator or expert, or where ambiguity or vagueness is common. A typical fuzzy system consists of a rule base, membership functions, and an inference procedure. Most Fuzzy Logic discussion takes place in the newsgroup comp.ai.fuzzy, but there is also some work (and discussion) about combining fuzzy logic with Neural Network approaches in comp.ai.neural-nets. For more details see (for example): Klir, G.J. and Folger, T.A., Fuzzy Sets, Uncertainty, and Information, Prentice-Hall, Englewood Cliffs, N.J., 1988. Kosko, B., Neural Networks and Fuzzy Systems, Prentice Hall, Englewood Cliffs, NJ, 1992. ------------------------------------------------------------------------ -A10.) Good introductory literature about Neural Networks ? 0.) The best (subjectively, of course -- please don't flame me): Hecht-Nielsen, R. (1990). Neurocomputing. Addison Wesley. Comments: "A good book", "comprises a nice historical overview and a chapter about NN hardware. Well structured prose. Makes important concepts clear." Hertz, J., Krogh, A., and Palmer, R. (1991). Introduction to the Theory of Neural Computation. Addison-Wesley: Redwood City, California. ISBN 0-201-50395-6 (hardbound) and 0-201-51560-1 (paperbound) Comments: "My first impression is that this one is by far the best book on the topic. And it's below $30 for the paperback."; "Well written, theoretical (but not overwhelming)"; It provides a good balance of model development, computational algorithms, and applications. The mathematical derivations are especially well done"; "Nice mathematical analysis on the mechanism of different learning algorithms"; "It is NOT for mathematical beginner. If you don't have a good grasp of higher level math, this book can be really tough to get through." 1.) Books for the beginner: Aleksander, I. and Morton, H. (1990). An Introduction to Neural Computing. Chapman and Hall. (ISBN 0-412-37780-2). Comments: "This book seems to be intended for the first year of university education." Beale, R. and Jackson, T. (1990). Neural Computing, an Introduction. Adam Hilger, IOP Publishing Ltd : Bristol. (ISBN 0-85274-262-2). Comments: "It's clearly written. Lots of hints as to how to get the adaptive models covered to work (not always well explained in the original sources). Consistent mathematical terminology. Covers perceptrons, error-backpropagation, Kohonen self-org model, Hopfield type models, ART, and associative memories." Dayhoff, J. E. (1990). Neural Network Architectures: An Introduction. Van Nostrand Reinhold: New York. Comments: "Like Wasserman's book, Dayhoff's book is also very easy to understand". McClelland, J. L. and Rumelhart, D. E. (1988). Explorations in Parallel Distributed Processing: Computational Models of Cognition and Perception (software manual). The MIT Press. Comments: "Written in a tutorial style, and includes 2 diskettes of NN simulation programs that can be compiled on MS-DOS or Unix (and they do too !)"; "The programs are pretty reasonable as an introduction to some of the things that NNs can do."; "There are *two* editions of this book. One comes with disks for the IBM PC, the other comes with disks for the Macintosh". McCord Nelson, M. and Illingworth, W.T. (1990). A Practical Guide to Neural Nets. Addison-Wesley Publishing Company, Inc. (ISBN 0-201-52376-0). Comments: "No formulas at all( ==> no good)"; "It does not have much detailed model development (very few equations), but it does present many areas of application. It includes a chapter on current areas of research. A variety of commercial applications is discussed in chapter 1. It also includes a program diskette with a fancy graphical interface (unlike the PDP diskette)". Orchard, G.A. & Phillips, W.A. (1991). Neural Computation: A Beginner's Guide. Lawrence Earlbaum Associates: London. Comments: "Short user-friendly introduction to the area, with a non-technical flavour. Apparently accompanies a software package, but I haven't seen that yet". Wasserman, P. D. (1989). Neural Computing: Theory & Practice. Van Nostrand Reinhold: New York. (ISBN 0-442-20743-3) Comments: "Wasserman flatly enumerates some common architectures from an engineer's perspective ('how it works') without ever addressing the underlying fundamentals ('why it works') - important basic concepts such as clustering, principal components or gradient descent are not treated. It's also full of errors, and unhelpful diagrams drawn with what appears to be PCB board layout software from the '70s. For anyone who wants to do active research in the field I consider it quite inadequate"; "Okay, but too shallow"; "Quite easy to understand"; "The best bedtime reading for Neural Networks. I have given this book to numerous collegues who want to know NN basics, but who never plan to implement anything. An excellent book to give your manager." 2.) The classics: Kohonen, T. (1984). Self-organization and Associative Memory. Springer-Verlag: New York. (2nd Edition: 1988; 3rd edition: 1989). Comments: "The section on Pattern mathematics is excellent." Rumelhart, D. E. and McClelland, J. L. (1986). Parallel Distributed Processing: Explorations in the Microstructure of Cognition (volumes 1 & 2). The MIT Press. Comments: "As a computer scientist I found the two Rumelhart and McClelland books really heavy going and definitely not the sort of thing to read if you are a beginner."; "It's quite readable, and affordable (about $65 for both volumes)."; "THE Connectionist bible.". 3.) Introductory journal articles: Hinton, G. E. (1989). Connectionist learning procedures. Artificial Intelligence, Vol. 40, pp. 185--234. Comments: "One of the better neural networks overview papers, although the distinction between network topology and learning algorithm is not always very clear. Could very well be used as an introduction to neural networks." Knight, K. (1990). Connectionist, Ideas and Algorithms. Communications of the ACM. November 1990. Vol.33 nr.11, pp 59-74. Comments:"A good article, while it is for most people easy to find a copy of this journal." Kohonen, T. (1988). An Introduction to Neural Computing. Neural Networks, vol. 1, no. 1. pp. 3-16. Comments: "A general review". 4.) Not-quite-so-introductory literature: Anderson, J. A. and Rosenfeld, E. (Eds). (1988). Neurocomputing: Foundations of Research. The MIT Press: Cambridge, MA. Comments: "An expensive book, but excellent for reference. It is a collection of reprints of most of the major papers in the field."; Anderson, J. A., Pellionisz, A. and Rosenfeld, E. (Eds). (1990). Neurocomputing 2: Directions for Research. The MIT Press: Cambridge, MA. Comments: "The sequel to their well-known Neurocomputing book." Caudill, M. and Butler, C. (1990). Naturally Intelligent Systems. MIT Press: Cambridge, Massachusetts. (ISBN 0-262-03156-6). Comments: "I guess one of the best books I read"; "May not be suited for people who want to do some research in the area". Khanna, T. (1990). Foundations of Neural Networks. Addison-Wesley: New York. Comments: "Not so bad (with a page of erroneous formulas (if I remember well), and #hidden layers isn't well described)."; "Khanna's intention in writing his book with math analysis should be commended but he made several mistakes in the math part". Levine, D. S. (1990). Introduction to Neural and Cognitive Modeling. Lawrence Erlbaum: Hillsdale, N.J. Comments: "Highly recommended". Lippmann, R. P. (April 1987). An introduction to computing with neural nets. IEEE Acoustics, Speech, and Signal Processing Magazine. vol. 2, no. 4, pp 4-22. Comments: "Much acclaimed as an overview of neural networks, but rather inaccurate on several points. The categorization into binary and continuous- valued input neural networks is rather arbitrary, and may work confusing for the unexperienced reader. Not all networks discussed are of equal importance." Maren, A., Harston, C. and Pap, R., (1990). Handbook of Neural Computing Applications. Academic Press. ISBN: 0-12-471260-6. (451 pages) Comments: "They cover a broad area"; "Introductory with suggested applications implementation". Pao, Y. H. (1989). Adaptive Pattern Recognition and Neural Networks Addison-Wesley Publishing Company, Inc. (ISBN 0-201-12584-6) Comments: "An excellent book that ties together classical approaches to pattern recognition with Neural Nets. Most other NN books do not even mention conventional approaches." Rumelhart, D. E., Hinton, G. E. and Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, vol 323 (9 October), pp. 533-536. Comments: "Gives a very good potted explanation of backprop NN's. It gives sufficient detail to write your own NN simulation." Simpson, P. K. (1990). Artificial Neural Systems: Foundations, Paradigms, Applications and Implementations. Pergamon Press: New York. Comments: "Contains a very useful 37 page bibliography. A large number of paradigms are presented. On the negative side the book is very shallow. Best used as a complement to other books". Zeidenberg. M. (1990). Neural Networks in Artificial Intelligence. Ellis Horwood, Ltd., Chichester. Comments: "Gives the AI point of view". Zornetzer, S. F., Davis, J. L. and Lau, C. (1990). An Introduction to Neural and Electronic Networks. Academic Press. (ISBN 0-12-781881-2) Comments: "Covers quite a broad range of topics (collection of articles/papers )."; "Provides a primer-like introduction and overview for a broad audience, and employs a strong interdisciplinary emphasis". ------------------------------------------------------------------------ -A11.) Any journals and magazines about Neural Networks ? [to be added: comments on speed of reviewing and publishing, whether they accept TeX format or ASCII by e-mail, etc.] A. Dedicated Neural Network Journals: ===================================== Title: Neural Networks Publish: Pergamon Press Address: Pergamon Journals Inc., Fairview Park, Elmsford, New York 10523, USA and Pergamon Journals Ltd. Headington Hill Hall, Oxford OX3, 0BW, England Freq.: 6 issues/year (vol. 1 in 1988) Cost/Yr: Free with INNS membership ($45?), Individual $65, Institution $175 ISSN #: 0893-6080 Remark: Official Journal of International Neural Network Society (INNS). Contains Original Contributions, Invited Review Articles, Letters to Editor, Invited Book Reviews, Editorials, Announcements and INNS News, Software Surveys. This is probably the most popular NN journal. (Note: Remarks supplied by Mike Plonski "plonski@aero.org") ------- Title: Neural Computation Publish: MIT Press Address: MIT Press Journals, 55 Hayward Street Cambridge, MA 02142-9949, USA, Phone: (617) 253-2889 Freq.: Quarterly (vol. 1 in 1989) Cost/Yr: Individual $45, Institution $90, Students $35; Add $9 Outside USA ISSN #: 0899-7667 Remark: Combination of Reviews (10,000 words), Views (4,000 words) and Letters (2,000 words). I have found this journal to be of outstanding quality. (Note: Remarks supplied by Mike Plonski "plonski@aero.org") ----- Title: IEEE Transaction on Neural Networks Publish: Institute of Electrical and Electronics Engineers (IEEE) Address: IEEE Service Cemter, 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ, 08855-1331 USA. Tel: (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., 687 Hartwell Street, Teaneck, NJ 07666. Tel: (201) 837-8858; Eurpoe: World Scientific Publishing Co. Pte. Ltd., 73 Lynton Mead, Totteridge, London N20-8DH, England. Tel: (01) 4462461; Other: World Scientific Publishing Co. Pte. Ltd., Farrer Road, P.O. Box 128, Singapore 9128. Tel: 2786188 Freq.: Quarterly (Vol. 1 in 1990?) Cost/Yr: Individual $42, Institution $88 (plus $9-$17 for postage) ISSN #: 0129-0657 (IJNS) Remark: The International Journal of Neural Systems is a quarterly journal which covers information processing in natural and artificial neural systems. It publishes original contributions on all aspects of this broad subject which involves physics, biology, psychology, computer science and engineering. Contributions include research papers, reviews and short communications. The journal presents a fresh undogmatic attitude towards this multidisciplinary field with the aim to be a forum for novel ideas and improved understanding of collective and cooperative phenomena with computational capabilities. (Note: Remarks supplied by B. Lautrup (editor), "LAUTRUP%nbivax.nbi.dk@CUNYVM.CUNY.EDU" ) Review is reported to be very slow. ------ Title: Neural Network News Publish: AIWeek Inc. Address: Neural Network News, 2555 Cumberland Parkway, Suite 299, Atlanta, GA 30339 USA. Tel: (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 brandt kehoe "kehoe@csufres.CSUFresno.EDU") ------ Title: Connection Science: Journal of Neural Computing, Artificial Intelligence and Cognitive Research Publish: Carfax Publishing Address: Europe: Carfax Publishing Company, P. O. Box 25, Abingdon, Oxfordshire OX14 3UE, UK. USA: Carafax Publishing Company, 85 Ash Street, Hopkinton, MA 01748 Freq.: Quarterly (vol. 1 in 1989) Cost/Yr: Individual $82, Institution $184, Institution (U.K.) 74 pounds ----- Title: International Journal of Neural Networks Publish: Learned Information Freq.: Quarterly (vol. 1 in 1989) Cost/Yr: 90 pounds ISSN #: 0954-9889 Remark: The journal contains articles, a conference report (at least the issue I have), news and a calendar. (Note: remark provided by J.R.M. Smits "anjos@sci.kun.nl") ----- Title: Concepts in NeuroScience Publish: World Scientific Publishing Address: Same Address (?) as for International Journal of Neural Systems Freq.: Twice per year (vol. 1 in 1989) Remark: Mainly Review Articles(?) (Note: remarks by Osamu Saito "saito@nttica.NTT.JP") ----- Title: International Journal of Neurocomputing Publish: ecn Neurocomputing GmbH Freq.: Quarterly (vol. 1 in 1989) Remark: Commercial journal, not the academic periodicals (Note: remarks by Osamu Saito "saito@nttica.NTT.JP") Review has been reported to be fast (less than 3 months) ----- Title: Neurocomputers Publish: Gallifrey Publishing Address: Gallifrey Publishing, PO Box 155, Vicksburg, Michigan, 49097, USA Tel: (616) 649-3772 Freq. Monthly (1st issue 1987?) ISSN #: 0893-1585 Editor: Derek F. Stubbs Cost/Yr: $32 (USA, Canada), $48 (elsewhere) Remark: I only have one exemplar so I cannot give you much detail about the contents. It is a very small one (12 pages) but it has a lot of (short) information in it about e.g. conferences, books, (new) ideas etc. I don't think it is very expensive but I'm not sure. (Note: remark provided by J.R.M. Smits "anjos@sci.kun.nl") ------ Title: JNNS Newsletter (Newsletter of the Japan Neural Network Society) Publish: The Japan Neural Network Society Freq.: Quarterly (vol. 1 in 1989) Remark: (IN JAPANESE LANGUAGE) Official Newsletter of the Japan Neural Network Society(JNNS) (Note: remarks by Osamu Saito "saito@nttica.NTT.JP") ------- Title: Neural Networks Today Remark: I found this title in a bulletin board of october last year. It was a message of Tim Pattison, timpatt@augean.OZ (Note: remark provided by J.R.M. Smits "anjos@sci.kun.nl") ----- Title: Computer Simulations in Brain Science ----- Title: Internation Journal of Neuroscience ----- Title: Neural Network Computation Remark: Possibly the same as "Neural Computation" ----- Title: Neural Computing and Applications Freq.: Quarterly Publish: Springer Verlag Cost/yr: 120 Pounds Remark: Is the journal of the Neural Computing Applications Forum. Publishes original research and other information in the field of practical applications of neural computing. B. NN Related Journals ====================== Title: Complex Systems Publish: Complex Systems Publications Address: Complex Systems Publications, Inc., P.O. Box 6149, Champaign, IL 61821-8149, USA Freq.: 6 times per year (1st volume is 1987) ISSN #: 0891-2513 Cost/Yr: Individual $75, Institution $225 Remark: Journal COMPLEX SYSTEMS devotes to the rapid publication of research on the science, mathematics, and engineering of systems with simple components but complex overall behavior. Send mail to "jcs@complex.ccsr.uiuc.edu" for additional info. (Remark is from announcement on Net) ----- Title: Biological Cybernetics (Kybernetik) Publish: Springer Verlag Remark: Monthly (vol. 1 in 1961) ----- Title: Various IEEE Transactions and Magazines Publish: IEEE Remark: Primarily see IEEE Trans. on System, Man and Cybernetics; Various Special Issues: April 1990 IEEE Control Systems Magazine.; May 1989 IEEE Trans. Circuits and Systems.; July 1988 IEEE Trans. Acoust. Speech Signal Process. ----- Title: The Journal of Experimental and Theoretical Artificial Intelligence Publish: Taylor & Francis, Ltd. Address: London, New York, Philadelphia Freq.: ? (1st issue Jan 1989) Remark: For submission information, please contact either of the editors: Eric Dietrich Chris Fields PACSS - Department of Philosophy Box 30001/3CRL SUNY Binghamton New Mexico State University Binghamton, NY 13901 Las Cruces, NM 88003-0001 dietrich@bingvaxu.cc.binghamton.edu cfields@nmsu.edu ----- Title: The Behavioral and Brain Sciences Publish: Cambridge University Press Remark: (Expensive as hell, I'm sure.) This is a delightful journal that encourages discussion on a variety of controversial topics. I have especially enjoyed reading some papers in there by Dana Ballard and Stephen Grossberg (separate papers, not collaborations) a few years back. They have a really neat concept: they get a paper, then invite a number of noted scientists in the field to praise it or trash it. They print these commentaries, and give the author(s) a chance to make a rebuttal or concurrence. Sometimes, as I'm sure you can imagine, things get pretty lively. I'm reasonably sure they are still at it--I think I saw them make a call for reviewers a few months ago. Their reviewers are called something like Behavioral and Brain Associates, and I believe they have to be nominated by current associates, and should be fairly well established in the field. That's probably more than I really know about it but maybe if you post it someone who knows more about it will correct any errors I have made. The main thing is that I liked the articles I read. (Note: remarks by Don Wunsch ) ----- Title: International Journal of Applied Intelligence Publish: Kluwer Academic Publishers Remark: first issue in 1990(?) ----- Title: Bulletin of Mathematica Biology ----- Title: Intelligence ----- Title: Journal of Mathematical Biology ----- Title: Journal of Complex System ----- Title: AI Expert Publish: Miller Freeman Publishing Co., for subscription call ++415-267-7672. Remark: Regularly includes ANN related articles, product announcements, and application reports. Listings of ANN programs are available on AI Expert affiliated BBS's ----- Title: International Journal of Modern Physics C Publish: World Scientific Publ. Co. Farrer Rd. P.O.Box 128, Singapore 9128 or: 687 Hartwell St., Teaneck, N.J. 07666 U.S.A or: 73 Lynton Mead, Totteridge, London N20 8DH, England Freq: published quarterly Eds: G. Fox, H. Herrmann and K. Kaneko ----- Title: Machine Learning Publish: Kluwer Academic Publishers Address: Kluwer Academic Publishers P.O. Box 358 Accord Station Hingham, MA 02018-0358 USA Freq.: Monthly (8 issues per year; increasing to 12 in 1993) Cost/Yr: Individual $140 (1992); Member of AAAI or CSCSI $88 Remark: Description: Machine Learning is an international forum for research on computational approaches to learning. The journal publishes articles reporting substantive research results on a wide range of learning methods applied to a variety of task domains. The ideal paper will make a theoretical contribution supported by a computer implementation. The journal has published many key papers in learning theory, reinforcement learning, and decision tree methods. Recently it has published a special issue on connectionist approaches to symbolic reasoning. The journal regularly publishes issues devoted to genetic algorithms as well. C. Journals loosely related to NNs ================================== JOURNAL OF COMPLEXITY (Must rank alongside Wolfram's Complex Systems) IEEE ASSP Magazine (April 1987 had the Lippmann intro. which everyone likes to cite) ARTIFICIAL INTELLIGENCE (Vol 40, September 1989 had the survey paper by Hinton) COGNITIVE SCIENCE (the Boltzmann machine paper by Ackley et al appeared here in Vol 9, 1983) COGNITION (Vol 28, March 1988 contained the Fodor and Pylyshyn critique of connectionism) COGNITIVE PSYCHOLOGY (no comment!) JOURNAL OF MATHEMATICAL PSYCHOLOGY (several good book reviews) ------------------------------------------------------------------------ -A12.) The most important conferences concerned with Neural Networks ? [preliminary] [to be added: has taken place how often yet; most emphasized topics; where to get proceedings ] A. Dedicated Neural Network Conferences: 1. Neural Information Processing Systems (NIPS) Annually in Denver, Colorado; late November or early December 2. International Joint Conference on Neural Networks (IJCNN) co-sponsored by INNS and IEEE 3. Annual Conference on Neural Networks (ACNN) 4. International Conference on Artificial Neural Networks (ICANN) Annually in Europe(?), 1992 in Brighton Major conference of European Neur. Netw. Soc. (ENNS) B. Other Conferences 1. International Joint Conference on Artificial Intelligence (IJCAI) 2. Intern. Conf. on Acustics, Speech and Signal Processing (ICASSP) 3. Annual Conference of the Cognitive Science Society 4. [Vision Conferences?] C. Pointers to Conferences 1. The journal "Neural Networks" has a long list of conferences, workshops and meetings in each issue. This is quite interdisciplinary. 2. There is a regular posting on comp.ai.neural-nets from Paultje Bakker: "Upcoming Neural Network Conferences", which lists names, dates, locations, contacts, and deadlines. ------------------------------------------------------------------------ -A13.) Neural Network Associations ? [Is this data still correct ? Who will send me some update ?] 1. International Neural Network Society (INNS). INNS membership includes subscription to "Neural Networks", the official journal of the society. Membership is $55 for non-students and $45 for students per year. Address: INNS Membership, P.O. Box 491166, Ft. Washington, MD 20749. 2. International Student Society for Neural Networks (ISSNNets). Membership is $5 per year. Address: ISSNNet, Inc., P.O. Box 15661, Boston, MA 02215 USA 3. Women In Neural Network Research and technology (WINNERS). Address: WINNERS, c/o Judith Dayhoff, 11141 Georgia Ave., Suite 206, Wheaton, MD 20902. Telephone: 301-933-9000. 4. European Neural Network Society (ENNS) 5. Japanese Neural Network Society (JNNS) Address: Japanese Neural Network Society Department of Engineering, Tamagawa University, 6-1-1, Tamagawa Gakuen, Machida City, Tokyo, 194 JAPAN Phone: +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... Address : ACTH - Le Castelnau R2 23 avenue de la Galline 34170 Castelnau-le-Lez FRANCE Contact : jdmuller@vnet.ibm.com 7. Neurosciences et Sciences de l'Ingenieur (NSI) Biology & Computer Science Activity : conference (every year) Address : NSI - TIRF / INPG 46 avenue Felix Viallet 38031 Grenoble Cedex FRANCE ------------------------------------------------------------------------ -A14.) Other sources of information about NNs ? 1. Neuron Digest Internet Mailing List. From the welcome blurb: "Neuron-Digest is a list (in digest form) dealing with all aspects of neural networks (and any type of network or neuromorphic system)" Moderated by Peter Marvit. To subscribe, send email to neuron-request@cattell.psych.upenn.edu comp.ai.neural-net readers also find the messages in that newsgroup in the form of digests. 2. Usenet groups comp.ai.neural-nets (Oha ! :-> ) and comp.theory.self-org-sys There is a periodic posting on comp.ai.neural-nets sent by srctran@world.std.com (Gregory Aharonian) about Neural Network patents. 3. Central Neural System Electronic Bulletin Board Modem: 509-627-6CNS; Sysop: Wesley R. Elsberry; P.O. Box 1187, Richland, WA 99352; welsberr@sandbox.kenn.wa.us Available thrugh FidoNet, RBBS-Net, and other EchoMail compatible bulletin board systems as NEURAL_NET echo. 4. Neural ftp archive site funic.funet.fi Is administrating a large collection of neural network papers and software at the Finnish University Network file archive site funic.funet.fi in directory /pub/sci/neural Contains all the public domain software and papers that they have been able to find. ALL of these files have been transferred from FTP sites in U.S. and are mirrored about every 3 months at fastest. Contact: magi@funic.funet.fi or magi@utu.fi (my home university address) 5. USENET newsgroup comp.org.issnnet Forum for discussion of academic/student-related issues in NNs, as well as information on ISSNNet (see A13) and its activities. ------------------------------------------------------------------------ -A15.) Freely available software packages for NN simulation ? [This is a bit chaotic and needs reorganization. A bit more information about what the various programs can do, on which platform they run, and how big they are would also be nice. And some important packages are still missing (?) Who volunteers for that ?] 1. Rochester Connectionist Simulator A quite versatile simulator program for arbitrary types of neural nets. Comes with a backprop package and a X11/Sunview interface. anonymous FTP from cs.rochester.edu (192.5.53.209) directory : pub/simulator files: README (8 KB) (documentation:) rcs_v4.2.justdoc.tar.Z (1.6 MB) (source code:) rcs_v4.2.justsrc.tar.Z (1.4 MB) 2. UCLA-SFINX ftp 131.179.16.6 (retina.cs.ucla.edu) Name: sfinxftp Password: joshua directory: pub/ files : README sfinx_v2.0.tar.Z Email info request : sfinx@retina.cs.ucla.edu 3. NeurDS request from mcclanahan%cookie.dec.com@decwrl.dec.com simulator for DEC systems supporting VT100 terminal. OR anonymous ftp gatekeeper.dec.com [16.1.0.2] directory: pub/DEC file: NeurDS031.tar.Z ( please check may be NeurDSO31.tar.Z ) 4. PlaNet5.7 (also known as SunNet) ftp 133.15.240.3 (tutserver.tut.ac.jp) pub/misc/PlaNet5.7.tar.Z or ftp 128.138.240.1 (boulder.colorado.edu) pub/generic-sources/PlaNet5.7.tar.Z (also the old PlaNet5.6.tar.Z) A popular connectionist simulator with versions to run under X Windows, and non-graphics terminals created by Yoshiro Miyata (Chukyo Univ., Japan). 60-page User's Guide in Postscript. Send any questions to miyata@sccs.chukyo-u.ac.jp 5. GENESIS anonymous ftp 131.215.135.64 ( genesis.cns.caltech.edu ) Register first via telnet genesis.cns.caltech.edu login as: genesis 6. Mactivation anonymous ftp from bruno.cs.colorado.edu [128.138.243.151] directory: /pub/cs/misc file: Mactivation-3.3.sea.hqx 7. CMU Connectionist Archive There is a lisp backprop simulator in the connectionist archive. unix> ftp b.gp.cs.cmu.edu (or 128.2.242.8) Name: ftpguest Password: cmunix ftp> cd connectionists/archives ftp> get backprop.lisp 8. Cascade Correlation Simulator There is a LISP and C version of the simulator based on Scott Fahlman's Cascade Correlation algorithm, who also created the LISP version. The C version was created by Scott Crowder. Anonymous ftp from pt.cs.cmu.edu (or 128.2.254.155) directory /afs/cs/project/connect/code files cascor1.lisp (56 KB) cascor1.c (108 KB) 9. Quickprop A variation of the back-propagation algorithm developed by Scott Fahlman. A LISP and C version can be obtained in the same directory as the cascade correlation simulator above. (25 KB) 10. DartNet DartNet is a Macintosh-based Neural Network Simulator. It makes full use of the Mac's graphical interface, and provides a number of powerful tools for building, editing, training, testing and examining networks. This program is available by anonymous ftp from dartvax.dartmouth.edu [129.170.16.4] as /pub/mac/dartnet.sit.hqx (124 KB) Copies may also be obtained through email from bharucha@dartmouth.edu. Along with a number of interface improvements and feature additions, v2.0 is an extensible simulator. That is, new network architectures and learning algorithms can be added to the system by writing small XCMD-like CODE resources called nDEF's ("Network Definitions"). A number of such architectures are included with v2.0, as well as header files for creating new nDEF's. Contact: sean@coos.dartmouth.edu (Sean P. Nolan) 11. SNNS "Stuttgarter Neuronale Netze Simulator" from the University of Stuttgart, Germany. A luxurious simulator for many types of nets; with X11 interface: Graphical topology editor, training visualisation, etc. ftp: ifi.informatik.uni-stuttgart.de [129.69.211.1] directory /pub/SNNS file SNNSv2.1.tar.Z OR SNNSv2.1.tar.Za[a-d] ( 826271 Bytes) manual SNNSv2.1.Manual.ps.Z (1041375 Bytes) SNNSv2.1.Readme ( 7645 Bytes) 12. Aspirin/MIGRAINES Aspirin/MIGRAINES 6.0 consists of a code generator that builds neural network simulations by reading a network description (written in a language called "Aspirin") and generates a C simulation. An interface (called "MIGRAINES") is provided to export data from the neural network to visualization tools. The system has been ported to a large number of platforms. The goal of Aspirin is to provide a common extendible front-end language and parser for different network paradigms. The MIGRAINES interface is a terminal based interface that allows you to open Unix pipes to data in the neural network. This replaces the NeWS1.1 graphical interface in version 4.0 of the Aspirin/MIGRAINES software. The new interface is not a simple to use as the version 4.0 interface but is much more portable and flexible. The MIGRAINES interface allows users to output neural network weight and node vectors to disk or to other Unix processes. Users can display the data using either public or commercial graphics/analysis tools. Example filters are included that convert data exported through MIGRAINES to formats readable by Gnuplot 3.0, Matlab, Mathematica, and xgobi. The software is available from two FTP sites: CMU's simulator collection on "pt.cs.cmu.edu" (128.2.254.155) in /afs/cs/project/connect/code/am6.tar.Z". and UCLA's cognitive science machine "ftp.cognet.ucla.edu" (128.97.50.19) in alexis/am6.tar.Z The compressed tar file is a little less than 2 megabytes. 13. Adaptive Logic Network kit Available from menaik.cs.ualberta.ca. This package differs from the traditional nets in that it uses logic functions rather than floating point; for many tasks, ALN's can show many orders of magnitude gain in training and performance speed. Anonymous ftp from menaik.cs.ualberta.ca [129.128.4.241] unix source code and examples: /pub/atree2.tar.Z (145 KB) Postscript documentation: /pub/atree2.ps.Z ( 76 KB) MS-DOS Windows 3.0 version: /pub/atree2.zip (353 KB) /pub/atree2zip.readme (1 KB) 14. NeuralShell Availible from FTP site quanta.eng.ohio-state.edu (128.146.35.1) in directory "pub/NeuralShell", filename "NeuralShell.tar". 15. PDP The PDP simulator package is available via anonymous FTP at nic.funet.fi (128.214.6.100) in /pub/sci/neural/sims/pdp.tar.Z (0.2 MB) The simulator is also available with the book "Explorations in Parallel Distributed Processing: A Handbook of Models, Programs, and Exercises" by McClelland and Rumelhart. MIT Press, 1988. Comment: "This book is often referred to as PDP vol III which is a very misleading practice! The book comes with software on an IBM disk but includes a makefile for compiling on UNIX systems. The version of PDP available at nic.funet.fi seems identical to the one with the book except for a bug in bp.c which occurs when you try to run a script of PDP commands using the DO command. This can be found and fixed easily." 16. Xerion Xerion is available via anonymous ftp from ftp.cs.toronto.edu in the directory /pub/xerion. xerion-3.0.PS.Z (0.9 MB) and xerion-3.0.tar.Z (1.1 MB) plus several concrete simulators built with xerion (about 0.3 MB each, see below). Xerion runs on SGI and Sun machines and uses X Windows for graphics. The software contains modules that implement Back Propagation, Recurrent Back Propagation, Boltzmann Machine, Mean Field Theory, Free Energy Manipulation, Hard and Soft Competitive Learning, and Kohonen Networks. Sample networks built for each of the modules are also included. Contact: xerion@ai.toronto.edu 17. Neocognitron simulator An implementation is available for anonymous ftp at [128.194.15.32] tamsun.tamu.edu as /pub/neocognitron.Z.tar or [129.12.21.7] unix.hensa.ac.uk as /pub/uunet/pub/ai/neural/neocognitron.tar.Z The simulator is written in C and comes with a list of references which are necessary to read to understand the specifics of the implementation. The unsupervised version is coded without (!) C-cell inhibition. 18. Multi-Module Neural Computing Environment (MUME) MUME is a simulation environment for multi-modules neural computing. It provides an object oriented facility for the simulation and training of multiple nets with various architectures and learning algorithms. MUME includes a library of network architectures including feedforward, simple recurrent, and continuously running recurrent neural networks. Each architecture is supported by a variety of learning algorithms. MUME can be used for large scale neural network simulations as it provides support for learning in multi-net environments. It also provide pre- and post-processing facilities. The modules are provided in a library. Several "front-ends" or clients are also available. MUME can be used to include non-neural computing modules (decision trees, ...) in applications. The software is the product of a number of staff and postgraduate students at the Machine Intelligence Group at Sydney University Electrical Engineering. The software is written in 'C' and is being used on Sun and DEC workstations. Efforts are underway to port it to the Fujitsu VP2200 vector processor using the VCC vectorising C compiler. MUME is made available to research institutions on media/doc/postage cost arrangements. Information on how to acquire it may be obtained by writing (or email) to: Marwan Jabri SEDAL Sydney University Electrical Engineering NSW 2006 Australia marwan@sedal.su.oz.au 19. LVQ_PAK, SOM_PAK These are packages for Learning Vector Quantization and Self-Organizing Maps, respectively. They have been built by the LVQ/SOM Programming Team of the Helsinki University of Technology, Laboratory of Computer and Information Science, Rakentajanaukio 2 C, SF-02150 Espoo, FINLAND There are versions for Unix and MS-DOS available from cochlea.hut.fi (130.233.168.48) in /pub/lvq_pak/lvq_pak-2.1.tar.Z (340 kB, Unix) /pub/lvq_pak/lvq_p2r1.exe (310 kB, MS-DOS self-extract archive) /pub/som_pak/som_pak-1.1.tar.Z (246 kB, Unix) /pub/som_pak/som_p1r1.exe (215 kB, MS-DOS self-extract archive) For some of these simulators there are user mailing lists. Get the packages and look into their documentation for further info. If you are using a small computer (PC, Mac, etc.) you may want to have a look at the Central Neural System Electronic Bulletin Board (see Answer 14) Modem: 509-627-6CNS; Sysop: Wesley R. Elsberry; P.O. Box 1187, Richland, WA 99352; welsberr@sandbox.kenn.wa.us There are lots of small simulator packages, the CNS ANNSIM file set. There is an ftp mirror site for the CNS ANNSIM file set at me.uta.edu (129.107.2.20) in the /pub/neural directory. Most ANN offerings are in /pub/neural/annsim. ------------------------------------------------------------------------ -A16.) Commercial software packages for NN simulation ? [preliminary] [who will write some short comment on each of the most important packages ?] The Number 1 of each volume of the journal "Neural Networks" has a list of some dozens of commercial suppliers of Neural Network things: Software, Hardware, Support, Programming, Design and Service. Here is a naked list of names of Simulators running on PC (and, partly, some other platforms, too): 1. NeuralWorks Professional 2+ (NeuralWare) 2. AIM 3. BrainMaker Professional 4. Brain Cel 5. Neural Desk 6. Neural Case 7. Neuro Windows 8. Explorenet 3000 9. NeuroShell (Systems Group) 10. Dynamind (Neurodynamix) ------------------------------------------------------------------------ -A17.) Neural Network hardware ? [preliminary] [who will write some short comment on the most important HW-packages and chips ?] The Number 1 of each volume of the journal "Neural Networks" has a list of some dozens of suppliers of Neural Network support: Software, Hardware, Support, Programming, Design and Service. Here is a list of companies contributed by xli@computing-maths.cardiff.ac.uk: 1. HNC, INC. 5501 Oberlin Drive San Diego California 92121 (619) 546-8877 and a second address at 7799 Leesburg Pike, Suite 900 Falls Church, Virginia 22043 (703) 847-6808 Note: Australian Dist.: Unitronics Tel : (09) 4701443 Contact: Martin Keye HNC markets: 'Image Document Entry Processing Terminal' - it recognises handwritten documents and converts the info to ASCII. 'ExploreNet 3000' - a NN demonstrator 'Anza/DP Plus'- a Neural Net board with 25MFlop or 12.5M peak interconnects per second. 2. SAIC (Sience Application International Corporation) 10260 Campus Point Drive MS 71, San Diego CA 92121 (619) 546 6148 Fax: (619) 546 6736 3. Micro Devices 30 Skyline Drive Lake Mary FL 32746-6201 (407) 333-4379 MicroDevices makes MD1220 - 'Neural Bit Slice' Each of the products mentioned sofar have very different usages. Although this sounds similar to Intel's product, the architectures are not. 4. Intel Corp 2250 Mission College Blvd Santa Clara, Ca 95052-8125 Attn ETANN, Mail Stop SC9-40 (408) 765-9235 Intel is making an experimental chip: 80170NW - Electrically trainable Analog Neural Network (ETANN) It has 64 'neurons' on it - almost fully internally connectted and the chip can be put in an hierarchial architecture to do 2 Billion interconnects per second. Support software has already been made by California Scientific Software 10141 Evening Star Dr #6 Grass Valley, CA 95945-9051 (916) 477-7481 Their product is called 'BrainMaker'. 5. NeuralWare, Inc Penn Center West Bldg IV Suite 227 Pittsburgh PA 15276 They only sell software/simulator but for many platforms. 6. Tubb Research Limited 7a Lavant Street Peterfield Hampshire GU32 2EL United Kingdom Tel: +44 730 60256 7. Adaptive Solutions Inc 1400 NW Compton Drive Suite 340 Beaverton, OR 97006 U. S. A. Tel: 503 - 690 - 1236 FAX: 503 - 690 - 1249 And here is an incomplete list of Neurocomputers (provided by jon@kongle.idt.unit.no (Jon Gunnar Solheim)): Overview over known Neural Computers with their newest known reference. \subsection*{Digital} \subsubsection{Special Computers} {\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 APx -- Array Processor Accelerator}\\ F.Pazienti.\\ Neural networks simulation with array processors. In {\it Advanced Computer Technology, Reliable Systems and Applications; Proceedings of the 5th Annual Computer Conference}, pages 547-551. IEEE Comput. Soc. Press, May 1991. ISBN: 0-8186-2141-9. {\bf ASP -- Associative String Processor}\\ A.Krikelis.\\ A novel massively associative processing architecture for the implementation artificial neural networks.\\ In {\it 1991 International Conference on Acoustics, Speech and Signal Processing}, volume 2, pages 1057-1060. IEEE Comput. Soc. Press, May 1991. {\bf BLAST}\\ J.G.Elias, M.D.Fisher, and C.M.Monemi.\\ A multiprocessor machine for large-scale neural network simulation. In {\it IJCNN91-Seattle: International Joint Conference on Neural Networks}, volume 1, pages 469-474. IEEE Comput. Soc. Press, July 1991. ISBN: 0-7883-0164-1. {\bf CNAPS Neurocomputer}\\ H.McCartor\\ Back Propagation Implementation on the Adaptive Solutions CNAPS Neurocomputer.\\ In {\it Advances in Neural Information Processing Systems}, 3, 1991. {\bf MA16 -- Neural Signal Processor} U.Ramacher, J.Beichter, and N.Bruls.\\ Architecture of a general-purpose neural signal processor.\\ In {\it IJCNN91-Seattle: International Joint Conference on Neural Networks}, volume 1, pages 443-446. IEEE Comput. Soc. Press, July 1991. ISBN: 0-7083-0164-1. {\bf NERV}\\ R.Hauser, H.Horner, R. Maenner, and M.Makhaniok.\\ Architectural Considerations for NERV - a General Purpose Neural Network Simulation System.\\ In {\it Workshop on Parallel Processing: Logic, Organization and Technology -- WOPPLOT 89}, pages 183-195. Springer Verlag, Mars 1989. ISBN: 3-5405-5027-5. {\bf NP -- Neural Processor}\\ D.A.Orrey, D.J.Myers, and J.M.Vincent.\\ A high performance digital processor for implementing large artificial neural networks.\\ In {\it Proceedings of of the IEEE 1991 Custom Integrated Circuits Conference}, pages 16.3/1-4. IEEE Comput. Soc. Press, May 1991. ISBN: 0-7883-0015-7. {\bf RAP -- Ring Array Processor }\\ N.Morgan, J.Beck, P.Kohn, J.Bilmes, E.Allman, and J.Beer.\\ The ring array processor: A multiprocessing peripheral for connectionist applications. \\ In {\it Journal of Parallel and Distributed Computing}, pages 248-259, April 1992. {\bf RENNS -- REconfigurable Neural Networks Server}\\ O.Landsverk, J.Greipsland, J.A.Mathisen, J.G.Solheim, and L.Utne.\\ RENNS - a Reconfigurable Computer System for Simulating Artificial Neural Network Algorithms.\\ In {\it Parallel and Distributed Computing Systems, Proceedings of the ISMM 5th International Conference}, pages 251-256. The International Society for Mini and Microcomputers - ISMM, October 1992. ISBN: 1-8808-4302-1. {\bf SMART -- Sparse Matrix Adaptive and Recursive Transforms}\\ P.Bessiere, A.Chams, A.Guerin, J.Herault, C.Jutten, and J.C.Lawson.\\ From Hardware to Software: Designing a ``Neurostation''.\\ In {\it VLSI design of Neural Networks}, pages 311-335, June 1990. {\bf SNAP -- Scalable Neurocomputer Array Processor} E.Wojciechowski.\\ SNAP: A parallel processor for implementing real time neural networks.\\ In {\it Proceedings of the IEEE 1991 National Aerospace and Electronics Conference; NAECON-91}, volume 2, pages 736-742. IEEE Comput.Soc.Press, May 1991. {\bf Toroidal Neural Network Processor}\\ S.Jones, K.Sammut, C.Nielsen, and J.Staunstrup.\\ Toroidal Neural Network: Architecture and Processor Granularity Issues.\\ In {\it VLSI design of Neural Networks}, pages 229-254, June 1990. \subsubsection{Standard Computers} {\bf EMMA-2}\\ R.Battiti, L.M.Briano, R.Cecinati, A.M.Colla, and P.Guido.\\ An application oriented development environment for Neural Net models on multiprocessor Emma-2.\\ In {\it Silicon Architectures for Neural Nets; Proceedings for the IFIP WG.10.5 Workshop}, pages 31-43. North Holland, November 1991. ISBN: 0-4448-9113-7. {\bf iPSC/860 Hypercube}\\ D.Jackson, and D.Hammerstrom\\ Distributing Back Propagation Networks Over the Intel iPSC/860 Hypercube}\\ In {\it IJCNN91-Seattle: International Joint Conference on Neural Networks}, volume 1, pages 569-574. IEEE Comput. Soc. Press, July 1991. ISBN: 0-7083-0164-1. {\bf SCAP -- Systolic/Cellular Array Processor}\\ Wei-Ling L., V.K.Prasanna, and K.W.Przytula.\\ Algorithmic Mapping of Neural Network Models onto Parallel SIMD Machines.\\ In {\it IEEE Transactions on Computers}, 40(12), pages 1390-1401, December 1991. ISSN: 0018-9340. ------------------------------------------------------------------------ -A19.) Databases for experimentation with NNs ? [are there any more ?] 1. The nn-bench Benchmark collection accessible via anonymous FTP on "pt.cs.cmu.edu" in directory "/afs/cs/project/connect/bench" or via the Andrew file system in the directory "/afs/cs.cmu.edu/project/connect/bench" In case of problems email contact is "nn-bench-request@cs.cmu.edu". The data sets in this repository include the 'nettalk' data, the 'two spirals' problem, a vowel recognition task, and a few others. 2. UCI machine learning database accessible via anonymous FTP on "ics.uci.edu" [128.195.1.1] in directory "/pub/machine-learning-databases" 3. NIST special databases of the National Institute Of Standards And Technology: NIST special database 2: Structured Forms Reference Set (SFRS) The NIST database of structured forms contains 5,590 full page images of simulated tax forms completed using machine print. THERE IS NO REAL TAX DATA IN THIS DATABASE. The structured forms used in this database are 12 different forms from the 1988, IRS 1040 Package X. These include Forms 1040, 2106, 2441, 4562, and 6251 together with Schedules A, B, C, D, E, F and SE. Eight of these forms contain two pages or form faces making a total of 20 form faces represented in the database. Each image is stored in bi-level black and white raster format. The images in this database appear to be real forms prepared by individuals but the images have been automatically derived and synthesized using a computer and contain no "real" tax data. The entry field values on the forms have been automatically generated by a computer in order to make the data available without the danger of distributing privileged tax information. In addition to the images the database includes 5,590 answer files, one for each image. Each answer file contains an ASCII representation of the data found in the entry fields on the corresponding image. Image format documentation and example software are also provided. The uncompressed database totals approximately 5.9 gigabytes of data. NIST special database 3: Binary Images of Handwritten Segmented Characters (HWSC) Contains 313,389 isolated character images segmented from the 2,100 full-page images distributed with "NIST Special Database 1". 223,125 digits, 44,951 upper-case, and 45,313 lower-case character images. Each character image has been centered in a separate 128 by 128 pixel region, error rate of the segmentation and assigned classification is less than 0.1%. The uncompressed database totals approximately 2.75 gigabytes of image data and includes image format documentation and example software. NIST special database 4: 8-Bit Gray Scale Images of Fingerprint Image Groups (FIGS) The NIST database of fingerprint images contains 2000 8-bit gray scale fingerprint image pairs. Each image is 512 by 512 pixels with 32 rows of white space at the bottom and classified using one of the five following classes: A=Arch, L=Left Loop, R=Right Loop, T=Tented Arch, W=Whirl. The database is evenly distributed over each of the five classifications with 400 fingerprint pairs from each class. The images are compressed using a modified JPEG lossless compression algorithm and require approximately 636 Megabytes of storage compressed and 1.1 Gigabytes uncompressed (1.6 : 1 compression ratio). The database also includes format documentation and example software. More short overview: Special Database 1 - NIST Binary Images of Printed Digits, Alphas, and Text Special Database 2 - NIST Structured Forms Reference Set of Binary Images Special Database 3 - NIST Binary Images of Handwritten Segmented Characters Special Database 4 - NIST 8-bit Gray Scale Images of Fingerprint Image Groups Special Database 6 - NIST Structured Forms Reference Set 2 of Binary Images Special Database 7 - NIST Test Data 1: Binary Images of Handprinted Segmented Characters Special Software 1 - NIST Scoring Package Release 1.0 Special Database 1 - $895.00 Special Database 2 - $250.00 Special Database 3 - $895.00 Special Database 4 - $250.00 Special Database 6 - $250.00 Special Database 7 - $1,000.00 Special Software 1 - $1,150.00 The system requirements for all databases are a 5.25" CD-ROM drive with software to read ISO-9660 format. Contact: Darrin L. Dimmick dld@magi.ncsl.nist.gov (301)975-4147 If you wish to order the database, please contact: Standard Reference Data National Institute of Standards and Technology 221/A323 Gaithersburg, MD 20899 (301)975-2208 or (301)926-0416 (FAX) 4. CEDAR CD-ROM 1: Database of Handwritten Cities, States, ZIP Codes, Digits, and Alphabetic Characters The Center Of Excellence for Document Analysis and Recognition (CEDAR) State University of New York at Buffalo announces the availability of CEDAR CDROM 1: USPS Office of Advanced Technology The database contains handwritten words and ZIP Codes in high resolution grayscale (300 ppi 8-bit) as well as binary handwritten digits and alphabetic characters (300 ppi 1-bit). This database is intended to encourage research in off-line handwriting recognition by providing access to handwriting samples digitized from envelopes in a working post office. Specifications of the database include: + 300 ppi 8-bit grayscale handwritten words (cities, states, ZIP Codes) o 5632 city words o 4938 state words o 9454 ZIP Codes + 300 ppi binary handwritten characters and digits: o 27,837 mixed alphas and numerics segmented from address blocks o 21,179 digits segmented from ZIP Codes + every image supplied with a manually determined truth value + extracted from live mail in a working U.S. Post Office + word images in the test set supplied with dic- tionaries of postal words that simulate partial recognition of the corresponding ZIP Code. + digit images included in test set that simulate automatic ZIP Code segmentation. Results on these data can be projected to overall ZIP Code recogni- tion performance. + image format documentation and software included System requirements are a 5.25" CD-ROM drive with software to read ISO- 9660 format. For any further information, including how to order the database, please contact: Jonathan J. Hull, Associate Director, CEDAR, 226 Bell Hall State University of New York at Buffalo, Buffalo, NY 14260 hull@cs.buffalo.edu (email) ------------------------------------------------------------------------ That's all folks. ======================================================================== Acknowledgements: Thanks to all the people who helped to get the stuff above into the posting. I cannot name them all, because I would make far too many errors then. :-> No ? Not good ? You want individual credit ? OK, OK. I'll try to name them all. But: no guarantee.... THANKS FOR HELP TO: (in alphabetical order of email adresses, I hope) Allen Bonde S.Taimi Ames anderson@atc.boeing.com Kim L. Blackwell Paul Bakker Yijun Cai L. Leon Campbell David DeMers Denni Rognvaldsson Wesley R. Elsberry Frank Schnorrenberg Gary Lawrence Murphy gaudiano@park.bu.edu Glen Clark guy@minster.york.ac.uk Jean-Denis Muller Jonathan Kamens Jon Gunnar Solheim Josef Nelissen Luke Koops William Mackeown Peter Marvit masud@worldbank.org Yoshiro Miyata Jyrki Alakuijala mrs@kithrup.com Maciek Sitnik Michael Plonski [myself] Richard Cornelius Rob Cunningham Osamu Saito Ted Stockwell Thomas G. Dietterich Thomas.Vogel@cl.cam.ac.uk Ulrich Wendl Matthew P Wiener Bye Lutz -- Lutz Prechelt (email: prechelt@ira.uka.de) | Whenever you Institut fuer Programmstrukturen und Datenorganisation | complicate things, Universitaet Karlsruhe; D-7500 Karlsruhe 1; Germany | they get (Voice: ++49/721/608-4068, FAX: ++49/721/694092) | less simple.