Received: from BUACCA by BUACCA.BU.EDU (Mailer R2.08 PTF009) with BSMTP id 6577; Fri, 19 Feb 93 01:31:27 EST Received: from noc2.dccs.upenn.edu by BUACCA.BU.EDU (IBM VM SMTP R1.2.1) with TCP; Fri, 19 Feb 93 01:31:24 EST Received: from CATTELL.PSYCH.UPENN.EDU by noc2.dccs.upenn.edu id AA16068; Fri, 19 Feb 93 01:28:48 -0500 Return-Path: Received: from LOCALHOST by cattell.psych.upenn.edu id AA28888; Fri, 19 Feb 93 00:23:09 EST Posted-Date: Fri, 19 Feb 93 00:22:32 EST From: "Neuron-Digest Moderator" To: Neuron-Distribution:; Subject: Neuron Digest V11 #12 (mailing lists & lots of discussion) Reply-To: "Neuron-Request" X-Errors-To: "Neuron-Request" Organization: University of Pennsylvania Date: Fri, 19 Feb 93 00:22:32 EST Message-Id: <28852.730099352@cattell.psych.upenn.edu> Sender: marvit@cattell.psych.upenn.edu Neuron Digest Friday, 19 Feb 1993 Volume 11 : Issue 12 Today's Topics: BIOSCI/bionet Frequently Asked Questions mailing list for cognitive neuroscientists NATO ASI: March 5 Deadline Approaching VLSI NN Re: "neural-net based software for digitization of maps" Applications for particle track segment detection?? Pattern Recognition Re: chip design neural net applications to fixed-income security markets Room sharing for ICNN pattern recognition (pratical database considerations) ? Send submissions, questions, address maintenance, and requests for old issues to "neuron-request@cattell.psych.upenn.edu". The ftp archives are available from cattell.psych.upenn.edu (130.91.68.31). Back issues requested by mail will eventually be sent, but may take a while. ---------------------------------------------------------------------- Subject: BIOSCI/bionet Frequently Asked Questions From: Dave Kristofferson [[ Editor's Note: This contains the information about the Neuroscience list which many of you readers have asked about. The entire file is about 48K long, so I've heavily edited the following with enough information about the Neuroscience list for you. If you (or colleagues) are biologically oriented (e.g., genetic sequencing, molecular biology, tropical ecology, etc), I highly recommend the BIOSCI/bionet resources. Consider browsing through the newsgroups or archives. -PM ]] BIOSCI/bionet Frequently Asked Questions (FAQ) ---------------------------------------------- (last revised - 1/15/93) This document describes the general purpose and uses of the BIOSCI/bionet newsgroups and provides details on how to participate in these forums. It is available for anonymous FTP from net.bio.net [134.172.2.69] in pub/BIOSCI/biosci.FAQ. This document may also be requested by e-mail to biosci@net.bio.net (use plain English - this is not a server address). It is posted the first of each month to the BIONEWS/bionet.announce newsgroup along with the BIOSCI information sheet and the list of changes to the newsgroups during the preceding month. The FAQ is also posted monthly to the USENET newsgroup news.answers and is archived along with other USENET newsgroup FAQs at pit-manager.mit.edu [18.172.1.27]. [[...]] Dissemination is by normal electronic mail and also over USENET in the form of the "bionet" newsgroups (see below for USENET details). The contents of the electronic mail distribution is identical to the USENET news distribution, but we encourage BIOSCI users to access the system through USENET news software whenever possible. E-mail distributions may eventually be phased out. As of October 1992, 59% of our readers used USENET news software instead of e-mail. [[...]] Two versions of the BIOSCI info sheet are available, one for the Americas and the Pacific Rim countries, and the second for Europe, Africa, and Central Asia. The former may be requested by e-mail to biosci@net.bio.net, while the latter may be requested from biosci@daresbury.ac.uk. [[...]] How do I request or cancel e-mail subscriptions to BIOSCI newsgroups? - --------------------------------------------------------------------- If you have access to USENET news software, then YOU DO NOT NEED AN E-MAIL SUBSCRIPTION! Only those people who need to receive postings by e-mail must request to be added to the mailing lists. USENET users can simply read the various bionet newsgroups using their news software. If your site has USENET news but does not get the bionet newsgroups, please request help by sending a message to biosci@net.bio.net. For those who need e-mail subscriptions or who want to cancel current e-mail subscriptions, please send a request to one of the following addresses. Please choose the site that serves your location. Simply pick the newsgroup(s) from the list above that you wish to subscribe to and request that your address be added to the chosen mailing lists. Please use plain English; no special message syntax is required in your subscription or cancellation request. Address Serving - ------- ------- biosci@net.bio.net The Americas and Pacific Rim biosci@daresbury.ac.uk Europe, Africa, and Central Asia ****If you are changing e-mail addresses****, please be sure to send a message to your appropriate biosci address above and request that your subscriptions be changed or canceled!! How can I get a list of newsgroups or my subscriptions? - ------------------------------------------------------- As with any other subscription correspondence, simply send a request to your appropriate BIOSCI distribution site: Address Serving - ------- ------- biosci@net.bio.net The Americas and Pacific Rim biosci@daresbury.ac.uk Europe, Africa, and Central Asia The most recent list of BIOSCI newsgroups/mailing addresses and the latest revision of the BIOSCI/bionet FAQ are posted the first of each month on the BIONEWS/bionet.announce newsgroup. You should save these postings for future reference. [[...]] MAILING LIST NAME USENET Newsgroup Name - ----------------- --------------------- AGEING bionet.molbio.ageing AGROFORESTRY bionet.agroforestry ARABIDOPSIS bionet.genome.arabidopsis BIOFORUM bionet.general BIO-INFORMATION-THEORY + bionet.info-theory BIONAUTS bionet.users.addresses BIONEWS ** bionet.announce BIO-JOURNALS bionet.journals.contents BIO-MATRIX bionet.molbio.bio-matrix BIO-SOFTWARE bionet.software CHROMOSOME-22 bionet.genome.chrom22 COMPUTATIONAL-BIOLOGY ** bionet.biology.computational EMBL-DATABANK bionet.molbio.embldatabank EMPLOYMENT bionet.jobs GDB bionet.molbio.gdb GENBANK-BB bionet.molbio.genbank GENETIC-LINKAGE bionet.molbio.gene-linkage HIV-MOLECULAR-BIOLOGY bionet.molbio.hiv HUMAN-GENOME-PROGRAM bionet.molbio.genome-program IMMUNOLOGY bionet.immunology JOURNAL-NOTES bionet.journals.note METHODS-AND-REAGENTS bionet.molbio.methds-reagnts MOLECULAR-EVOLUTION bionet.molbio.evolution NEUROSCIENCE bionet.neuroscience PLANT-BIOLOGY bionet.plants POPULATION-BIOLOGY bionet.population-bio PROTEIN-ANALYSIS bionet.molbio.proteins PROTEIN-CRYSTALLOGRAPHY bionet.xtallography SCIENCE-RESOURCES bionet.sci-resources TROPICAL-BIOLOGY bionet.biology.tropical VIROLOGY bionet.virology WOMEN-IN-BIOLOGY bionet.women-in-bio + full name is BIOLOGICAL-INFORMATION-THEORY-AND-CHOWDER-SOCIETY ** Note that newsgroups flagged with ** are moderated, i.e., postings are directed to a moderator (editor) who later forwards messages (possibly edited or condensed) to the newsgroup. NEWSGROUP NAME TOPIC - -------------- ----- AGEING Discussions about ageing research AGROFORESTRY Discussions about agroforestry research ARABIDOPSIS Newsgroup for the Arabidopsis Genome Project BIOFORUM Discussions about biological topics for which there is not yet a dedicated newsgroup BIOLOGICAL-INFORMATION- THEORY-AND-CHOWDER-SOCIETY Applications of information theory to biology BIONAUTS Question/answer forum for help using electronic networks, locating e-mail addresses, etc. BIONEWS ** General announcements of widespread interest to biologists BIO-JOURNALS Tables of Contents of biological journals BIO-MATRIX Applications of computers to biological databases BIO-SOFTWARE Information on software for the biological sciences CHROMOSOME-22 Mapping and Sequencing of Human Chromosome 22 COMPUTATIONAL-BIOLOGY ** Mathematical and computer applications in biology EMBL-DATABANK Messages to and from the EMBL database staff EMPLOYMENT Job opportunities GDB Messages to and from the Genome Data Bank staff GENBANK-BB Messages to and from the GenBank database staff GENETIC-LINKAGE Newsgroup for genetic linkage analysis HIV-MOLECULAR-BIOLOGY Discussions about the molecular biology of HIV HUMAN-GENOME-PROGRAM NIH-sponsored newsgroup on human genome issues IMMUNOLOGY Discussions about research in immunology JOURNAL-NOTES Practical advice on dealing with professional journals METHODS-AND-REAGENTS Requests for information and lab reagents MOLECULAR-EVOLUTION Discussions about research in molecular evolution NEUROSCIENCE Discussions about research in the neurosciences PLANT-BIOLOGY Discussions about research in plant biology POPULATION-BIOLOGY Discussions about research in population biology PROTEIN-ANALYSIS Discussions about research on proteins and messages for the PIR and SWISS-PROT databank staffs. PROTEIN-CRYSTALLOGRAPHY Discussion about crystallography of macromolecules and messages for the PDB staff SCIENCE-RESOURCES Information from/about scientific funding agencies TROPICAL-BIOLOGY Discussions about research in tropical biology VIROLOGY Discussions about research in virology WOMEN-IN-BIOLOGY Discussions about issues concerning women biologists ** Note that newsgroups flagged with ** are moderated, i.e., postings are directed to a moderator (editor) who later forwards messages (possibly edited or condensed) to the newsgroup. ------------------------------ Subject: mailing list for cognitive neuroscientists From: kpc@pluto.arc.nasa.gov (k p c) Organization: NASA Ames Research Center AI Research Branch; Sterling. Date: 27 Oct 92 01:26:08 +0000 [[ Editor's Note: Here is another mailing list of interest to some. I attempted to joi, but have received nothing. I sent a new message a couple of days ago, still without reply. I can therefore not personally vouch for this list's existance. -PM ]] ANNOUNCEMENT OF THE COGNEURO (COGNITIVE NEUROSCIENCE) MAILING LIST SUBJECT this list is an informal, intentionally low-volume way to discuss matters at the interface of cognitive science and neuroscience. the discussion will be scientific and academic, covering biological aspects of behavior and cognitive issues in neuroscience. also discussable are curricula, graduate programs, and jobs in the field. HOW TO USE THE LIST please follow these examples exactly, so that my software works. to SUBSCRIBE, send mail like this. To: cogneuro-request@ptolemy.arc.nasa.gov Subject: cogneuro: subscribe to UNSUBSCRIBE, send mail like this. To: cogneuro-request@ptolemy.arc.nasa.gov Subject: cogneuro: unsubscribe you don't need to put anything in the body of the message. there will be no automatic confirmation, but you might get a note from me. to CHANGE YOUR EMAIL ADDRESS (also very polite to do if you know that your MACHINE WILL GO DOWN for a while, or in case you LEAVE THE NET) simply unsubscribe from your old address and resubscribe from your new address. this prevents error messages and prevents me from having to verify your address manually. to POST (send a message to everybody on the list), send mail to cogneuro@ptolemy.arc.nasa.gov, or followup to an existing message. e.g. To: cogneuro@ptolemy.arc.nasa.gov Subject: corpus callosum to ask a METAQUESTION, send it to cogneuro-request@ptolemy.arc.nasa.gov. suggestions for improving this announcement or the list are welcome. GUIDELINES the language of the list is english. the list is meant to be low in volume and high in s/n ratio. since cogneuro is a huge field, submissions shouldn't be too off-topic or otherwise not essentially scientific or academic. controversy and speculation are welcome, as are lack of controversy and rigor. since the emphasis is scientific and academic, participants are expected to be extremely tolerant of other participants' opinions and choice of words. the list is initially open to anybody who is interested. although i don't expect ever to need to exercise it, i reserve the right to remove anybody from the list if there are problems. i want to keep a spirit of free exchange of cognitive neuroscience. other than this, the list is unmoderated and informal. ------------------------------ Subject: NATO ASI: March 5 Deadline Approaching From: John Moody Date: Thu, 04 Feb 93 17:38:08 -0800 As the March 5th application deadline is now four weeks away, I am posting this notice again. NATO Advanced Studies Institute (ASI) on Statistics and Neural Networks June 21 - July 2, 1993, Les Arcs, France Directors: Professor Vladimir Cherkassky, Department of Electrical Eng., University of Minnesota, Minneapolis, MN 55455, tel.(612)625-9597, fax (612)625- 4583, email cherkass@ee.umn.edu Professor Jerome H. Friedman, Statistics Department, Stanford University, Stanford, CA 94309 tel(415)723-9329, fax(415)926-3329, email jhf@playfair.stanford.edu Professor Harry Wechsler, Computer Science Department, George Mason University, Fairfax VA22030, tel(703)993-1533, fax(703)993-1521, email wechsler@gmuvax2.gmu.edu List of invited lecturers: I. Alexander, L. Almeida, A. Barron, A. Buja, E. Bienenstock, G. Carpenter, V. Cherkassky, T. Hastie, F. Fogelman, J. Friedman, H. Freeman, F. Girosi, S. Grossberg, J. Kittler, R. Lippmann, J. Moody, G. Palm, R. Tibshirani, H. Wechsler, C. Wellekens Objective, Agenda and Participants: Nonparametric estimation is a problem of fundamental importance for many applications involving pattern classification and discrimination. This problem has been addressed in Statistics, Pattern Recognition, Chaotic Systems Theory, and more recently in Artificial Neural Network (ANN) research. This ASI will bring together leading researchers from these fields to present an up-to-date review of the current state-of-the art, to identify fundamental concepts and trends for future development, to assess the relative advantages and limitations of statistical vs neural network techniques for various pattern recognition applications, and to develop a coherent framework for the joint study of Statistics and ANNs. Topics range from theoretical modeling and adaptive computational methods to empirical comparisons between statistical and neural network techniques. Lectures will be presented in a tutorial manner to benefit the participants of ASI. A two-week programme is planned, complete with lectures, industrial/government sessions, poster sessions and social events. It is expected that over seventy students (which can be researchers or practitioners at the post-graduate or graduate level) will attend, drawn from each NATO country and from Central and Eastern Europe. The proceedings of ASI will be published by Springer-Verlag. Applications: Applications for participation at the ASI are sought. Prospective students, industrial or government participants should send a brief statement of what they intend to accomplish and what form their participation would take. Each application should include a curriculum vitae, with a brief summary of relevant scientific or professional accomplishments, and a documented statement of financial need (if funds are applied for). Optionally, applications may include a one page summary for making a short presentation at the poster session. Poster presentations focusing on comparative evaluation of statistical and neural network methods and application studies are especially sought. For junior applicants, support letters from senior members of the professional community familiar with the applicant's work would strengthen the application. Prospective participants from Greece, Portugal and Turkey are especially encouraged to apply. Costs and Funding: The estimated cost of hotel accommodations and meals for the two-week duration of the ASI is US$1,600. In addition, participants from industry will be charged an industrial registration fee, not to exceed US$1,000. Participants representing industrial sponsors will be exempt from the fee. We intend to subsidize costs of participants to the maximum extent possible by available funding. Prospective participants should also seek support from their national scientific funding agencies. The agencies, such as the American NSF or the German DFG, may provide some ASI travel funds upon the recommendation of an ASI director. Additional funds exist for students from Greece, Portugal and Turkey. We are also seeking additional sponsorship of ASI. Every sponsor will be fully acknowledged at the ASI site as well as in the printed proceedings. Correspondence and Registration: Applications should be forwarded to Dr. Cherkassky at the above address. Applications arriving after March 5, 1993 may not be considered. All approved applicants will be informed of the exact registration arrangements. Informal email inquiries can be addressed to Dr. Cherkassky at nato_asi@ee.umn.edu ------------------------------ Subject: VLSI NN From: Gasser Auda Date: Sat, 06 Feb 93 13:30:14 -0600 [[ Editor's Note: I'm starting to reject these vague general calls for help, due to the increased volume of submissions to the Digest. However, I don't want to shut out the rank beginners from this exciting field either. So, I hope faithful readers will take pity on those who are just starting out with some pointers. -PM ]] Dear neural networkers, I'm performing a survey on NN solutions of the handwriting problems, especialy VLSI implemented solutions. If you have any advise, information, paper, or commercial product, just email me at: gasser@cs.uregina.ca THANKS IN ADVANCE. gasser ------------------------------ Subject: Re: "neural-net based software for digitization of maps" From: eytan@dpt-info.u-strasbg.fr (Michel Eytan, LILoL) Date: Sun, 07 Feb 93 11:24:26 +0100 >A colleague from India is asking me if there is any effort on "neural >network based software for the digitization of maps and photos (maps and >photos presumably obtained from remotely-sensed data)" Is there any one >who can help answer this question? Thanks > >Rao Vemuri >Dept. of Applied Science >UC Davis, Livermore, CA >vemuri@icdc.llnl.gov My colleague J. Korczak has a student, Ms. Hamadi, doing a Ph. D. on a similar subject. Hope this helps. Michel Eytan, U. Strasbourg II eytan@dpt-info.u-strasbg.fr Lab Info, Log & Lang, Dpt. Info V: +33 88 41 74 29 22 rue Descartes, 67084 Strasbourg F: +33 88 41 74 40 ------------------------------ Subject: Applications for particle track segment detection?? From: "Martin J. Dudziak" Date: Sun, 07 Feb 93 14:09:00 -0500 I am involved in a joint collaboration involving U-Pitt and the Institute of Nuclear Physics, Novosibirsk (Russia) wherein we are studying the possible useful applications of neural networks for particle track segment detection and track reconstruction. The process is now handled by some effective and well-established algorithms developed at CERN, Novosibirsk and elsewhere, but we believe that neural nets may have applicability for both improved detection (picking up on track segments registered in the drift chamber portion of the detector) and performance, particularly as the data rates in the detection process will be quite high (@40-100MBytes/sec.) in the next generation accelerator and we are working with a parallel processing architecture already to meet that computing requirement. We are aware of some work in the high energy physics community involving neural networks, most of which is more concerned with classification of particle interactions - the step that follows after one has identified tracks of interest. Much of that work has also not involved the low-level end of the detection process such as we are concerned about, nor has it involved the density of tracks. I thought it would be useful to inquire through the neural network community in grapevines such as Neuron-Digest to see if people are aware of projects applying neural nets to problems such as the above. Any information on NNs in HEP and for track detection/reconstruction in particular will be greatly appreciated. Whatever is sent will be compiled into a bibliography and redistributed to those who are interested. Martin Dudziak c/o J. Thompson, School of Physics, Univ. of Pittsburgh dudziak@vms.cis.pitt.edu Thank you! ------------------------------ Subject: Pattern Recognition From: VOGLINOG@AGR04.ST.IT Date: Mon, 08 Feb 93 11:47:55 +0700 [[ Editor's Note: Again, a rather general and vague request. Perhaps someone from Italy on this mailing list could help out a fellow countryperson? -PM ]] Hello. Where to get some software on pattern recognition based on neural network ? Thank you. GIuseppe Voglino SGS-Thomson Microelectronics Neural Network Group R&D Agrate B. _ MILAN - ITALY ------------------------------ Subject: Re: chip design From: blam@Engn.Uwindsor.Ca (B Lam) Date: Mon, 08 Feb 93 13:06:16 -0500 [[ Editor's Note: This plucky person rephrased his question from "Tell me about neural nets" to the follow (somewhat more specific) question... after I rejected his first attempt. My public reply to the specifics of his questio would be to look at Carver Mead's book (MIT Press?) "Analog VLSI" to start, but I don't know whether it covers fuzzy stuff. -PM ]] I'm looking the analog VLSI design for the Neural Network. I'm thinking to combine Fuzzy into NN. Is there any paper that talk about this topic? I'd look the journal of IEEE Tran. on NN but it seems to talk about the theory or software only. So please help me with this one. Thanks! ------------------------------ Subject: neural net applications to fixed-income security markets From: danlap@internet.sbi.com (Dan LaPushin) Date: Mon, 08 Feb 93 18:26:43 -0500 To The Editor, I am new to the field of neural networks but have a strong background in mathematics, economics, and some computer programming. I work at a large Wall St. firm and am interested in applying neural network technology to the field of fixed-income research. Such instruments include bonds, mortgage-backed securities and the like. There seems to be, as far as I can tell, little research into neural net application to such markets. I suspect this is because the data is hard to come by for those not in the field, but I'm not sure. Could you direct me to any research in this area so that I don't inadvertently recreate the wheel? Thanks for your help! Dan LaPushin I'm on your mailing list as danlap@sp_server.sbi.com ------------------------------ Subject: Room sharing for ICNN From: nanda@ogicse.cse.ogi.edu (Nandakishore Kambhatla) Organization: Oregon Graduate Institute (formerly OGC), Beaverton, OR Date: 10 Feb 93 18:02:30 +0000 Hi, I am a male graduate student attending ICNN'93 at SF. I am looking for roommates (1) to share a hotel room with at San Fransisco for ICNN'93. The conference runs from Mar 29th to April 1st. Please respond thru email in case you are interested. - -Nanda Nandakishore Kambhatla email: nanda@cse.ogi.edu phone: daytime-(503)-690-1121 (Extn-7051) evenings-(503)-646-5777 ------------------------------ Subject: pattern recognition (pratical database considerations) ? From: "Duane A. White" Date: Sat, 13 Feb 93 03:45:41 -0600 I am interested in pattern recognition. In my particular application I would like to compare a 2D monochrome bitmap image with those in a database. The program should determine if there is a match, and if not then add it to the database. Most of the literature I've read on pattern matching networks use a relatively small set of classification patterns (such as letters of the alphabet, numbers). In my case it wouldn't seem practical to train a single network to identify every entry in the database (on the order of hundreds or thousands of entries). Is there something fundemental in the approach that I'm missing? Also the program should to a small degree be rotation and translation invariant. ------------------------------ End of Neuron Digest [Volume 11 Issue 12] ***************************************** Received: from BUACCA by BUACCA.BU.EDU (Mailer R2.08 PTF009) with BSMTP id 6666; Tue, 23 Feb 93 12:54:11 EST Received: from noc2.dccs.upenn.edu by BUACCA.BU.EDU (IBM VM SMTP R1.2.1) with TCP; Tue, 23 Feb 93 12:54:08 EST Received: from CATTELL.PSYCH.UPENN.EDU by noc2.dccs.upenn.edu id AA03416; Tue, 23 Feb 93 12:44:05 -0500 Return-Path: Received: from LOCALHOST by cattell.psych.upenn.edu id AA04331; Tue, 23 Feb 93 10:57:09 EST Posted-Date: Tue, 23 Feb 93 10:56:33 EST From: "Neuron-Digest Moderator" To: Neuron-Distribution:; Subject: Neuron Digest V11 #13 (discussion) Reply-To: "Neuron-Request" X-Errors-To: "Neuron-Request" Organization: University of Pennsylvania Date: Tue, 23 Feb 93 10:56:33 EST Message-Id: <4321.730482993@cattell.psych.upenn.edu> Sender: marvit@cattell.psych.upenn.edu Neuron Digest Tuesday, 23 Feb 1993 Volume 11 : Issue 13 Today's Topics: Biologically Plausible Dynamic Artificial Neural Networks Reviewed Send submissions, questions, address maintenance, and requests for old issues to "neuron-request@cattell.psych.upenn.edu". The ftp archives are available from cattell.psych.upenn.edu (130.91.68.31). Back issues requested by mail will eventually be sent, but may take a while. ---------------------------------------------------------------------- Subject: Biologically Plausible Dynamic Artificial Neural Networks Reviewed From: Paul Fawcett Date: Sat, 06 Feb 93 18:57:41 +0000 Thank you for publishing in Neuron Digest Vol. 11, Issue 6 my original posting on the subject of 'Biologically Plausible Dynamic Artificial Networks'. As a follow-up to this post I circulated a summary of replies to those who contacted me directly by email. Each contributor agreed to their comments being quoted in this way. Please feel free to use this material, in full or in part, for any future edition of the Neuron Digest. The summary follows: Last update: 20 JAN 93 DISTRIBUTION: Ulf Andrick Mark J. Crosbie S. E. Fahlman Bernie French John R. Mcdonnell Larry D. Pyeatt Bill Saidel Mark W. Tilden Jari Vaario Paul Verschure Stanley Zietz THIS BULLETIN SUMMARIZES THE MAJORITY OF E-MAIL REPLIES TO THE FOLLOWING USENET ARTICLE: All contributors have agreed to publication of their comments. From: paulf@manor.demon.co.uk (Paul Fawcett) Newsgroups: comp.ai, comp.ai.neural-nets, sci.cognitive, comp.theory.cell-automata, bionet.neuroscience, bionet.molbio.evolution, bionet.software Subject: Biologically Plausible Dynamic Artificial Neural Networks Date: Tue, 05 Jan 93 05:53:57 GMT Biologically Plausible Dynamic Artificial Neural Networks. ------------------------------------------------------------ A *Dynamic Artificial Neural Network* (DANN) [1] [my acronym] possesses processing elements that are created and/or annihilated, either in real time or as some part of a development phase [2]. Of particular interest is the possibility of constructing *biologically plausible* DANN's that models developmental neurobiological strategies for establishing and modifying processing elements and their connections. Work with cellular automata in modeling cell genesis and cell pattern formation could be applicable to the design of DANN topologies. Likewise, biological features that are determined by genetic or evolutionary factors [3] would also have a role to play. Putting all this together with a view to constructing a working DANN, possessing cognitive/behavioral attributes of a biological system is a tall order; the modeling of nervous systems in simple organisms may be the best approach when dealing with a problem of such complexity [4]. Any comments, opinions or references in respect of the above assertions would be most welcome. References. 1. Ross, M. D., et al (1990); Toward Modeling a Dynamic Biological Neural Network, Mathl Comput. Modeling, Vol 13 No.7, pp97-105. 2. Lee, Tsu-Chang,(1991); Structure Level Adaptation for Artificial Neural Networks, Kluwer Academic Publishers. 3. Edleman, Gerald,(1987); Neural Darwinism the Theory of Neural Group Selection, Basic Books. 4. Beer, Randal, D,(1990); Intelligence as Adaptive Behavior : An Experiment in Computational Neuroethology. Academic Press. OVERVIEW The cross-posting strategy was successful in bringing about replies from contributors working in the computer and life sciences. Of those with a computer science background Jari Vaario is constructing (and publishing) DANN's inspired by biological systems and an evolutionary metaphor, John Mcdonnell also reports he has been able to construct evolving networks and Larry Pyeatt has had some success at modeling these processes. Mark Crossbie is interested in combining cellular automata and genetic algorithms to build simple machines. Mark Tilden offers a robotics viewpoint and suggests there may be some 'simple' and 'elegant' solutions. Paul Verschure offers some references to his own work in this field. Life science replies came from Bernie French who suggested the nematode as a suitable organism as model for a DANN. Stanley Zietz has suggested modeling simple structures rather than organisms, and draws attention to the work at NASA-Ames where they are attempting to make 'real' neural networks. To counter this Bill Saidel points out the distinction between the type of neuron being studied in NASA-Ames research and those in the cortex. Thanks to Ulf Andrick and Scott Fahlman for their replies on the Usenet. I hope this discussion will initiate further debate and those who participated will take the opportunity to make some informal contacts with the other contributors. Many thanks Paul. c/o AI Division School of Computer Science University of Westminster 115 New Cavendish Street London W1M 8JS UK. ------------------------------------------------------------- READING The Wolpert and Brown references are suitable for those who do not have a strong background in biology. I would describe Langton's two Artificial Life volumes as essential reading. Artificial Life II contains several papers exploring evolving artificial neural networks. Beer's book is so new I have not been able to look at a copy yet. Title: Biological neural networks in invertebrate neuroethology and robotics / edited by Randall D. Beer, Roy E. Ritzmann, Thomas McKenna. Publication Info: Boston : Academic Press, c1993. Phys. Description: xi, 417 p. : ill. ; 24 cm. Series Name: Neural networks, foundations to applications Subjects: Neural circuitry. Subjects: Invertebrates--Physiology. Subjects: Neural networks (Computer science) ISBN: 0-12-084728-0 Author: Wolpert, L. (Lewis) Title: The triumph of the embryo / Lewis Wolpert ; with illustrations drawn by Debra Skinner. Publication Info: Oxford [England] ; New York : Oxford University Press, 1991. Phys. Description: vii, 211 p. : ill. ; 25 cm. Subjects: Embryology, Human--Popular works. ISBN: 0-19-854243-7 : $22.95 Author: Brown, M. C. (Michael Charles) Title: Essentials of neural development / M.C. Brown, W.G. Hopkins, and R.J. Keynes. Publication Info: Cambridge ; New York : Cambridge University Press, c1991. Phys. Description: x, 176 p. : ill. ; 24 cm. Notes: Rev. ed. of: Development of nerve cells and their connections / by W.G. Hopkins and M.C. Brown. 1984. Subjects: Developmental neurology. Subjects: Nerves. Subjects: Neurons. Subjects: Nerve endings. Subjects: Neurons. ISBN: 0-521-37556-8 ISBN: 0-521-37698-X (pbk.) Title: Artificial life II : the proceedings of an interdisciplinary workshop on the synthesis and simulation of living systems held 1990 in Los Alamos, New Mexico / edited by Christopher G. Langton ... [et al.]. Publication Info: Redwood City, Calif. : Addison-Wesley, 1991. Series Name: Santa Fe Institute studies in the sciences of complexity proceedings ; v. 10 Notes: Proceedings of the Second Artificial Life Workshop. Subjects: Biological systems--Computer simulation--Congresses. Subjects: Biological systems--Simulation methods--Congresses. ISBN: 0-201-52570-4 ISBN: 0-201-52571-2 (pbk.) Author: Interdisciplinary Workshop on the Synthesis and Simulation of Living Systems (1987 : (Los Alamos, N.M.) Title: Artificial life : the proceedings of an Interdisciplinary Workshop on the Synthesis and Simulation of Living Systems, held September, 1987, in Los Alamos, New Mexico / Christopher G. Langton, editor. Publication Info: Redwood City, Calif. : Addison-Wesley Pub. Co., Advanced Book Program, c1989. Phys. Description: xxix, 655 p., [10] p. of plates : ill. (some col.) ; 25 cm. Series Name: Santa Fe Institute studies in the sciences of complexity ; v. 6 ISBN: 0-201-09346-4 ISBN: 0-201-09356-1 (pbk.) ---------------------------------------------------------------------- From: Jari Vaario I have been working already awhile to create a method to describe dynamical neural networks as you describe above. The latest journal papers of mine are Jari Vaario and Setsuo Ohsuga: An Emergent Construction of Adaptive Neural Architectures, Heuristics - The Journal of Knowledge Engineering, vol. 5, No 2, 1992. Abstract: In this paper a modeling method for an emergent construction of neural network architectures is proposed. The structure and behavior of neural networks are an emergent of small construction and behavior rules, that in cooperation with extrinsic signals, define the gradual growth of the neural network structure and its adaptation capability (learning behavior). The inspiration for the work is taken directly from biological systems, even though the simulation itself is not an exact biological simulation. The example used to demonstrate the modeling method is also from biological context: the sea hare Aplysia (a mollusk). Jari Vaario, Koichi Hori and Setsuo Ohsuga: Toward Evolutionary Design of Autonomous Systems, The International Journal in Computer Simulation, A Special Issue on High Autonomous Systems, to be appear 1993. Abstract: An evolutionary method for designing autonomous systems is proposed. The research is a computer exploration on how the global behavior of autonomous systems can emerge from neural circuits. The evolutionary approach is used to increase the repertoire of behaviors. Autonomous systems are viewed as organisms in an environment. Each organism has its own set of production rules, a genetic code, that gives birth to the neural structure. Another set of production rules describe the environmental factors. These production rules together give rise to a neural network embedded in the organism model. The neural network is the only means to direct reproduction. This gives rise to intelligence, organisms which have ``more'' intelligent methods to reproduce will have a relative advantage for survival. Jari Vaario Research Center for Advanced Science and Technology University of Tokyo, Japan JARI HAS OFFERED TO PROVIDE COPIES OF HIS PAPERS TO READERS OF THIS BULLETIN. PLEASE CONTACT HIM BY EMAIL FOR FURTHER DETAILS. --------------------------------------------------------------------- From: "John R. Mcdonnell" I have been developing networks which "evolve" structure as well as the connection strengths. This is done using an evolutionary programming paradigm as a mechanism for stochastic search. One thing I have noticed is that particular structures tend to dominate the population (EP is a multi-agent stochastic search technique). THis has caused me to backtrack a little and investigate the search space for simultaneously determining model structure and parameters. Nevertheless, I have been able to "evolve" networks for simple binary mappings such as XOR, 3-bit parity, and the T-C problem. In the future I am aiming at more general (feedforward) networks which do not have layers per se, but neuron class {input, hidden, output}. Self-organizing of sub-groups of neurons would be an interesting phenomenon to observe for, say, image recognition problems. I think that to accomplish this a distance metric between neurons might be necessary. Fahlman's cascade-correlation architecture is very interesting. However, it has the constraint that the neurons be fully connected to subsequent neurons in the network. This might not be detrimental in that unimportant connections could have very small weights. From an information standpoint, these free parameters should be included in a cost function. I do like his approach in adding additional hidden nodes. As one last comment, when I "evolve" (I use the term loosely) networks for the XOR mapping with an additional input of U(0,1) noise, this third (noisy) input node has all of its outputs disconnected. This was a nice result since inputs which contain no information can be disconnected. John McDonnell NCCOSC, RDT&E Division Code 731 Information & Signal Processing Dept. San Diego, CA 92152-5000 ----------------------------------------------------------------------- From: "Larry D. Pyeatt" I have just completed some code to allow modelling of DANN's. It allows PE's to be created, destroyed, and reconnected at any time........ I have been using genetic algorithms to construct networks with desired properties. Encoding the genes is a major problem....... I have been thinking that it would be interesting to try to evolve or create a simple "creature" which lives in a computer simulated world. The "creature" would have a small set of inputs and responses with which to interact with the simulated world. Once the "creature" has evolved sufficiently, you could make its world richer and give it more neurons. Eventually, you might have a respectably complex organism. Larry D. Pyeatt The views expressed here are not Internet : pyeatt@texaco.com those of my employer or of anyone Voice : (713) 975-4056 that I know of with the possible exception of myself. ----------------------------------------------------------------------- From: Bernie French I noticed your Usenet post on the use of simple organisms as a model to produce a biologically plausible DANN. One organism that would seem to fit your need for producing a DANN is the nematode, Caenorhabditis elegans. The positions of neuronal processes as well as the neuronal connectivity has been extensively mapped in this organism. The development in terms of cellular fates is also well studied for the nervous system. Integration of neuronal subsystems into the neuronal processes during development have been studied. This would fit your description of a DANN where processing elements are created as part of a development phase. Further, the two sexes of C. elegans (male and hermaphrodite) have different numbers of total neurons as adults, 302 for the hermaphrodite. Howver, during development of the neuronal processes there is no difference between the two sexes. During a certain stage in development there is the production of sex-specific neurons. This sex-specificity occurs by a process of programmed cell death, in which certain neurons are "programmed" to die. This fits your description of a DANN where processing elements are annihilated as part of a development phase. This organism also provides some spatial information, since some neuronal cells undergo migration within the organism. Disruption of this migration results in synaptic differences in the neuronal connectivity, with corresponding differences in the organism response to external stimuli. A good starting reference, if your interested in looking at this organism as a model, is "The Nematode Caenorhabditis Elegans". The book is edited by William B. Wood and published by Cold Spring Harbor Laboratory. -- Bernie (btf64@cas.org) ------------------------------------------------------------------------- From: "Mark J. Crosbie" As part of a project which I am working on for my degree, I am studying how to evolve machines for solving simple problems. I saw that Cellular Automata were able to evolve colonies of cells and control how these cells lived or died, but I felt that this was not a powerful enough representation of cells to be of use. I have combined the CA approach with a Genetic Algorithm approach within each CA to give me colonies of evolving cells which can modify their behaviour over time. Each cell can be pre-programmed to perform a certain task (an adder or multiplexor say) and the first hurdle in the project is getting these cells to grow together and interconnect properly. I think that study of how cells grow and interconnect will lead to not only a better understanding of the nervous systems of living organisms, but also of how to solve problems using these "Genetic Programming" techniques. I feel that this idea overlaps somewhat with your DANN which you described in comp.theory.cell-automata. Do you agree? Would you agree that building simple machines by genetic means would be a better starting point for experimentation than trying to simulate a complex nervous system? Given enough of these cells and a complex enough interconnection system, do you feel that a system will evolve which will equal a nervous system in functionality and intelligence? Mark Crosbie mcrosbie@vax1.tcd.ie Dept. of Computer Science Trinity College, Dublin Dublin 2 Eire. -------------------------------------------------------------------- From: "Mark W. Tilden" Forcing a DANN, as you call it, through simple topological structures with recursive sub-elements does tend towards complexity befitting a functional organism with surprisingly few components. In my lab I have a variety of robotic devices with adaptive nervous systems not exceeding the equivalent of 10 neurons. These devices not only learn to walk from first principles but can also adapt to many different circumstances including severe personal damage. There are no processors involved; my most complex device uses only 50 transistors for its entire spectrum of behavior, response and control. My point is that there are simple, elegant solutions to "constructing a working DANN". More so than you might expect. I'm sorry I have no papers to quote as I am awaiting patents, but I will be at the Brussels Alife show in May with some of my devices, or you can check out an article on me in the September 92 issue of Scientific American. Mark W. Tilden M.F.C.F Hardware Design Lab. U of Waterloo. Ont. Can, N2L-3G1 (519)885-1211 ext. 2454 ----------------------------------------------------------------------- From: Stanley Zietz 05 Jan 93 You may not need to model simple organisms, but simple structures. Since you quote Muriel Ross's paper, you probably know that the Biocomputation Center at NASA-Ames is exhaustively studying the linear accelerometer in the inner ear as a prototypical system to make 'real' biological neural networks. 07 Jan 93 To paraphrase many of the papers, it has been shown that the geometry of the connection of the hair cells (before the Spike initiation zone) is critical to the summation of events, and indeed there is a lot of variability (perhaps throwing in a stochastic element into the network). Also, as Muriel reported at headquarters, the results of analyzing the space flight animals have shown that the number of synapses is very plastic in microgravity - the number of synapses increase in space (and decrease if you put the animals in hypergravity on a centrifuge). Therefore the synapses (and presumably the electrical conduction in the network) is responding to the input. Such self adaption is probably very important in biological communications systems (Steve Grossberg has expounded such a concept in 1979). We already know that such environment driven events are important in development. Dr. Stanley Zietz email szietz@mcs.drexel.edu Assoc. Director - Biomed. Eng. and Sci. Inst. tel (215) - 895-2681 Assoc. Prof. - Mathematics and Computer Sciences Fax (215) - 895-4983 Drexel University - 32nd and Chestnut Sts. Phila., PA 19104 USA also Biocomputation Center NASA-Ames Research Center -------------------------------------------------------------------------- From: Bill Saidel The question that remains [see ref. 1 in my post, also the Zietz reply] is whether a hard wired (where hard is a relative term, relative to say cortex in the cns) set of connections from a peripheral sensory receptor to the peripheral afferent fiber and then to the brain comprises a "net" in the same sense as neural net is used. An analogous example would be from cones to bipolar (and NOT to ganglion cell). My omission of ganglion cell is simply that ganglion cell in the serial ordering of processing would be equivalent to the first order vestibular neuron in the vestibular nuclei of the hindbrain. This series of connections seems to qualify as a genetically-determined (and I am probably overconstraining the use of determined) set of connections (receptor to next layer). I know of no learning treatment that changes this layer of connections. However, manipulating the sensory input in the retina by strobing does produce strange deficits in frog and cat velocity detection (and other features). The Ross argument has depended on looking at the EM level of connections. These connections are all at the level of the sensory periphery and so they are under normal circumstances, not manipulable. Nets in the cortex are manipulable as are nnets in computer simulations. However, Ross has also been involved in an intriguing study of the structure of synaptic connections in rats or mice that were born in space (I think) and found that the synaptic connections are fewer when gravity is missing or diminished. I prefer to think of that manipulation of the nervous periphery more as an example of experimental epistomology because the change occurred due to changes inthe biophysical structure of experience. Again, let me point out that at the periphery the neuronal processing is driven by biophysics. In the cns, neuronal processing is driven by preceeding nerve cells that know nothing about the outside world. Perhaps, this distinction is the one that I use to distinguish between nets and constructions (a possibly artificial distinction but to my mind, useful one). Bill Saidel Dept. of Biology (609) 225-6336 (phone)Rutgers University Camden, NJ 08102 saidel@clam.rutgers.edu (email) ---------------------------------------------------------------------------- EDITED REPRINT OF NEWSPOST WITH ADDITIONAL REFERENCE From: Scott E. Fahlman I would just point out that adding or subtracting neurons from the functional net does not necessarily correspond to adding, destroying, or moving any physical neurons. If a physical neuron (or functional group of neurons) has a lot of excess connections, invisible changes in the synapses can effectively wire it into the net in a large number of different ways, or effectively remove it. Something like my dynamic (additive) Cascade-Correlation model can be implemented by a sort of phase change, rather than a re-wiring of the net: A candidate unit has a lot of trainable inputs, but it produces no output (or all the potential recipients ignore its output). After a unit is tenured, a specific pattern of input weights is frozen in, but now the neuron does produce effective outputs. I don't know if such a phase transition has been observed in biological neurons -- it would be interesting to find out. Note that what I'm calling a "unit" might correspond to a group of biological neurons rather than a single one. Scott E. Fahlman Internet: sef+@cs.cmu.edu Senior Research Scientist Phone: 412 268-2575 School of Computer Science Fax: 412 681-5739 Carnegie Mellon University Latitude: 40:26:33 N 5000 Forbes Avenue Longitude: 79:56:48 W Pittsburgh, PA 15213 REFERENCE LEARNING WITH LIMITED NUMERICAL PRECISION USING THE CASCADE-CORRELATION ALGORITHM HOEHFELD M, FAHLMAN SE IEEE TRANSACTIONS ON NEURAL NETWORKS 1992 VOL.3 NO.4 PP.602-611 ------------------------------------------------------------------------ From: Paul Verschure The work we're doing would fit pretty well in your approach. Let me give you some references: Verschure, P.F.M.J., Krose, B, Pfeifer, R.(1992) Distributed Adaptive Control: The self-organization of structured behavior. Robotics and Autonomous Systems, 9, 181-196. (Control architectures for Autonomous agents based on self-organizing model for classical conditioning). Verschure, P.F.M.J., & Coolen, T. (1991) Adaptive Fields: Distributed representations of clasically conditioned associations. Network, 2, 189-206. (Two neural models for reinforcement learning which do NOT rely on supervised learning and local representations and incorporate some general properties of neural functioning, the models are analyzed using techniques from statistical physics and not with simulations) Verschure, P.F.M.J. (1992) Taking connectionism seriously: The vague promis of subsymbolism and an alternative. In Proc. 14th Ann. Conf. of the Cog. Sci. Soc., 653-658, Hillsdale, N.J.: Erlbaum. Paul Verschure AI lab Department of Computer Science University Zurich-Irchel Tel + 41 - 1 - 257 43 06 Winterthurerstrasse 190 Fax + 41 - 1 - 363 00 35 CH - 8057 Zurich, Switzerland verschur@ifi.unizh.ch ------------------------------------------------------------------ For after all what is man in nature? A nothing in relation to infinity, all in relation to nothing, a central point between nothing and all, and infinitely far from understanding either. The ends of things and their beginnings are impregnably concealed from him in an impenetrable secret. He is equally incapable of seeing the nothingness out of which he was drawn and the infinite in which he is engulfed. Blaise Pascal (1623-1662) -------------------------------------------------------------------------- Paul Fawcett | Internet: paulf@manor.demon.co.uk London, UK. | tenec@westminster.ac.uk -------------------------------------------------------------------------- ------------------------------ End of Neuron Digest [Volume 11 Issue 13] ***************************************** Received: from BUACCA by BUACCA.BU.EDU (Mailer R2.08 PTF009) with BSMTP id 5442; Mon, 01 Mar 93 18:50:17 EST Received: from noc2.dccs.upenn.edu by BUACCA.BU.EDU (IBM VM SMTP R1.2.1) with TCP; Mon, 01 Mar 93 18:50:13 EST Received: from CATTELL.PSYCH.UPENN.EDU by noc2.dccs.upenn.edu id AA01999; Mon, 1 Mar 93 18:43:34 -0500 Return-Path: Received: from LOCALHOST by cattell.psych.upenn.edu id AA01365; Fri, 26 Feb 93 02:39:52 EST Posted-Date: Fri, 26 Feb 93 02:39:14 EST From: "Neuron-Digest Moderator" To: Neuron-Distribution:; Subject: Neuron Digest V11 #14 (discussion + jobs) Reply-To: "Neuron-Request" X-Errors-To: "Neuron-Request" Organization: University of Pennsylvania Date: Fri, 26 Feb 93 02:39:14 EST Message-Id: <1359.730712354@cattell.psych.upenn.edu> Sender: marvit@cattell.psych.upenn.edu Neuron Digest Friday, 26 Feb 1993 Volume 11 : Issue 14 Today's Topics: neural net applications to fixed-income security markets connectionist models summer school -- final call for applications RE: Neuron Digest V11 #8 (discussion + reviews + jobs) Re: Speaker normalization and adaptation A computationally efficient squashing function BP network paralysis Re: pattern recognition (pratical database considerations) ? Re: pattern recognition (pratical database considerations) Computational Biology Degree Programs postdoctoral traineeships available Postdoc position in computational/biological vision (learning) Position for Programmer/Analyst with Neural Networks (YALE) Send submissions, questions, address maintenance, and requests for old issues to "neuron-request@cattell.psych.upenn.edu". The ftp archives are available from cattell.psych.upenn.edu (130.91.68.31). Back issues requested by mail will eventually be sent, but may take a while. ---------------------------------------------------------------------- Subject: neural net applications to fixed-income security markets From: danlap@internet.sbi.com (Dan LaPushin) Date: Mon, 08 Feb 93 18:26:43 -0500 [[ Editor's Note: Once again, neural nets has reached Wall Street, but this time from quite a different angle. A cursory search in past issues of the Digest turned up nothing of relevance. Perhaps one of our faithful readers might either lend a helpful ear or might get interested in this as a new project via email! -PM ]] To The Editor, I am new to the field of neural networks but have a strong background in mathematics, economics, and some computer programming. I work at a large Wall St. firm and am interested in applying neural network technology to the field of fixed-income research. Such instruments include bonds, mortgage-backed securities and the like. There seems to be, as far as I can tell, little research into neural net application to such markets. I suspect this is because the data is hard to come by for those not in the field, but I'm not sure. Could you direct me to any research in this area so that I don't inadvertently recreate the wheel? Thanks for your help! Dan LaPushin I'm on your mailing list as danlap@sp_server.sbi.com ------------------------------ Subject: connectionist models summer school -- final call for applications From: "Michael C. Mozer" Date: Thu, 11 Feb 93 22:10:05 -0700 FINAL CALL FOR APPLICATIONS CONNECTIONIST MODELS SUMMER SCHOOL The University of Colorado will host the 1993 Connectionist Models Summer School from June 21 to July 3, 1993. The purpose of the summer school is to provide training to promising young researchers in connectionism (neural networks) by leaders of the field and to foster interdisciplinary collaboration. This will be the fourth such program in a series that was held at Carnegie-Mellon in 1986 and 1988 and at UC San Diego in 1990. Previous summer schools have been extremely successful and we look forward to the 1993 session with anticipation of another exciting event. The summer school will offer courses in many areas of connectionist modeling, with emphasis on artificial intelligence, cognitive neuroscience, cognitive science, computational methods, and theoretical foundations. Visiting faculty (see list of invited faculty below) will present daily lectures and tutorials, coordinate informal workshops, and lead small discussion groups. The summer school schedule is designed to allow for significant interaction among students and faculty. As in previous years, a proceedings of the summer school will be published. Applications will be considered only from graduate students currently enrolled in Ph.D. programs. About 50 students will be accepted. Admission is on a competitive basis. Tuition will be covered for all students, and we expect to have scholarships available to subsidize housing and meal costs, but students are responsible for their own travel arrangements. Applications should include the following materials: * a vita, including mailing address, phone number, electronic mail address, academic history, list of publications (if any), and relevant courses taken with instructors' names and grades received; * a one-page statement of purpose, explaining major areas of interest and prior background in connectionist modeling and neural networks; * two letters of recommendation from individuals familiar with the applicants' work (either mailed separately or in sealed envelopes); and * a statement from the applicant describing potential sources of financial support available (department, advisor, etc.) for travel expenses. Applications should be sent to: Connectionist Models Summer School c/o Institute of Cognitive Science Campus Box 344 University of Colorado Boulder, CO 80309 All application materials must be received by March 1, 1993. Admission decisions will be announced around April 15. If you have specific questions, please write to the address above or send e-mail to "cmss@cs.colorado.edu". Application materials cannot be accepted via e-mail. Organizing Committee Jeff Elman (UC San Diego) Mike Mozer (University of Colorado) Paul Smolensky (University of Colorado) Dave Touretzky (Carnegie Mellon) Andreas Weigend (Xerox PARC and University of Colorado) Additional faculty will include: Yaser Abu-Mostafa (Cal Tech) Sue Becker (McMaster University) Andy Barto (University of Massachusetts, Amherst) Jack Cowan (University of Chicago) Peter Dayan (Salk Institute) Mary Hare (Birkbeck College) Cathy Harris (Boston University) David Haussler (UC Santa Cruz) Geoff Hinton (University of Toronto) Mike Jordan (MIT) John Kruschke (Indiana University) Jay McClelland (Carnegie Mellon) Ennio Mingolla (Boston University) Steve Nowlan (Salk Institute) Dave Plaut (Carnegie Mellon) Jordan Pollack (Ohio State) Dean Pomerleau (Carnegie Mellon) Dave Rumelhart (Stanford) Patrice Simard (ATT Bell Labs) Terry Sejnowski (UC San Diego and Salk Institute) Sara Solla (ATT Bell Labs) Janet Wiles (University of Queensland) The Summer School is sponsored by the American Association for Artificial Intelligence, the National Science Foundation, Siemens Research Center, and the University of Colorado Institute of Cognitive Science. Colorado has recently passed a law explicitly denying protection for lesbians, gays, and bisexuals. However, the Summer School does not discriminate in admissions on the basis of age, sex, race, national origin, religion, disability, veteran status, or sexual orientation. ------------------------------ Subject: RE: Neuron Digest V11 #8 (discussion + reviews + jobs) From: rkeller@academic.cc.colorado.edu Date: Sun, 14 Feb 93 12:18:03 -0700 James L. McClelland and David E. Rumelhart provide code for a Boltzmann Machine in "Explorations in Parallel Distributed Processing: A Handbook of Models, Programs and Exercises" The code is written for a PC. This is A Bradford Book available from The MIT Press and was designed to provide excersises to accompany their two volume book "Parallel Distributed Processing." ------------------------------ Subject: Re: Speaker normalization and adaptation From: Yaakov Stein Date: Wed, 17 Feb 93 07:51:07 +0200 Nico Weymaere (WEYMAERE@lem.rug.ac.be) asked for references on speaker normalization and adaptation. While the idea of exploiting the self organization and learning capabilities of neural networks for this task seems quite natural, I have not seen much in the proceedings of NN conferences. The related questions of speaker independent recognition and speaker identification / verification have been far more thoroughly treated in this literature. In the speech and signal processing conferences more has appeared. A quick search through my article file turned up the following : Bridle JS and Cox SJ, RecNorm: Simultaneous Normalization and Classification Applied to Speech Recognition, NIPS-3, 234-40 (1991) Cox SJ and Bridle JS, Simultaneous Speaker Normalization and Utterance Labelling Using Bayesian/Neural Net Techniques, ICASSP-90 article S3.8, vol 1, 161-4 (1990) Hampshire JB II and Waibel AH, The Meta-Pi Network: Connectionist Rapid Adaptation for High Performace Multi-Speaker Phoneme Recognition, ICASSP-90 article S3.9, vol 1, 165-8 (1990) Fukuzawa K and Komori Y, A Segment-based Speaker Adaptation Neural Network Applied to Continuous Speech Recognition, ICASSP-92, I 433-6 (1992) Huang X, Speaker Normalization for Speech Recognition, ICASSP-92, I 465-8 (1992) Iso K, Asogawa M, Yoshida K and Watanabe T, Speaker Adaptation Using Neural Network, Proc. Spring Meeting of Acoust. Soc. of Japan, I 6-16 (March 1989) [in Japanese, quoted widely but I don't have it] Konig Y and Morgan N, GDNN: A Gender-Dependent Neural Network for Continuous Speech Recognition, IJCNN-92(Baltimore), II 332-7 (1992) Montacie C, Choukri K and Chollet G, Speech Recognition using Temporal Decomposition and Multilayer Feedforward Automata, ICASSP-89 article S8.6, vol 1, 409-12 (1989) Nakamura S and Akabane T, A Neural Speaker Model for Speaker Clustering, ICASSP-91 article S13.6, vol 2, 853-856 (1991) Nakamura S and Shikano K, Speaker Adaptation Applied to HMM and Neural Networks, ICASSP-89 article S3.3, vol 1, 89-92 (1989) Nakamura S and Shikano K, A Comparative Study of Spectral Mapping for Speaker Adaptation, ICASSP-90 article S3.7, vol 1, 157-160 (1990) Schmidbauer O and Tebelskis J, An LVQ Based Reference Model for Speaker Adaptative Speech Recognition, ICASSP-92, I 441-4 (1992) Witbrock M and Hoffman P, Rapid Connectionist Speaker Adaptation, ICASSP-92, pp. I 453-6 (1992) Hope this helps. Yaakov Stein ------------------------------ Subject: A computationally efficient squashing function From: "Michael P. Perrone" Date: Thu, 18 Feb 93 15:42:34 -0500 Recently on the comp.ai.neural-nets bboard, there has been a discussion of more computationally efficient squashing functions. Some colleagues of mine suggested that many members of this mailing list may not have access to the comp.ai.neural-nets bboard; so I have included a summary below. Michael - ------------------------------------------------------ David L. Elliot mentioned using the following neuron activation function: x f(x) = ------- 1 + |x| He argues that this function has the same qualitative properties of the hyperbolic tangent function but in practice faster to calculate. I have suggested a similar speed-up for radial basis function networks: 1 f(x) = ------- 1 + x^2 which avoids the transcendental calculation associated with gaussian RBF nets. I have run simulations using the above squashing function in various backprop networks. The performance is comparable (sometimes worse sometimes better) to usual training using hyperbolic tangents. I also found that the performance of networks varied very little when the activation functions were switched (i.e. two networks with identical weights but different activation functions will have comparable performance on the same data). I tested these results on two databases: the NIST OCR database (preprocessed by Nestor Inc.) and the Turk and Pentland human face database. - ------------------------------------------------------------------------------ -- Michael P. Perrone Email: mpp@cns.brown.edu Institute for Brain and Neural Systems Tel: 401-863-3920 Brown University Fax: 401-863-3934 Providence, RI 02912 ------------------------------ Subject: BP network paralysis From: slablee@mines.u-nancy.fr Date: Fri, 19 Feb 93 13:55:35 +0700 Dear Netters, My english is a bit frenchy (!) so please excuse some poor sentences ! I'm trying to use NN to detect the start of a sampled signal. I'm using a 7520 x 150 x 70 x 1 BackPropagation network. My problem is : whatever could be the parameters I choose (learning rate, momentum...) the Network stop learning with a rather high error (about 0.4 for each unit, into a [0,1] range ! ). I thought of two problems : - network "paralysis" (as describe by Rumelhart) involved by too high weight (which leads to activations near 0 or 1, preventing the weights from being changed : the changes are proportional to a(1-a) ). But the weights of my network always have average values... - some local minima. But a great value for the learning rate seems to change nothing to it. I've tried to add a noise in the input units, whithout any success. I've also tried to change the number of hidden units, but the local minima are always here, even if lower. Who could help me to escape from this problem ? -------------------------------------------------- | Stephane Lablee | | ***** | | Ecole des Mines de Nancy | | Parc de Saurupt | | 54042 Nancy Cedex | | France | | ***** | | E-mail : slablee@mines.u-nancy.fr | -------------------------------------------------- - -- ------------------------------ Subject: Re: pattern recognition (pratical database considerations) ? From: gray@itd.nrl.navy.mil (Jim Gray) Date: Fri, 19 Feb 93 09:33:19 -0500 Duane A. White writes: > I am interested in pattern recognition. In my particular application I > would like to compare a 2D monochrome bitmap image with those in a > database. The program should determine if there is a match, and if not > then add it to the database. Most of the literature I've read on pattern > matching networks use a relatively small set of classification patterns > (such as letters of the alphabet, numbers). In my case it wouldn't seem > practical to train a single network to identify every entry in the > database (on the order of hundreds or thousands of entries). Is there > something fundemental in the approach that I'm missing? You might try looking at Adaptive Resonance Theory (ART). A good place to start is the book: Carpenter and Grossberg, eds., Pattern Recognition by Self-Organizing Neural Networks, The MIT Press, Cambridge, MA (1991) ISBN 0-262-03176-0 I'm not sure whether ART networks can be applied to "thousands of entries" in practice, but the basic operation is as you describe: the network determines if there is a match, and if not, then adds it to the database. > Also the program should to a small degree be rotation and translation > invariant. I'm not sure whether ART networks have been applied to this type of problem, but you might try looking at: Hinton, "A Parallel Computation that assigns Canonical Object-Based Frames of Reference", in Proceedings of the International Joint Conference on Artificial Intelligence, 1981, pp. 683-685. Jim Gray. ------------------------------ Subject: Re: pattern recognition (pratical database considerations) From: shsbishp@reading.ac.uk Date: Tue, 23 Feb 93 11:26:45 +0000 >I am interested in pattern recognition. In my particular application I >would like to compare a 2D monochrome bitmap image with those in a >database. The program should determine if there is a match, and if not >then add it to the database. Most of the literature I've read on pattern >matching networks use a relatively small set of classification patterns >(such as letters of the alphabet, numbers). In my case it wouldn't seem >practical to train a single network to identify every entry in the >database (on the order of hundreds or thousands of entries). Is there >something fundemental in the approach that I'm missing? > >Also the program should to a small degree be rotation and translation >invariant. Having just perused todays neural digest (Vol.11; No. 12), I noticed the above plea for help. Having been unable to email the sender direct, I enclose the following information for the list. As part of my doctoral research I developed a neural architecture (The Stochastic Search Network) for use on this type of problem - Anarchic Techniques for Pattern Classification, PhD thesis 1989, University of Reading, UK. A recent reference on this work is; Bishop, J.M. & Torr, P., in Lingard, R., Myers, D.J. & Nightingale, C. (eds), Neural Networks for Vision, Speech & Natural Language, Chapman Hall, pp: 370-388. For further information please email to (shsbishp@uk.ac.rdg) or write to Dr. J.M.Bishop, Department of Cybernetics, University of Reading, Berkshire, UK. ------------------------------ Subject: Computational Biology Degree Programs From: georgep@rice.edu (George Phillips) Organization: Rice University Date: 05 Feb 93 15:38:31 +0000 The W.M. Keck Center for Computational Biology offers studies in Computational Biology through three partner institutions: Rice University, Baylor College of Medicine, and the University of Houston. Science and engineering are in the process of being transformed by the power of new computing technologies. Our goal is to train a new kind of scientist--one poised to seize the advantages of a national computational prowess in solving important problems in biology. The program emphasizes algorithm development, computation, and visualization in biology, biochemistry and biophysics. The Program draws on the intellectual and technologic resources of The Center for Research on Parallel Computation at Rice, the Human Genome Center at Baylor College of Medicine, and the Institute for Molecular Design at the University of Houston, among others. The research groups involved in the W.M. Keck Center for Computational Biology are at the forefronts of their respective areas, and their laboratories are outstanding settings for the program. A list of participating faculty and application information can be obtained by sending email to georgep@rice.edu. ======================================+======================================= Prof. George N. Phillips, Jr., Ph.D. | InterNet: georgep@rice.edu Dept. of Biochemistry and Cell Biology| Rice University, P.O. Box 1892 | Phone: (713) 527-4910 Houston, Texas 77251 | Fax: (713) 285-5154 ------------------------------ Subject: postdoctoral traineeships available From: "John K. Kruschke" Date: Tue, 09 Feb 93 09:45:45 -0500 POST-DOCTORAL FELLOWSHIPS AT INDIANA UNIVERSITY Postdoctoral Traineeships in MODELING OF COGNITIVE PROCESSES Please call this notice to the attention of all interested parties. The Psychology Department and Cognitive Science Programs at Indiana University are pleased to announce the availability of one or more Postdoctoral Traineeships in the area of Modeling of Cognitive Processes. The appointment will pay rates appropriate for a new PhD (about $18,800), and will be for one year, starting after July 1, 1993. The duration could be extended to two years if a training grant from NIH is funded as anticipated (we should receive final notification by May 1). Post-docs are offered to qualified individuals who wish to further their training in mathematical modeling or computer simulation modeling, in any substantive area of cognitive psychology or Cognitive Science. We are particularly interested in applicants with strong mathematical, scientific, and research credentials. Indiana University has superb computational and research facilities, and faculty with outstanding credentials in this area of research, including Richard Shiffrin and James Townsend, co-directors of the training program, and Robert Nosofsky, Donald Robinson, John Castellan, John Kruschke, Robert Goldstone, Geoffrey Bingham, and Robert Port. Trainees will be expected to carry out original theoretical and empirical research in association with one or more of these faculty and their laboratories, and to interact with other relevant faculty and the other pre- and postdoctoral trainees. Interested applicants should send an up to date vitae, personal letter describing their specific research interests, relevant background, goals, and career plans, and reference letters from two individuals. Relevant reprints and preprints should also be sent. Women, minority group members, and handicapped individuals are urged to apply. PLEASE NOTE: The conditions of our anticipated grant restrict awards to US citizens, or current green card holders. Awards will also have a 'payback' provision, generally requiring awardees to carry out research or teach for an equivalent period after termination of the traineeship. Send all materials to: Professors Richard Shiffrin and James Townsend, Program Directors Department of Psychology, Room 376B Indiana University Bloomington, IN 47405 We may be contacted at: 812-855-2722; Fax: 812-855-4691 email: shiffrin@ucs.indiana.edu Indiana University is an Affirmative Action Employer ------------------------------ Subject: Postdoc position in computational/biological vision (learning) From: "John G. Harris" Date: Tue, 16 Feb 93 18:50:28 -0500 One (or possibly two) postdoctoral positions are available for one or two years in computational vision starting September 1993 (flexible). The postdoc will work in Lucia Vaina's laboratory at Boston University, College of Engineering, to conduct research in learning the direction in global motion. The researchers currently involved in this project are Lucia M. Vaina, John Harris, Charlie Chubb, Bob Sekuler, and Federico Girosi. Requirements are PhD in CS or related area with experience in visual modeling or psychophysics. Knowledge of biologically relevant neural models is desirable. Stipend ranges from $28,000 to $35,000 depending upon qualifications. Deadline for application is March 1, 1993. Two letter of recommendation, description of current research and an up to date CV are required. In the research we combine computational psychophysics, neural networks modeling and analog VLSI to study visual learning specifically applied to direction in global motion. The global motion problem requires estimation of the direction and magnitude of coherent motion in the presence of noise. We are proposing a set of psychophysical experiments in which the subject, or the network must integrate noisy, spatially local motion information from across the visual field in order to generate a response. We will study the classes of neural networks which best approximate the pattern of learning demonstrated in psychophysical tasks. We will explore Hebbian learning, multilayer perceptrons (e.g. backpropagation), cooperative networks, Radial Basis Function and Hyper-Basis Functions. The various strategies and their implementation will be evaluated on the basis of their performance and their biological plausibility. For more details, contact Prof. Lucia M. Vaina at vaina@buenga.bu.edu or lmv@ai.mit.edu. ------------------------------ Subject: Position for Programmer/Analyst with Neural Networks (YALE) From: Anand Rangarajan Date: Thu, 18 Feb 93 13:18:40 -0500 Programmer/Analyst Position in Artificial Neural Networks The Yale Center for Theoretical and Applied Neuroscience (CTAN) and the Department of Computer Science Yale University, New Haven, CT We are offering a challenging position in software engineering in support of new techniques in image processing and computer vision using artificial neural networks (ANNs). 1. Basic Function: Designer and programmer for computer vision and neural network software at CTAN and the Computer Science department. 2. Major duties: (a) To implement computer vision algorithms using a Khoros (or similar) type of environment. (b) Use the aforementioned tools and environment to run and analyze computer experiments in specific image processing and vision application areas. (c) To facilitate the improvement of neural network algorithms and architectures for vision and image processing. 3. Position Specifications: (a) Education: BA, including linear algebra, differential equations, calculus. helpful: mathematical optimization. (b) Experience: programming experience in C++ (or C) under UNIX. some of the following: neural networks, vision or image processing applications, scientific computing, workstation graphics, image processing environments, parallel computing, computer algebra and object-oriented design. Preferred starting date: March 1, 1993. For information or to submit an application, please write: Eric Mjolsness Department of Computer Science Yale University P. O. Box 2158 Yale Station New Haven, CT 06520-2158 e-mail: mjolsness-eric@cs.yale.edu Any application must also be submitted to: Jeffrey Drexler Department of Human Resources Yale University 155 Whitney Ave. New Haven, CT 06520 - -Eric Mjolsness and Anand Rangarajan (prospective supervisors) ------------------------------ End of Neuron Digest [Volume 11 Issue 14] ***************************************** Received: from BUACCA by BUACCA.BU.EDU (Mailer R2.08 PTF009) with BSMTP id 5802; Sat, 27 Feb 93 18:47:54 EST Received: from noc2.dccs.upenn.edu by BUACCA.BU.EDU (IBM VM SMTP R1.2.1) with TCP; Sat, 27 Feb 93 18:47:51 EST Received: from CATTELL.PSYCH.UPENN.EDU by noc2.dccs.upenn.edu id AA04341; Sat, 27 Feb 93 18:42:56 -0500 Return-Path: Received: from LOCALHOST by cattell.psych.upenn.edu id AA09209; Sat, 27 Feb 93 17:09:40 EST Posted-Date: Sat, 27 Feb 93 17:09:10 EST From: "Neuron-Digest Moderator" To: Neuron-Distribution:; Subject: Neuron Digest V11 #15 (discussion, software, & jobs) Reply-To: "Neuron-Request" X-Errors-To: "Neuron-Request" Organization: University of Pennsylvania Date: Sat, 27 Feb 93 17:09:10 EST Message-Id: <9204.730850950@cattell.psych.upenn.edu> Sender: marvit@cattell.psych.upenn.edu Neuron Digest Saturday, 27 Feb 1993 Volume 11 : Issue 15 Today's Topics: Applying Standards to Neural Networks Sheet of neurons simulation Re: Sheet of neurons simulation Re: Sheet of neurons simulation NNET model choice. Handbook of Neural Algorithms COMPUTER STANDARDS & INTERFACES addendum Position Available at JPL lectureship Industrial Position in Artificial Intelligence and/or Neural Networks lectureship in cognitive science Microsoft Speech Research Neural Computation & Cognition: Opening for NN Programmer Send submissions, questions, address maintenance, and requests for old issues to "neuron-request@cattell.psych.upenn.edu". The ftp archives are available from cattell.psych.upenn.edu (130.91.68.31). Back issues requested by mail will eventually be sent, but may take a while. ---------------------------------------------------------------------- Subject: Applying Standards to Neural Networks From: erwin@trwacs.fp.trw.com (Harry Erwin) Organization: TRW Systems Division, Fairfax VA Date: 12 Feb 93 16:54:57 +0000 I was asked to review a proposal concerning the standardization of vocabulary for machine learning and neural networks. This is being distributed by the U.S. Technical Advisory Group to ANSI (JTC1 TAG). X3K5 is coordinating and developing a recommended position to JTC1 TAG for approval for submission to ISO/IEC JTC 1. This recommendation has to be returned to the JTC1 TAG Administrator no later than 1 March, 1993. The contact person is the JTC1 TAG Administrator Computer and Business Equipment Manufacturers Association (CBEMA) 1250 Eys Street NW, Suite 200 Washington, DC 20005-3922 phone: 202-737-8888 (Press 1 Twice) The vocabulary whose definitions are being standardized include: "knowledge acquisition" "learning strategy" "concept" "concept learning" "conceptual clustering" "taxonomy formation" "machine discovery" "connectionist model" "massively parallel processing" "connection machine" "connection system" "neural network" "connectionist network" "neurocomputer" "learning task" "concept description" "chunking" "discrimination network" "characteristic description" "discriminant description" "structural description" "concept formation" "partially learned concept" "version space (of a concept)" "description space" "instance space (of a concept)" "(concept) generalization" "consistent generalization" "constraint-based generalization" "similarity-based generalization" "complete generalization" "specialization" "caching (in machine learning)" "concept validation" "confusion matrix" "rote learning" "adaptive learning" "advice taking" "learning by being told" "learning from instruction" "incremental learning" "supervised learning" "inductive learning" "learning from induction" "deductive learning" "analytic learning" "explanation-based learning" "operationalization" "learning by analogy" "associative learning" "learning from observation and discovery" "learning without a teacher" "unsupervised learning" "learning from examples" "positive example" "negative example" "near-miss" "credit/blame assignment" "causal analysis" "unit (in neural networks)" "link (in neural networks)" "stable coalition" "hidden layer" "back propagation" "transfer function" For example, a "neural network" or "connectionist network" is defined as a "A network of neuron-like processors each of which performs some simple logical function, typically a logic threshold function. NOTE A neural network completes a computation when its units have finished exchanging messages and updating their potential, and settle into a stable state." A "hidden layer" is defined as "An object-oriented software layer which contains the method of instruction delivery among different programs run by different types of data. NOTE Every processor is told to block out any program that does not apply to the data object stored in it. From the user's point of view however it appears that different types of processors run different programs." --My recommendation on this proposal to the TRW representative to this standardization body is to vote no, since it is highly premature to standardize on terminology when the underlying concepts remain the subject of such active research." Cheers, Harry Erwin Internet: erwin@trwacs.fp.trw.com ------------------------------ Subject: Sheet of neurons simulation From: fburton@nyx.cs.du.edu (Francis Burton) Organization: University of Denver, Dept. of Math & Comp. Sci. Date: 18 Feb 93 18:11:27 +0000 On behalf of a colleague, I am looking for software that can be used to simulate large networks of connected neurons. The individual elements would have fairly unsophisticated (possibly identical) input/output properties. The topology of the network would be a flat sheet with random local interconnections, but later he may want to extend it to several layers. The program should run on a PC - preferably freeware, but he would be willing to pay for a commercial product (though I don't imagine there would be much of a market for such a program). I suspect that typical programs for neural-nets are not well suited to this particular problem -- please correct me if I am mistaken. Thank you for any pointers or advice. Francis Burton Physiology, Glasgow University, Glasgow G12 8QQ, Scotland. 041 339 8855 x8085 | JANET: F.L.Burton@glasgow.ac.uk !net: via mcsun & uknet "A horse! A horse!" | INTERNET: via nsfnet-relay.ac.uk BITNET: via UKACRL ------------------------------ Subject: Re: Sheet of neurons simulation From: hunter@work.nlm.nih.gov (Larry Hunter) Organization: National Library of Medicine Date: 18 Feb 93 22:48:18 +0000 Francis Burton asks: On behalf of a colleague, I am looking for software that can be used to simulate large networks of connected neurons. Well, there are many public domain (or nearly so) neural network simulators out there that can do arbitrary topologies and update rules, at least with a little bit of programming. IMHO, by far the best, both in terms of what comes with the system and how easy it is to program to meet specific needs, is the Xerion system from University of Toronto. It has wonderful graphical interfaces (X windows) and runs on practically any Unix/X platform. It is originally designed for use in machine learning and on artificial neural nets, but I think it offers a good possibility for adaptation to natural neural network simulation. Also, the author of the program, Drew van Camp is pretty accessible. It is available by anonymous ftp from the host ai.toronto.edu in the directory /pub/xerion. Here's a snippet from the README file: Xerion is a Neural Network simulator developed and used by the connectionist group at the University of Toronto. It contains libraries of routines for building networks, and graphically displaying them. As well it contains an optimization package which can train nets using several different methods including conjugate gradient. It is written in C and uses the X window system to do the graphics. It is being given away free of charge to Canadian industry and researchers. It comes with NO warranty. This distribution contains all the libraries used to build the simulators as well as several simulators built using them (Back Propagation, Recurrent Back Propagation, Boltzmann Machine, Mean Field Theory, Free Energy Manipulation, Kohonnen Net, Hard and Soft Competitive Learning). Also included are some sample networks built for the individual simulators. There are man pages for the simulators themselves and for many of the C language routines in the libraries. As well, xerion has online help available once the simulators are started. There is a tutorial on using Xerion in the 'doc' directory. I hope this does what you want. Larry Lawrence Hunter, PhD. National Library of Medicine Bldg. 38A, MS-54 Bethesda. MD 20894 USA tel: +1 (301) 496-9300 fax: +1 (301) 496-0673 internet: hunter@nlm.nih.gov encryption: PGP 2.1 public key via "finger hunter@work.nlm.nih.gov" ------------------------------ Subject: Re: Sheet of neurons simulation From: senseman@lucy.brainlab.utsa.edu (David M. Senseman) Organization: University of Texas at San Antonio Date: 19 Feb 93 13:32:04 +0000 In article Hunter@nlm.nih.gov writes: >IMHO, by far the best, both in terms of what >comes with the system and how easy it is to program to meet specific needs, >is the Xerion system from University of Toronto. The original posting asked for something to run "on a PC." This sounds unlikely to run on a PC even if it were running an X server. However, if you can get a hold of a UNIX based workstation, (Sparc, SGI, HP, IBM, etc), you might want to check out the Caltech Neurosimulator called "GENESIS". GENESIS also sports a very nice X-windows based front-end called "XODUS" (what else :). Unlike Xerion which was primarily designed for "non-biological" neural networks (i.e. back-propagation, etc.), GENESIS was designed from the beginning to model REAL neurons. In fact GENESIS has a group of commands that generates "sheets of neurons" and synaptically connects them to other sheets. Real HHK action potentials, Calcium channels, dendrtitic spines, etc, etc... I'm at home so I don't have all the details here, but if any one is interested, they can contact me by E-Mail. Again this program MUST be run on a UNIX box that supports X-Windows. If all you have is a PC, then this isn't for you. David M. Senseman, Ph.D. | Imagine the Creator as a low (senseman@lonestar.utsa.edu) | comedian, and at once the world Life Sciences Visualization Lab | becomes explicable. University of Texas at San Antonio | H.L. Mencken ------------------------------ Subject: NNET model choice. From: "Don" Date: Fri, 19 Feb 93 14:34:56 -0600 I need some help selecting a NNET model to use for a classification problem which involves looking at 20 thermal couples over a period of 10-20 samples. (continuously) The idea is to respond to a fault condition. (fault/nofault) I am considering Grossberg's STC(spacio-temperal classifier) model. We will be implementing on NeuralWare's Neuralmaker PRO II. Does any one know of other models or have info on how best to make this work? Thanks in advance. Don Schiewer | Internet schiewer@pa881a.inland.com | Onward Great Inland Steel | UUCP: !uucp!pa881a.inland!schiewer | Stream... ------------------------------ Subject: Handbook of Neural Algorithms From: "Sean Pidgeon" Date: Thu, 25 Feb 93 11:58:01 -0500 I would like to thank all those who took the trouble to respond to the questionnaire posted in the 23 September 1992 issue by my colleague Tamara Isaacs-Smith. The level of interest in our proposed Handbook has been gratifying. A focus group was convened in Philadelphia on February 23 to discuss the best way forward for the project, and our editorial plan is now quite well developed. All those interested in learning more about the Handbook project are invited to contact me directly or to visit the IOP Publishing booth at the World Congress on Neural Networks in Portland. Again, thanks for your support. ------------------------------ Subject: COMPUTER STANDARDS & INTERFACES addendum From: John Fulcher Date: Fri, 26 Feb 93 13:55:36 -0500 COMPUTER STANDARDS & INTERFACES (North-Holland) Forthcoming Special Issue on ANN Standards ADDENDUM TO ORIGINAL POSTING Prompted by enquiries from several people regarding my original Call for Papers posting, I felt I should offer the following additional information (clarification). By ANN "Standards" we do not mean exclusively formal standards (in the ISO, IEEE, ANSI, CCITT etc. sense), although naturally enough we will be including papers on activities in these areas. "Standards" should be interpreted in its most general sense, namely as standard APPROACHES (e.g. the backpropagation algorithm & its many variants). Thus if you have a paper on some (any?) aspect of ANNs, provided it is prefaced by a summary of the standard approach(es) in that particular area, it could well be suitable for inclusion in this special issue of CS&I. If in doubt, post fax or email a copy by April 30th to: John Fulcher, Department of Computer Science, University of Wollongong, Northfields Avenue, Wollongong NSW 2522, Australia. fax: +61 42 213262 email: john@cs.uow.edu.au.oz ------------------------------ Subject: Position Available at JPL From: Padhraic Smyth Date: Thu, 18 Feb 93 11:49:36 -0800 We currently have an opening in our group for a new PhD graduate in the general area of signal processing and pattern recognition. While the job description does not mention neural computation per se, it may be of interest to some members of the this mailing list. For details see below. Padhraic Smyth, JPL RESEARCH POSITION AVAILABLE AT THE JET PROPULSION LABORATORY, CALIFORNIA INSTITUTE OF TECHNOLOGY The Communications Systems Research Section at JPL has an immediate opening for a permanent member of technical staff in the area of adaptive signal processing and statistical pattern recognition. The position requires a PhD in Electrical Engineering or a closely related field and applicants should have a demonstrated ability to perform independent research. A background in statistical signal processing is highly desirable. Background in information theory, estimation and detection, advanced statistical methods, and pattern recognition, would also be a plus. Current projects within the group include the use of hidden Markov models for change detection in time series, and statistical methods for geologic feature detection in remotely sensed image data. The successful applicant will be expected to perform both basic and applied research and to propose and initiate new research projects. Permanent residency or U.S. citizenship is not a strict requirement - however, candidates not in either of these categories should be aware that their applications will only be considered in exceptional cases. Interested applicants should send their resume (plus any supporting background material such as recent relevant papers) to: Dr. Stephen Townes JPL 238-420 4800 Oak Grove Drive Pasadena, CA 91109. (email: townes@bvd.jpl.nasa.gov) ------------------------------ Subject: lectureship From: Tony_Prescott Date: Fri, 19 Feb 93 10:59:46 +0000 LECTURESHIP IN COGNITIVE SCIENCE University of Sheffield, UK. Applications are invited for the above post tenable from 1st October 1993 for three years in the first instance but with expectation of renewal. Preference will be given to candidates with a PhD in Cognitive Science, Artificial Intelligence, Cognitive Psychology, Computer Science, Robotics, or related disciplines. The Cognitive Science degree is an integrated course taught by the departments of Psychology and Computer Science. Research in Cognitive Science was highly evaluated in the recent UFC research evaluation exercise, special areas of interest being vision, speech, language, neural networks, and learning. The successful candidate will be expected to undertake research vigorously. Supervision of programming projects will be required, hence considerable experience with Lisp, Prolog, and/or C is essential. It is expected that the appointment will be made on the Lecturer A scale (13,400-18,576 pounds(uk) p.a.) according to age and experience but enquiries from more experienced staff able to bring research resources are welcomed. Informal enquiries to Professor John P Frisby 044-(0)742-826538 or e-mail jpf@aivru.sheffield.ac.uk. Further particulars from the director of Personnel Services, The University, Sheffield S10 2TN, UK, to whom all applications including a cv and the names and addresses of three referees (6 copies of all documents) should be sent by 1 April 1993. Short-listed candidates will be invited to Sheffield for interview for which travel expenses (within the UK only) will be funded. Current permanent research staff in Cognitive Science at Sheffield include: Prof John Frisby (visual psychophysics), Prof John Mayhew (computer vision, robotics, neural networks) Prof Yorik Wilks (natural language understanding) Dr Phil Green (speech recognition) Dr John Porrill (computer vision) Dr Paul McKevitt (natural language understanding) Dr Peter Scott (computer assisted learning) Dr Rod Nicolson (human learning) Dr Paul Dean (neuroscience, neural networks) Mr Tony Prescott (neural networks, comparative cog sci) ------------------------------ Subject: Industrial Position in Artificial Intelligence and/or Neural Networks From: Jerome Soller Date: Fri, 19 Feb 93 14:09:43 -0700 I have just been made aware of a job opening in artificial intelligence and/or neural networks in southeast Ogden, UT. This company maintains strong technical interaction with existing industrial, U.S. government laboratory, and university strengths in Utah. Ogden is a half hour to 45 minute drive from Salt Lake City, UT. For further information, contact Dale Sanders at 801-625-8343 or dsanders@bmd.trw.com . The full job description is listed below. Sincerely, Jerome Soller U. of Utah Department of Computer Science and VA Geriatric, Research, Education and Clinical Center Knowledge engineering and expert systems development. Requires five years formal software development experience, including two years expert systems development. Requires experience implementing at least one working expert system. Requires familiarity with expert systems development tools and DoD specification practices. Experience with neural nets or fuzzy logic systems may qualify as equivalent experience to expert systems development. Familiarity with Ada, C/C++, database design, and probabilistic risk assessment strongly desired. Requires strong communication and customer interface skills. Minimum degree: BS in computer science, engineering, math, or physical science. M.S. or Ph.D. preferred. U.S. Citizenship is required. Relocation funding is limited. ------------------------------ Subject: lectureship in cognitive science From: Martin Cooke Date: Tue, 23 Feb 93 12:54:29 +0000 To Dan: thanks, and all the best for the auditory list. To the list: a job possibility Martin - ------------------------------ LECTURESHIP IN COGNITIVE SCIENCE University of Sheffield, UK. Applications are invited for the above post tenable from 1st October 1993 for three years in the first instance but with expectation of renewal. Preference will be given to candidates with a PhD in Cognitive Science, Artificial Intelligence, Cognitive Psychology, Computer Science, Robotics, or related disciplines. The Cognitive Science degree is an integrated course taught by the departments of Psychology and Computer Science. Research in Cognitive Science was highly evaluated in the recent UFC research evaluation exercise, special areas of interest being vision, speech, language, neural networks, and learning. The successful candidate will be expected to undertake research vigorously. Supervision of programming projects will be required, hence considerable experience with Lisp, Prolog, and/or C is essential. It is expected that the appointment will be made on the Lecturer A scale (13,400-18,576 pounds(uk) p.a.) according to age and experience but enquiries from more experienced staff able to bring research resources are welcomed. Informal enquiries to Professor John P Frisby 044-(0)742-826538 or e-mail jpf@aivru.sheffield.ac.uk. Further particulars from the director of Personnel Services, The University, Sheffield S10 2TN, UK, to whom all applications including a cv and the names and addresses of three referees (6 copies of all documents) should be sent by 1 April 1993. Short-listed candidates will be invited to Sheffield for interview for which travel expenses (within the UK only) will be funded. Current permanent research staff in Cognitive Science at Sheffield include: Prof J P Frisby (visual psychophysics), Prof J E W Mayhew *(computer vision, robotics, neural networks) Prof Y Wilks (natural language understanding, from June 93) Dr P D Green (speech recognition) Dr J Porrill (computer vision) Dr P McKevitt (natural language understanding) Dr P Scott (computer assisted learning) Dr R I Nicolson (human learning) Dr P Dean (neuroscience, neural networks) Dr M P Cooke (auditory modelling) Dr G J Brown (auditory modelling) Mr A J Prescott (neural networks, comparative cog sci) ------------------------------ Subject: Microsoft Speech Research From: Xuedong Huang Date: Tue, 23 Feb 93 22:19:47 -0800 As you may know, I've started a new speech group here at Microsoft. For your information, I have enclosed the full advertisement we have been using to publicize the openings. If you are interested in joining MS, I strongly encourage you to apply and we will look forward to following up with you. - ------------------------------------------------------------ THE FUTURE IS HERE. Speech Recognition. Intuitive Graphical Interfaces. Sophisticated User Agents. Advanced Operating Systems. Robust Environments. World Class Applications. Who's Pulling It All Together? Microsoft. We're setting the stage for the future of computing, building a world class research group and leveraging a solid foundation of object based technology and scalable operating systems. What's more, we're extending the recognition paradigm, employing advanced processor and RISC-based architecture, and harnessing distributed networks to connect users to worlds of information. We want to see more than just our own software running. We want to see a whole generation of users realize the future of computing. Realize your future with a position in our Speech Recognition group. Research Software Design Engineers, Speech Recognition. Primary responsibilities include designing and developing User Interface and systems level software for an advanced speech recognition system. A minimum of 3 years demonstrated microcomputer software design and development experience in C is required. Knowledge of Windows programming, speech recognition systems, hidden Markov model theory, statistics, DSP, or user interface development is preferred. A BA/BS in computer science or related discipline is required. An advanced degree (MS or Ph.D.) in a related discipline is preferred. Researchers, Speech Recognition. Primary responsibilities include research on stochastic modeling techniques to be applied to an advanced speech recognition system. A minimum of 4 years demonstrated research excellence in the area of speech recognition or spoken language understanding systems is required. Knowledge of Windows and real-time C programming for microcomputers, hidden Markov model theory, decoder systems design, DSP, and spoken language understanding is preferred. A MA/MS in CS or related discipline is required. A PhD degree in CS, EE, or related discipline is preferred. Make The Most of Your Future. At Microsoft, our technical leadership and strong Software Developers and Researchers stay ahead of the times, creating vision and turning it into reality. To apply, send your resume and cover letter, noting "ATTN: N5935-0223" to: Surface: Microsoft Recruiting ATTN: N5935-0223 One Microsoft Way Redmond, WA 98052-6399 Email: ASCII ONLY y-wait@microsoft.com.us Microsoft is an equal opportunity employer working to increase workforce diversity. ------------------------------ Subject: Neural Computation & Cognition: Opening for NN Programmer From: gluck@pavlov.rutgers.edu (Mark Gluck) Date: Mon, 22 Feb 93 08:04:28 -0500 POSITION AVAILABLE: NEURAL-NETWORK RESEARCH PROGRAMMER At the Center for Neuroscience at Rutgers-Newark, we have an opening for a full or part-time research programmer to assist in developing neural-network simulations. The research involves integrated experimental and theoretical analyses of the cognitive and neural bases of learning and memory. The focus of this research is on understanding the underlying neurobiological mechanisms for complex learning behaviors in both animals and humans. Substantial prior experience and understanding of neural-network theories and algorithms is required. Applicants should have a high level of programming experience (C or Pascal), and familiarity with Macintosh and/or UNIX. Strong English-language communication and writing skills are essential. *** This position would be particularly appropriate for a graduating college senior who seeks "hands-on" research experience prior to graduate school in the cognitive, neural, or computational sciences *** Applications are being accepted now for an immediate start-date or for starting in June or September of this year. NOTE TO N. CALIF. APPLICANTS: Interviews for applicants from the San Francisco/Silicon Valley area will be conducted at Stanford in late March. The Neuroscience Center is located 20 minutes outside of New York City in northern New Jersey. For further information, please send an email or hard-copy letter describe your relevant background, experience, and career goals to: ______________________________________________________________________ Dr. Mark A. Gluck Center for Molecular & Behavioral Neuroscience Rutgers University 197 University Ave. Newark, New Jersey 07102 Phone: (201) 648-1080 (Ext. 3221) Fax: (201) 648-1272 Email: gluck@pavlov.rutgers.edu ------------------------------ End of Neuron Digest [Volume 11 Issue 15] *****************************************