From rfl77551@pegasus.cc.ucf.edu Sun May 19 14:25:41 1996 Received: from lucy.cs.wisc.edu (lucy.cs.wisc.edu [128.105.2.11]) by sea.cs.wisc.edu (8.6.12/8.6.12) with ESMTP id OAA21756 for ; Sun, 19 May 1996 14:25:31 -0500 Received: from TELNET-1.SRV.CS.CMU.EDU (TELNET-1.SRV.CS.CMU.EDU [128.2.254.108]) by lucy.cs.wisc.edu (8.6.12/8.6.12) with SMTP id OAA09286 for ; Sun, 19 May 1996 14:25:30 -0500 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id aa27290; 19 May 96 14:23:24 EDT Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa27288; 19 May 96 14:10:30 EDT Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa03927; 19 May 96 14:09:48 EDT Received: from EDRC.CMU.EDU by B.GP.CS.CMU.EDU id al01136; 19 May 96 14:08:30 EDT Received: from [132.170.246.40] by EDRC.CMU.EDU id aa21548; 19 May 96 13:59:11 EDT Received: from Pegasus.cc.ucf.edu by knights.cc.ucf.edu with smtp RFC1413_id rfl77551 (Smail3.1.29.1 #53) id m0uLChm-0006zzC; Sun, 19 May 96 13:55 EDT Date: Sun, 19 May 1996 13:56:44 -0400 (EDT) From: Richard F Long X-Sender: rfl77551@Pegasus Reply-To: Richard F Long To: connectionists@cs.cmu.edu Subject: Re: Connectionist Learning - Some New Ideas In-Reply-To: <199605161926.OAA27944@mozzarella.cs.wisc.edu> Message-ID: MIME-Version: 1.0 Content-Type: TEXT/PLAIN; charset=US-ASCII There may be another reason for the brain to construct networks that are 'minimal' having to do with Chaitin and Kolmogorov computational complexity. If a minimal network corresponds to a 'minimal algorithm' for implementing a particular computation, then that particular network must utilize all of the symmetries and regularities contained in the problem, or else these symmetries could be used to reduce the network further. Chaitin has shown that no algorithm for finding this minimal algorithm in the general case is possible. However, if an evolutionary programming method is used in which the fitness function is both 'solves the problem' and 'smallest size' (i.e. Occam's razor), then it is possible that the symmetries and regularities in the problem would be extracted as smaller and smaller networks are found. I would argue that such networks would compute the solution less by rote or brute force, and more from a deep understanding of the problem. I would like to hear anyone else's thoughts on this. Richard Long rfl77551@pegasus.cc.ucf.edu General Research and Device Corp. Oviedo, FL & University of Central Florida From maja@garnet.cs.brandeis.edu Sun May 19 19:14:32 1996 Received: from lucy.cs.wisc.edu (lucy.cs.wisc.edu [128.105.2.11]) by sea.cs.wisc.edu (8.6.12/8.6.12) with ESMTP id TAA23835 for ; Sun, 19 May 1996 19:14:26 -0500 Received: from TELNET-1.SRV.CS.CMU.EDU (TELNET-1.SRV.CS.CMU.EDU [128.2.254.108]) by lucy.cs.wisc.edu (8.6.12/8.6.12) with SMTP id TAA10926 for ; Sun, 19 May 1996 19:14:24 -0500 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id aa27646; 19 May 96 19:12:07 EDT Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa27644; 19 May 96 19:04:45 EDT Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa04061; 19 May 96 19:04:10 EDT Received: from EDRC.CMU.EDU by B.GP.CS.CMU.EDU id aa03900; 19 May 96 18:58:37 EDT Received: from garnet.cs.brandeis.edu by EDRC.CMU.EDU id aa22143; 19 May 96 18:58:16 EDT Received: by garnet.cs.brandeis.edu (940816.SGI.8.6.9/940406.SGI) id SAA03201; Sun, 19 May 1996 18:56:40 -0400 Date: Sun, 19 May 1996 18:56:40 -0400 Message-Id: <199605192256.SAA03201@garnet.cs.brandeis.edu> From: Maja Mataric To: alife@cognet.ucla.edu, cogpsy@neuro.psy.soton.ac.uk, connectionists@cs.cmu.edu, gann-list@cs.iastate.edu, hybrid-list@cs.ua.edu, intcon@phoenix.ee.unsw.edu.au, ml@ics.uci.edu, reinforce@cs.uwa.EDU.AU Subject: CALL for PAPERS CALL FOR PAPERS (http://www.cs.brandeis.edu:80/~maja/abj-special-issue/) ADAPTIVE BEHAVIOR Journal Special Issue on COMPLETE AGENT LEARNING IN COMPLEX ENVIRONMENTS Guest editor: Maja J Mataric Submission Deadline: June 1, 1996. Adaptive Behavior is an international journal published by MIT Press; Editor-in-Chief: Jean-Arcady Meyer, Ecole Normale Superieure, Paris. In the last decade, the problems being treated in AI, Alife, and Robotics have witnessed an increase in complexity as the domains under investigation have transitioned from theoretically clean scenarios to more complex dynamic environments. Agents that must adapt in environments such as the physical world, an active ecology or economy, and the World Wide Web, challenge traditional assumptions and approaches to learning. As a consequence, novel methods for automated adaptation, action selection, and new behavior acquisition have become the focus of much research in the field. This special issue of Adaptive Behavior will focus on situated agent learning in challenging environments that feature noise, uncertainty, and complex dynamics. We are soliciting papers describing finished work on autonomous learning and adaptation during the lifetime of a complete agent situated in a dynamic environment. We encourage submissions that address several of the following topics within a whole agent learning system: * learning from ambiguous perceptual inputs * learning with noisy/uncertain action/motor outputs * learning from sparse, irregular, inconsistent, and noisy reinforcement/feedback * learning in real time * combining built-in and learned knowledge * learning in complex environments requiring generalization in state representation * learning from incremental and delayed feedback * learning in smoothly or discontinuously changing environments We invite submissions from all areas in AI, Alife, and Robotics that treat either complete synthetic systems or models of biological adaptive systems situated in complex environments. Submitted papers should be delivered by June 1, 1996. Authors intending to submit a manuscript should contact the guest editor to discuss paper suitability for this issue. Use maja@cs.brandeis.edu or tel: (617) 736-2708 or fax: (617) 736-2741. Manuscripts should be typed or laser-printed in English (with American spelling preferred) and double-spaced. Both paper and electronic submission are possible, as described below. Copies of the complete Adaptive Behavior Instructions to Contributors are available on request--also see the Adaptive Behavior journal's home page at: http://www.ens.fr:80/bioinfo/www/francais/AB.html. For paper submissions, send five (5) copies of submitted papers (hard-copy only) to: Maja Mataric Volen Center for Complex Systems Computer Science Department Brandeis University Waltham, MA 02254-9110, USA For electronic submissions, use Postscript format, ftp the file to ftp.cs.brandeis.edu/incoming, and send an email notification to maja@cs.brandeis.edu. For a Web page of this call, and detailed ftp directions, see: http://www.cs.brandeis.edu/~maja/abj-special-issue/ From mark@cdu.ucl.ac.uk Mon May 20 11:41:02 1996 Received: from lucy.cs.wisc.edu (lucy.cs.wisc.edu [128.105.2.11]) by sea.cs.wisc.edu (8.6.12/8.6.12) with ESMTP id LAA09715 for ; Mon, 20 May 1996 11:40:56 -0500 Received: from TELNET-1.SRV.CS.CMU.EDU (TELNET-1.SRV.CS.CMU.EDU [128.2.254.108]) by lucy.cs.wisc.edu (8.6.12/8.6.12) with SMTP id LAA18781 for ; Mon, 20 May 1996 11:40:54 -0500 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id aa28557; 20 May 96 9:43:26 EDT Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa28554; 20 May 96 9:23:45 EDT Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa04851; 20 May 96 9:22:56 EDT Received: from EDRC.CMU.EDU by B.GP.CS.CMU.EDU id aa09308; 20 May 96 6:25:25 EDT Received: from [144.82.100.24] by EDRC.CMU.EDU id aa23751; 20 May 96 6:25:06 EDT Received: from cdu.ucl.ac.uk (actually cdu.cdu.ucl.ac.uk) by mail-d.bcc.ac.uk with SMTP (PP); Mon, 20 May 1996 11:24:36 +0100 Received: from [128.40.36.22] (mac5) by cdu.ucl.ac.uk (4.1/SMI-4.1) id AA12902; Mon, 20 May 96 11:24:27 BST Date: Mon, 20 May 96 11:24:26 BST X-Sender: mark@cdu.cdu.ucl.ac.uk Message-Id: Mime-Version: 1.0 Content-Type: text/plain; charset="us-ascii" To: connectionists@cs.cmu.edu From: Mark Johnson Subject: Connectionist Learning - Some New Ideas >One loses about 100,000 cortical neurons a day (about a percent of >the original number every three years) under normal conditions. Does anyone have a concrete citation (a journal article) for this or any other similar estimate regarding the daily cell death rate in the cortex of a normal brain? I've read such numbers in a number of connectionist papers but none cite any neurophysiological studies that substantiate these numbers. Thanks, Raj ================= >From my reading of the recent literature massive postnatal cell loss in the human cortex is a myth. There is postnatal cortical cell death in rodents, but in primates (including humans) there is only (i) a decreased density of cell packing, and (ii) massive (up to 50%) synapse loss. (The decreased density of cell packing was apparently misinterpreted as cell loss in the past). Of course, there are pathological cases, such as Alzheimers, in which there is cell loss. I have written a review of human postnatal brain development which I can send out on request. Mark Johnson =============== Mark H. Johnson Senior Research Scientist (Special Appointment) Professor of Psychology, University College London MRC Cognitive Development Unit, 4 Taviton Street, London WC1H OBT, UK tel: 0171-387-4692 fax: 0171-383-0398 From carl@cs.toronto.edu Tue May 21 16:16:50 1996 Received: from lucy.cs.wisc.edu (lucy.cs.wisc.edu [128.105.2.11]) by sea.cs.wisc.edu (8.6.12/8.6.12) with ESMTP id QAA08609 for ; Tue, 21 May 1996 16:16:45 -0500 Received: from TELNET-1.SRV.CS.CMU.EDU (TELNET-1.SRV.CS.CMU.EDU [128.2.254.108]) by lucy.cs.wisc.edu (8.6.12/8.6.12) with SMTP id QAA09412 for ; Tue, 21 May 1996 16:16:42 -0500 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id aa29821; 21 May 96 15:08:11 EDT Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa29819; 21 May 96 14:39:27 EDT Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa05718; 21 May 96 14:39:02 EDT Received: from RI.CMU.EDU by B.GP.CS.CMU.EDU id ab01970; 21 May 96 14:13:46 EDT Received: from yonge.cs.toronto.edu by RI.CMU.EDU id aa06325; 21 May 96 14:11:53 EDT Received: from neuron.ai.toronto.edu ([128.100.3.14]) by yonge.cs.toronto.edu with SMTP id <86544>; Tue, 21 May 1996 14:11:46 -0400 Received: by neuron.ai.toronto.edu id <1240>; Tue, 21 May 1996 14:11:36 -0400 From: Carl Edward Rasmussen To: Connectionists@cs.cmu.edu Subject: DELVE Message-Id: <96May21.141136edt.1240@neuron.ai.toronto.edu> Date: Tue, 21 May 1996 14:11:35 -0400 Announcing the release of DELVE DELVE --- Data for Evaluating Learning in Valid Experiments DELVE contains a collection of datasets for use in evaluating the predictive performance of empirical learning methods, such as linear models, neural networks, smoothing splines, decision trees, and many other regression and classification procedures. DELVE also includes software that facilitates using this data to assess learning methods in a statistically valid way. Ultimately, DELVE will include results of applying many methods to many tasks, making comparisons between methods much easier than in the past. A preliminary version of DELVE is now freely available on the web at URL http://www.cs.utoronto.ca/~delve. From this web site, you can get to the manual, the software for the DELVE environment, the DELVE datasets, and precise definitions, source code, and results for various learning methods. Contributions of data and methods from other researchers will be added to the web site in future. DELVE was created at the university of Toronto by C. E. Rasmussen R. M. Neal G. E. Hinton D. van Camp M. Revow Z. Ghahramani R. Kustra R. Tibshirani -- \ Carl Edward Rasmussen Email: carl@cs.toronto.edu o/\_ Dept of Computer Science Phone: +1 (416) 978 7391 <|__,\ University of Toronto, Home : +1 (416) 531 5685 "> | Toronto, ONTARIO, FAX : +1 (416) 978 1455 ` | Canada, M5S 1A4 web : http://www.cs.toronto.edu/~carl From eengler@u-media.com Wed May 22 09:39:11 1996 Received: from lucy.cs.wisc.edu (lucy.cs.wisc.edu [128.105.2.11]) by sea.cs.wisc.edu (8.6.12/8.6.12) with ESMTP id JAA25914 for ; Wed, 22 May 1996 09:38:07 -0500 Received: from cs.uwa.oz.au (bilby.cs.uwa.oz.au [130.95.1.11]) by lucy.cs.wisc.edu (8.6.12/8.6.12) with ESMTP id JAA19229 for ; Wed, 22 May 1996 09:38:01 -0500 Received: from (mafm@parma.cs.uwa.oz.au [130.95.1.7]) by cs.uwa.oz.au (8.6.8/8.5) with SMTP id TAA24993; Wed, 22 May 1996 19:32:09 +0800 Message-Id: <199605221132.TAA24993@cs.uwa.oz.au> From: Edward Engler To: connect-bb@ed.eusip, incon@dcs.shef.ac.uk, reinforce@cs.uwa.edu.au, gann-list@cs.iastate.edu, neuron@CATTELL.psych.upenn.edu, alife@cognet.ucla.edu, cogpsy@neuro.psy.soton.ac.uk, connectionists@cs.cmu.edu, epsynet%uhupvm1.BITNET@bitnet.mailgate.cs.mu.oz.au, elsnet-list@cogsci.ed.ac.uk, irl-net%irlearn.BITNET@bitnet.mailgate.cs.mu.oz.au, arpanet-bboards@edu.mit.lcs.mc, hybrid-list@cs.ua.edu, colt@cs.uiuc.edu, ilpnet@IJS.si, cphc-jobs@ukc.ac.uk, ai-stats@watstat.uwaterloo.ca Subject: Research and development positions Date: Fri, 17 May 1996 10:02:07 -0400 Empirical Media Corporation Pittsburgh, PA Positions Available: Research Scientists and Software Developers Empirical Media Corporation is a venture capital-backed software startup directed at bringing the latest technology in machine learning and collaborative filtering to the Internet. EMC has positions open in both product development and scientific research for highly qualified candidates. EMC's web-based service provides highly personalized information filtering to general and vertical market Internet users. User feedback regarding content is collected and used to increase the system's understanding of the user's interests. The work in the product development area includes user interface development, machine learning, collaborative filtering, information retrieval, agent technology, and distributed systems. EMC is pushing the edge of research particularly hard in the areas of machine learning, neural networks, collaborative filtering, and virtual society communications (a branch of HCI). Candidates must have a research background in one of the relevant areas, have extensive programming experience, excellent analytical skills, strong interpersonal and communication skills, and be self-motivated. Ideal Candidates will have experience in object-oriented design and C++ programming. Experience with Oracle, Windows NT, Visual C++, RPC technology, and Internet-related areas is also beneficial. For further information, please contact Ed Engler at (412)688-8870. Additional information can be found at http://www.empirical.com, however our service is currently in a limited beta, and has not yet been made available to the general public. Edward Engler {M( -- Empirical Media Corporation 5001 Centre Ave., Pittsburgh, PA 15213 Voice: (412) 688-8870 Fax:(412) 688-8853 From moriarty@AIC.NRL.Navy.Mil Wed May 22 10:20:51 1996 Received: from lucy.cs.wisc.edu (lucy.cs.wisc.edu [128.105.2.11]) by sea.cs.wisc.edu (8.6.12/8.6.12) with ESMTP id KAA26901 for ; Wed, 22 May 1996 10:20:34 -0500 Received: from TELNET-1.SRV.CS.CMU.EDU (TELNET-1.SRV.CS.CMU.EDU [128.2.254.108]) by lucy.cs.wisc.edu (8.6.12/8.6.12) with SMTP id KAA19972 for ; Wed, 22 May 1996 10:20:32 -0500 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id ab00792; 22 May 96 3:16:57 EDT Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id ab00790; 22 May 96 3:04:44 EDT Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa06217; 22 May 96 3:04:18 EDT Received: from EDRC.CMU.EDU by B.GP.CS.CMU.EDU id aa11627; 20 May 96 10:31:56 EDT Received: from [192.26.18.51] by EDRC.CMU.EDU id aa24598; 20 May 96 10:30:06 EDT Received: from sun27.aic.nrl.navy.mil by Sun0.AIC.NRL.Navy.Mil (4.1/SMI-4.0) id AA19564; Mon, 20 May 96 10:29:24 EDT Received: by sun27.aic.nrl.navy.mil; Mon, 20 May 96 10:29:23 EDT Date: Mon, 20 May 96 10:29:23 EDT From: moriarty@AIC.NRL.Navy.Mil Message-Id: <9605201429.AA16331@sun27.aic.nrl.navy.mil> To: Connectionists@cs.cmu.edu Subject: Papers Available: Neuro-Evolution in Robotics Reply-To: moriarty@AIC.NRL.Navy.Mil The following two papers on applying neuro-evolution to robot arm control are available from our WWW page: http://www.cs.utexas.edu/users/nn/ Source code for the SANE system is also avaiable from the WWW site. ------------------------------------------------------------------------ Evolving Obstacle Avoidance Behavior in a Robot Arm David E. Moriarty and Risto Miikkulainen To Appear at From Animals to Animats The Fourth International Conference on Simulation of Adaptive Behavior (SAB96). Cape Cod, MA. 1996 8 pages Abatract: Existing approaches for learning to control a robot arm rely on supervised methods where correct behavior is explicitly given. It is difficult to learn to avoid obstacles using such methods, however, because examples of obstacle avoidance behavior are hard to generate. This paper presents an alternative approach that evolves neural network controllers through genetic algorithms. No input/output examples are necessary, since neuro-evolution learns from a single performance measurement over the entire task of grasping an object. The approach is tested in a simulation of the OSCAR-6 robot arm which receives both visual and sensory input. Neural networks evolved to effectively avoid obstacles at various locations to reach random target locations. ------------------------------------------------------------------------ Hierarchical Evolution of Neural Networks David E. Moriarty and Risto Miikkulainen Technical Report #AI96-242, Department of Computer Sciences, The University of Texas at Austin. 16 pages Abstract: In most applications of neuro-evolution, each individual in the population represents a complete neural network. Recent work on the SANE system, however, has demonstrated that evolving individual neurons often produces a more efficient genetic search. This paper explores the merits of neuro-evolution both at the neuron level and at the network level. While SANE can solve easy tasks in just a few generations, in tasks that require high precision, its progress often stalls and is exceeded by a standard, network-level evolution. In this paper, a new approach called Hierarchical SANE is presented that combines the advantages of both approaches by integrating two levels of evolution in a single framework. Hierarchical SANE couples the early explorative quality of SANE's neuron-level search with the late exploitative quality of a more standard network-level evolution. In a sophisticated robot arm manipulation task, Hierarchical SANE significantly outperformed both SANE and a standard, network-level neuro-evolution approach, suggesting that it can more efficiently solve a broad range of tasks. ------------------------------------------------------------------------ Dave Moriarty Artificial Intelligence Laboratory Department of Computer Sciences The University of Texas at Austin moriarty@cs.utexas.edu http://www.cs.utexas.edu/users/moriarty http://www.cs.utexas.edu/users/nn From smlamb@owlnet.rice.edu Wed May 22 10:20:54 1996 Received: from lucy.cs.wisc.edu (lucy.cs.wisc.edu [128.105.2.11]) by sea.cs.wisc.edu (8.6.12/8.6.12) with ESMTP id KAA26903 for ; Wed, 22 May 1996 10:20:36 -0500 Received: from TELNET-1.SRV.CS.CMU.EDU (TELNET-1.SRV.CS.CMU.EDU [128.2.254.108]) by lucy.cs.wisc.edu (8.6.12/8.6.12) with SMTP id KAA19974 for ; Wed, 22 May 1996 10:20:34 -0500 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id ac00792; 22 May 96 3:17:41 EDT Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id ac00790; 22 May 96 3:04:46 EDT Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa06221; 22 May 96 3:04:21 EDT Received: from CS.CMU.EDU by B.GP.CS.CMU.EDU id aa12727; 20 May 96 11:36:34 EDT Received: from owlnet.rice.edu by CS.CMU.EDU id aa06941; 20 May 96 11:36:04 EDT Received: from great-gray.owlnet.rice.edu (great-gray.owlnet.rice.edu [128.42.49.3]) by owlnet.rice.edu (8.7.1/8.7.1) with ESMTP id KAA22083; Mon, 20 May 1996 10:35:52 -0500 (CDT) Received: (from smlamb@localhost) by great-gray.owlnet.rice.edu (8.7.5/8.7.3) id KAA13723; Mon, 20 May 1996 10:35:51 -0500 (CDT) Date: Mon, 20 May 1996 10:35:50 -0500 (CDT) From: Sydney M Lamb To: Jonathan_Stein@comverse.com cc: cherkaue@cs.wisc.edu, Steven Small , connectionists@cs.cmu.edu Subject: Re: Re[2]: Connectionist Learning - Some New Ideas In-Reply-To: <9604178323.AA832376960@hub.comverse.com> Message-ID: MIME-Version: 1.0 Content-Type: TEXT/PLAIN; charset=US-ASCII On Fri, 17 May 1996 Jonathan_Stein@comverse.com wrote: > > One needn't draw upon injuries to prove the point. One loses about 100,000 > cortical neurons a day (about a percent of the original number every three > years) under normal conditions. This loss is apparently not significant > for brain function. This has been often called the strongest argument for > distributed processing in the brain. Compare this ability with the fact that > single conductor disconnection cause total system failure with high > probability in conventional computers. > > Although certainly acknowledged by the pioneers of artificial neural > network techniques, very few networks designed and trained by present > techniques are anywhere near that robust. Studies carried out on the > Hopfield model of associative memory DO show graceful degradation of > memory capacity with synapse dilution under certain conditions (see eg. > DJ Amit's book "Attractor Neural Networks"). Synapse pruning has been > applied to trained feedforward networks (eg. LeCun's "Optimal Brain Damage") > but requires retraining of the network. > > JS > There seems to be some differing information coming from different sources. The way I heard it, the typical person has lost only about 3% of the original total of cortical neurons after about 70 or 80 years. As for the argument about distributed processing, two comments: (1) there are different kinds of distributive processing; one of them also uses strict localization of points of convergence for distributed subnetworks of information (cf. A. Damasio 1989 --- several papers that year). (2) If the brain is like other biological systems, the neurons being lost are probably most the ones not being used --- ones that have been remaining latent and available to assume some function, but never called upon. Hence what you get with old age is not so much loss of information as loss of ability to learn new things --- varying in amount, of course, from one individual to the next. Syd Lamb Linguistics and Cognitive Science Rice University From karaali@ukraine.corp.mot.com Wed May 22 10:20:55 1996 Received: from lucy.cs.wisc.edu (lucy.cs.wisc.edu [128.105.2.11]) by sea.cs.wisc.edu (8.6.12/8.6.12) with ESMTP id KAA26905 for ; Wed, 22 May 1996 10:20:38 -0500 Received: from TELNET-1.SRV.CS.CMU.EDU (TELNET-1.SRV.CS.CMU.EDU [128.2.254.108]) by lucy.cs.wisc.edu (8.6.12/8.6.12) with SMTP id KAA19977 for ; Wed, 22 May 1996 10:20:35 -0500 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id ad00792; 22 May 96 3:18:39 EDT Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id ad00790; 22 May 96 3:04:51 EDT Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa06225; 22 May 96 3:04:26 EDT Received: from CS.CMU.EDU by B.GP.CS.CMU.EDU id aa22654; 20 May 96 23:34:45 EDT Received: from motgate2.mot.com by CS.CMU.EDU id aa13151; 20 May 96 23:34:27 EDT Received: from pobox.mot.com (pobox.mot.com [129.188.137.100]) by motgate2.mot.com (8.7.3/8.6.10/MOT-3.8) with ESMTP id DAA21805 for ; Tue, 21 May 1996 03:32:58 GMT Received: from ukraine.corp.mot.com (ukraine.ccrl.mot.com [182.1.83.20]) by pobox.mot.com (8.7.3/8.6.10/MOT-3.8) with SMTP id WAA08708 for ; Mon, 20 May 1996 22:34:23 -0500 (CDT) Received: from fiji.mot.com by ukraine.corp.mot.com (4.1/SMI-4.1) id AA12612; Mon, 20 May 96 09:44:27 CDT Received: by fiji.mot.com (SMI-8.6/SMI-SVR4) id JAA05484; Mon, 20 May 1996 09:44:26 -0500 Date: Mon, 20 May 1996 09:44:26 -0500 From: Orhan Karaali Message-Id: <199605201444.JAA05484@fiji.mot.com> To: Connectionists@cs.cmu.edu Subject: Linguist with neural net background X-Sun-Charset: US-ASCII Motorola Chicago, IL COMPUTATIONAL LINGUIST FOR TEXT-TO-SPEECH SYNTHESIS Motorola's Chicago Corporate Research Laboratories is currently seeking a computational linguist to join the Speech Synthesis Group in its Speech Processing Systems Research Laboratory in Schaumburg, Illinois. The Speech Synthesis Group of Motorola's Speech Processing Laboratory has developed a world-class multi-language text-to-speech synthesizer. This synthesizer is based on innovative neural network and signal processing technologies and produces more natural sounding speech than traditional speech synthesis methods. The successful candidate will work on the components of a text-to-speech system that convert text into a phonetic representation, including part of speech tagging, word sense disambiguation and parsing for prosody. The duties of the position include applied research, software development, data collection, and transfer of developed technologies to product groups. Innovation in research, application of technology and a high level of motivation is the standard for all members of the team. The individual should possess a Ph.D. in the area of computational linguistics with a minimum of two years work experience developing spoken language systems. Strong programming skills in C or C++ are required. Knowledge of neural networks, decision trees, genetic algorithms, and statistical techniques is highly desirable. Please send resume and cover letter by June 15, 1996 to be considered for this position to Motorola Inc., Corporate Staffing Department, Attn: LP-T1521, 1303 E. Algonquin Rd., Schaumburg, IL 60196. Fax: 847-576-4959. Motorola is an equal opportunity/affirmative action employer. We welcome and encourage diversity in our workforce. From gds@sys.uea.ac.uk Wed May 22 10:20:58 1996 Received: from lucy.cs.wisc.edu (lucy.cs.wisc.edu [128.105.2.11]) by sea.cs.wisc.edu (8.6.12/8.6.12) with ESMTP id KAA26908 for ; Wed, 22 May 1996 10:20:40 -0500 Received: from TELNET-1.SRV.CS.CMU.EDU (TELNET-1.SRV.CS.CMU.EDU [128.2.254.108]) by lucy.cs.wisc.edu (8.6.12/8.6.12) with SMTP id KAA19980 for ; Wed, 22 May 1996 10:20:37 -0500 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id ae00792; 22 May 96 3:19:38 EDT Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id ae00790; 22 May 96 3:05:04 EDT Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa06229; 22 May 96 3:04:30 EDT Received: from RI.CMU.EDU by B.GP.CS.CMU.EDU id aa00637; 21 May 96 12:54:45 EDT Received: from s5.sys.uea.ac.uk by RI.CMU.EDU id aa05953; 21 May 96 12:52:24 EDT Received: from [139.222.4.80] (gds.sys.uea.ac.uk [139.222.4.80]) by s5.sys.uea.ac.uk (8.7.4/8.7.3) with SMTP id RAA17399; Tue, 21 May 1996 17:50:33 +0100 (BST) Date: Tue, 21 May 1996 17:50:33 +0100 (BST) X-Sender: gds@mail.sys.uea.ac.uk Message-Id: Mime-Version: 1.0 Content-Type: text/plain; charset="iso-8859-1" Content-Transfer-Encoding: quoted-printable To: connect-bb@ed.eusip, incon@dcs.shef.ac.uk, reinforce@cs.uwa.edu.au, gann-list@cs.iastate.edu, neuron-request@CATTELL.psych.upenn.edu, alife@cognet.ucla.edu, cogpsy@neuro.psy.soton.ac.uk, connectionists@cs.cmu.edu, epsynet%uhupvm1.BITNET@cunyvm.cuny.edu, elsnet-list@cogsci.ed.ac.uk, irl-net%irlearn.BITNET@cunyvm.cuny.edu, arpanet-bboards@edu.mit.lcs.mc, hybrid-list@cs.ua.edu, colt@cs.uiuc.edu, ilpnet@ijs.si, cphc-jobs@ukc.ac.uk From: George Smith Subject: ICANNGA97 ICANNGA97 _________ Third International Conference on Artificial Neural Networks and Genetic Algorithms Preceded by a one-day Introductory Workshop Tuesday 1st - Friday 4th April, 1997 Norwich, England, UK CALL FOR PAPERS AND INVITATION TO PARTICIPATE Conference Theme: _________________ The main theme of the ICCANGA series is the development and application of software paradigms based on natural processes, principally artificial neural networks, genetic algorithms and hybrids thereof. However, the scope of the conference extends to cover many related topics including fuzzy logic, genetic programming and other evolutionary computation systems, classifier systems and adaptive agent systems, distributed intelligence and artificial life, generic optimisation heuristics including simulated annealing and tabu search, and many more. Following the successes of ICANNGA93 (Innsbruck, Austria) and ICCANGA95 (Ales, France), the third meeting of this interdisciplinary conference will be held at the University of East Anglia in the picturesque, medieval city of Norwich, England. The ICANNGA series has quickly established itself as a platform, not only for established workers in the fields, but also for new and young researchers wishing to extend their knowledge and experience. The conference will be preceded by a one day workshop during which introductory sessions on a range of relevant topics will be held. There will be ample opportunity to gain practical experience in the techniques pertaining to the workshop and conference. The conference is hosted by the University of East Anglia, which is a campus university in a parkland setting, offering first class conference facilities including award winning en-suite accomodation and lecture theatres. The conference will include invited talks and contributed oral and poster presentations. It is expected that the ICANNGA97 Proceedings will be printed by Springer-Verlag (Vienna), following the tradition set by its predecessors. International Advisory Committee _________________________ _______ Prof. R. Albrecht, University of Innsbruck, Austria Dr. D. Pearson, Ecole des Mines d'Ales, France Prof. N. Steele, Coventry University, England (Chair) Dr. G. D. Smith, University of East Anglia, England Programme Committee ___________________ Thomas Baeck, Informatik Centrum, Dortmund, Germany Wilfried Brauer, TU Munchen, Germany Marco Dorigo, Universite Libre de Bruxelles, Belgium Terry Fogarty, University of West England, Bristol, UK Jelena Godjevac, EPFL Laboratories, Lausanne, Switzerland Michael Heiss, Neural Network Group, Siemens AG, Austria Tom Harris, Brunel University, London, UK Anne Johannet, EMA-EERIE, Nimes, France Helen Karatza, Aristotle University of Thessaloniki, Greece Sami Kuri, San Jose State University, USA Pedro Larranaga, University Basque Country, San Sebastian, Spain Francesco Masulli, University of Genoa, Italy Josef Mazanec, WU Wien, Austria Janine Magnier, EMA-EERIE, N=EEmes, France Franz Oppacher, Carleton University, Ottawa, Canada Ian Parmee, University of Plymouth, UK David Pearson, EMA-EERIE, N=EEmes, France Vic Rayward-Smith, University of East Anglia, Norwich, UK Colin Reeves, Coventry University, Coventry, UK Bernardete Ribeiro, Universidade de Coimbra, Portugal Valentina Salapura, TU-Wien, Austria V. David Sanchez A., University of Miami, Florida, USA Henrik Sax=E9n, =C5bo Akademi, Finland George D. Smith, University of East Anglia, Norwich, UK Nigel Steele, Coventry University, Coventry, UK Kevin Warwick, Reading University, Reading, UK Darrell Whitley, Colorado State University, USA Diethelm Wurtz, Swiss Federal Inst. of Technology, Zurich, Switzerland Organising Committee ____________________ Dr. G. D. Smith, University of East Anglia, England Nigel Steele, Coventry University, Coventry Prof. Vic Rayward-Smith, University of East Anglia, Norwich Submission Instructions _______________________ Contributions are sought in the following topic areas, which is not exhaustive: - Theoretical and Computational Aspects of Artificial Neural Networks: including computational learning, approximation theory, novel paradigms and training methods, dynamical systems, hardware implementation - Practical Applications of Artificial Neural Networks: including pattern recognition, speech and signal processing, visual processing, time series prediction, medical and other diagnostic systems, fault and anomaly detection, financial applications, data compression, datamining, machine learning - Theoretical and Computational Aspects of Genetic Algorithms: including schema theory developments, Markov models, convergence analysis, no free lunch theorem, computational analysis, novel sequential and parallel GA systems - Practical Applications of Genetic Algorithms; including function and combinatorial optimisation, machine learning, classifier and agent systems, datamining, real-world industrial and commercial applications - Hybrid and related topics: including genetic programming, evolutionary programming and evolution strategies, fuzzy logic and control, neuro-fuzzy systems, simulated annealing and tabu search, hybrid search algorithms, hybrid ANN/GA systems Authors should submit an extended abstract of around 1500-2000 words, or full paper, of their proposed contribution before 31st August 1996. Abstracts and papers must be in English and must contain a concise description of the problem, the results achieved, their relevance and a comparison with previous work. The abstract/paper should also contain the following details: Title Authors' names and affiliations Name, address and email address of contact author Keywords Three typed/printed copies should be sent to the following address: Dr George D. Smith School of Information Systems University of East Anglia Norwich, Norfolk, NR4 7TJ UK Alternatively, abstracts may be sent by email to either: gds@sys.uea.ac.uk or rs@sys.uea.ac.uk Notification of acceptance of the paper for presentation will be made by November 30th 1996. Papers accepted for both oral and poster presentations will be published in the Conference Proceedings. Pre-Conference Workshop _______________________ It is intended to hold a workshop on April 1st, 1997, prior to the Conference. This workshop is intended for those who are new to the topics and wish to gain a better understanding of the fundamental aspects of neural networks and genetic algorithms. The format of this workshop will be as follows: Theoretical issues of ANNs Key Issues in the application of ANNs Introduction to GAs and other heuristic search algorithms Key Issues in the application of GAs and related heuristics The second and fourth topics are backed up with laboratory sessions in which participants will have the opportunity to use some of the latest software toolkits supporting the respective technologies. Dates to remember: __________________ First Announcement & CFP: April/May 1996 Submission of Abstracts/Papers: August 31st 1996 Notification of Acceptance: November 30th 1996 Delivery of full paper: January 30th 1997 Pre-Conference Workshop: April 1st 1997 ICANNGA97: April 2nd-4th 1997 Further Information: ____________________ For more information on ICANNGA97, regularly updated, visit the WWW site at: http://www.sys.uea.ac.uk/Research/ResGroups/MAG/ICANNGA97/Default.html This web page also contains a pre-registration form. Pre-Registration form: ______________________ Please enter your details below to receive further information about ICANNGA97 and a full registration form. First name: ______________________________________ Family name: ______________________________________ Affiliation: ______________________________________ Address: ______________________________________ City: ______________________________________ State/Province/County: ______________________________________ ZIP/Postal Code: ______________________________________ Country: ______________________________________ Daytime telephone number: ______________________________________ Email address: ______________________________________ _________________________ _________________________ _________________________ Dr. George D Smith Computing Science Sector School of Information Systems University of East Anglia Norwich NR4 7TJ, UK Tel: + 44 (0)1603 593260 FAX: + 44 (0)1603 503344 Email: gds@sys.uea.ac.uk www: http://www.sys.uea.ac.uk/Teaching/Staff/gds.html From gds@sys.uea.ac.uk Wed May 22 11:26:29 1996 Received: from lucy.cs.wisc.edu (lucy.cs.wisc.edu [128.105.2.11]) by sea.cs.wisc.edu (8.6.12/8.6.12) with ESMTP id LAA27580 for ; Wed, 22 May 1996 11:26:04 -0500 Received: from cs.uwa.oz.au (bilby.cs.uwa.oz.au [130.95.1.11]) by lucy.cs.wisc.edu (8.6.12/8.6.12) with ESMTP id LAA20982 for ; Wed, 22 May 1996 11:26:00 -0500 Received: from (mafm@parma.cs.uwa.oz.au [130.95.1.7]) by cs.uwa.oz.au (8.6.8/8.5) with SMTP id TAA25075; Wed, 22 May 1996 19:39:22 +0800 Message-Id: <199605221139.TAA25075@cs.uwa.oz.au> From: gds@sys.uea.ac.uk (George Smith) To: connect-bb@ed.eusip, incon@dcs.shef.ac.uk, reinforce@cs.uwa.edu.au, gann-list@cs.iastate.edu, neuron-request@CATTELL.psych.upenn.edu, alife@cognet.ucla.edu, cogpsy@neuro.psy.soton.ac.uk, connectionists@cs.cmu.edu, epsynet%uhupvm1.BITNET@bitnet.mailgate.cs.mu.oz.au, elsnet-list@cogsci.ed.ac.uk, irl-net%irlearn.BITNET@bitnet.mailgate.cs.mu.oz.au, arpanet-bboards@edu.mit.lcs.mc, hybrid-list@cs.ua.edu, colt@cs.uiuc.edu, ilpnet@IJS.si, cphc-jobs@ukc.ac.uk Subject: ICANNGA97 [connectionists] Date: Tue, 21 May 1996 17:50:33 +0100 (BST) ICANNGA97 =3D=3D=3D=3D=3D=3D=3D=3D=3D Third International Conference on Artificial Neural Networks and Genetic Algorithms Preceded by a one-day Introductory Workshop Tuesday 1st - Friday 4th April, 1997 Norwich, England, UK CALL FOR PAPERS AND INVITATION TO PARTICIPATE Conference Theme: =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D The main theme of the ICCANGA series is the development and application of software paradigms based on natural processes, principally artificial neural networks, genetic algorithms and hybrids thereof. However, the scope of the conference extends to cover many related topics including fuzzy logic, genetic programming and other evolutionary computation systems, classifier systems and adaptive agent systems, distributed intelligence and artificial life, generic optimisation heuristics including simulated annealing and tabu search, and many more. =46ollowing the successes of ICANNGA93 (Innsbruck, Austria) and ICCANGA95 (Ales, France), the third meeting of this interdisciplinary conference will be held at the University of East Anglia in the picturesque, medieval city of Norwich, England. The ICANNGA series has quickly established itself as a platform, not only for established workers in the fields, but also for new and young researchers wishing to extend their knowledge and experience. The conference will be preceded by a one day workshop during which introductory sessions on a range of relevant topics will be held. There will be ample opportunity to gain practical experience in the techniques pertaining to the workshop and conference. The conference is hosted by the University of East Anglia, which is a campus university in a parkland setting, offering first class conference facilities including award winning en-suite accomodation and lecture theatres. The conference will include invited talks and contributed oral and poster presentations. It is expected that the ICANNGA97 Proceedings will be printed by Springer-Verlag (Vienna), following the tradition set by its predecessors. International Advisory Committee =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D Prof. R. Albrecht, University of Innsbruck, Austria Dr. D. Pearson, Ecole des Mines d'Ales, France Prof. N. Steele, Coventry University, England (Chair) Dr. G. D. Smith, University of East Anglia, England Programme Committee =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D Thomas Baeck, Informatik Centrum, Dortmund, Germany Wilfried Brauer, TU M=FCnchen, Germany Marco Dorigo, Universit=E9 Libre de Bruxelles, Belgium Terry Fogarty, University of West England, Bristol, UK Jelena Godjevac, EPFL Laboratories, Lausanne, Switzerland Michael Heiss, Neural Network Group, Siemens AG, Austria Tom Harris, Brunel University, London, UK Anne Johannet, EMA-EERIE, Nimes, France Helen Karatza, Aristotle University of Thessaloniki, Greece Sami Kuri, San Jose State University, USA Pedro Larranaga, University Basque Country, San Sebastian, Spain =46rancesco Masulli, University of Genoa, Italy Josef Mazanec, WU Wien, Austria Janine Magnier, EMA-EERIE, N=EEmes, France =46ranz Oppacher, Carleton University, Ottawa, Canada Ian Parmee, University of Plymouth, UK David Pearson, EMA-EERIE, N=EEmes, France Vic Rayward-Smith, University of East Anglia, Norwich, UK Colin Reeves, Coventry University, Coventry, UK Bernardete Ribeiro, Universidade de Coimbra, Portugal Valentina Salapura, TU-Wien, Austria V. David S=E1nchez A., University of Miami, Florida, USA Henrik Sax=E9n, =C5bo Akademi, Finland George D. Smith, University of East Anglia, Norwich, UK Nigel Steele, Coventry University, Coventry, UK Kevin Warwick, Reading University, Reading, UK Darrell Whitley, Colorado State University, USA Diethelm W=FCrtz, Swiss Federal Inst. of Technology, Z=FCrich, Switzerland Organising Committee =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D Dr. G. D. Smith, University of East Anglia, England Nigel Steele, Coventry University, Coventry Prof. Vic Rayward-Smith, University of East Anglia, Norwich Submission Instructions =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D Contributions are sought in the following topic areas, which is not exhausti= ve: - Theoretical and Computational Aspects of Artificial Neural Networks: including computational learning, approximation theory, novel paradigms and training methods, dynamical systems, hardware implementation - Practical Applications of Artificial Neural Networks: including pattern recognition, speech and signal processing, visual processing, time series prediction, medical and other diagnostic systems, fault and anomaly detection, financial applications, data compression, datamining, machine learning - Theoretical and Computational Aspects of Genetic Algorithms: including schema theory developments, Markov models, convergence analysis, no free lunch theorem, computational analysis, novel sequential and parallel GA systems - Practical Applications of Genetic Algorithms; including function and combinatorial optimisation, machine learning, classifier and agent systems, datamining, real-world industrial and commercial applications - Hybrid and related topics: including genetic programming, evolutionary programming and evolution strategies, fuzzy logic and control, neuro-fuzzy systems, simulated annealing and tabu search, hybrid search algorithms, hybrid ANN/GA systems Authors should submit an extended abstract of around 1500-2000 words, or full paper, of their proposed contribution before 31st August 1996. Abstracts and papers must be in English and must contain a concise description of the problem, the results achieved, their relevance and a comparison with previous work. The abstract/paper should also contain the following details: Title Authors' names and affiliations Name, address and email address of contact author Keywords Three typed/printed copies should be sent to the following address: Dr George D. Smith School of Information Systems University of East Anglia Norwich, Norfolk, NR4 7TJ UK Alternatively, abstracts may be sent by email to either: gds@sys.uea.ac.uk or rs@sys.uea.ac.uk Notification of acceptance of the paper for presentation will be made by November 30th 1996. Papers accepted for both oral and poster presentations will be published in the Conference Proceedings. Pre-Conference Workshop =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D It is intended to hold a workshop on April 1st, 1997, prior to the Conference. This workshop is intended for those who are new to the topics and wish to gain a better understanding of the fundamental aspects of neural networks and genetic algorithms. The format of this workshop will be as follows: Theoretical issues of ANNs Key Issues in the application of ANNs Introduction to GAs and other heuristic search algorithms Key Issues in the application of GAs and related heuristics The second and fourth topics are backed up with laboratory sessions in which participants will have the opportunity to use some of the latest software toolkits supporting the respective technologies. Dates to remember: =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D =46irst Announcement & CFP: April/May 1996 Submission of Abstracts/Papers: August 31st 1996 Notification of Acceptance: November 30th 1996 Delivery of full paper: January 30th 1997 Pre-Conference Workshop: April 1st 1997 ICANNGA97: April 2nd-4th 1997 =46urther Information: =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D =46or more information on ICANNGA97, regularly updated, visit the WWW site at: http://www.sys.uea.ac.uk/Research/ResGroups/MAG/ICANNGA97/Default.html This web page also contains a pre-registration form. Pre-Registration form: =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D Please enter your details below to receive further information about ICANNGA97 and a full registration form. =46irst name: ______________________________________ =46amily name: ______________________________________ Affiliation: ______________________________________ Address: ______________________________________ City: ______________________________________ State/Province/County: ______________________________________ ZIP/Postal Code: ______________________________________ Country: ______________________________________ Daytime telephone number: ______________________________________ Email address: ______________________________________ =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D Dr. George D Smith Computing Science Sector School of Information Systems University of East Anglia Norwich NR4 7TJ, UK Tel: + 44 (0)1603 593260 FAX: + 44 (0)1603 503344 Email: gds@sys.uea.ac.uk www: http://www.sys.uea.ac.uk/Teaching/Staff/gds.html =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D From ATAXR@ASUVM.INRE.ASU.EDU Wed May 22 12:12:15 1996 Received: from lucy.cs.wisc.edu (lucy.cs.wisc.edu [128.105.2.11]) by sea.cs.wisc.edu (8.6.12/8.6.12) with ESMTP id MAA27996 for ; Wed, 22 May 1996 12:12:09 -0500 Received: from cs.uwa.oz.au (bilby.cs.uwa.oz.au [130.95.1.11]) by lucy.cs.wisc.edu (8.6.12/8.6.12) with ESMTP id MAA21545 for ; Wed, 22 May 1996 12:12:05 -0500 Received: from (mafm@parma.cs.uwa.oz.au [130.95.1.7]) by cs.uwa.oz.au (8.6.8/8.5) with SMTP id TAA25068; Wed, 22 May 1996 19:37:22 +0800 Message-Id: <199605221137.TAA25068@cs.uwa.oz.au> From: Asim Roy To: reinforce@cs.uwa.edu.au Subject: Connectionist Learning - Some New Ideas/Questions Date: Mon, 20 May 1996 16:10:36 -0700 (MST) [This is for posting to your mailing list.] We have recently published a set of principles for learning in neural networks/connectionist models that is different from classical connectionist learning (Neural Networks, Vol. 8, No. 2; IEEE Transactions on Neural Networks, to appear; see references below). Below is a brief summary of the new learning theory and why we think classical connectionist learning, which is characterized by pre-defined nets, local learning laws and memoryless learning (no storing of training examples for learning), is not brain-like at all. Since vigorous and open debate is very healthy for a scientific field, we invite comments for and against our ideas from all sides. "A New Theory for Learning in Connectionist Models" We believe that a good rigorous theory for artificial neural networks/connectionist models should include learning methods that perform the following tasks or adhere to the following criteria: A. Perform Network Design Task: A neural network/connectionist learning method must be able to design an appropriate network for a given problem, since, in general, it is a task performed by the brain. A pre-designed net should not be provided to the method as part of its external input, since it never is an external input to the brain. From a neuroengineering and neuroscience point of view, this is an essential property for any "stand-alone" learning system - a system that is expected to learn "on its own" without any external design assistance. B. Robustness in Learning: The method must be robust so as not to have the local minima problem, the problems of oscillation and catastrophic forgetting, the problem of recall or lost memories and similar learning difficulties. Some people might argue that ordinary brains, and particularly those with learning disabilities, do exhibit such problems and that these learning requirements are the attributes only of a "super" brain. The goal of neuroengineers and neuroscientists is to design and build learning systems that are robust, reliable and powerful. They have no interest in creating weak and problematic learning devices that need constant attention and intervention. C. Quickness in Learning: The method must be quick in its learning and learn rapidly from only a few examples, much as humans do. For example, one which learns from only 10 examples learns faster than one which requires a 100 or a 1000 examples. We have shown that on-line learning (see references below), when not allowed to store training examples in memory, can be extremely slow in learning - that is, would require many more examples to learn a given task compared to methods that use memory to remember training examples. It is not desirable that a neural network/connectionist learning system be similar in characteristics to learners characterized by such sayings as "Told him a million times and he still doesn't understand." On-line learning systems must learn rapidly from only a few examples. D. Efficiency in Learning: The method must be computationally efficient in its learning when provided with a finite number of training examples (Minsky and Papert[1988]). It must be able to both design and train an appropriate net in polynomial time. That is, given P examples, the learning time (i.e. both design and training time) should be a polynomial function of P. This, again, is a critical computational property from a neuroengineering and neuroscience point of view. This property has its origins in the belief that biological systems (insects, birds for example) could not be solving NP-hard problems, especially when efficient, polynomial time learning methods can conceivably be designed and developed. E. Generalization in Learning: The method must be able to generalize reasonably well so that only a small amount of network resources is used. That is, it must try to design the smallest possible net, although it might not be able to do so every time. This must be an explicit part of the algorithm. This property is based on the notion that the brain could not be wasteful of its limited resources, so it must be trying to design the smallest possible net for every task. General Comments This theory defines algorithmic characteristics that are obviously much more brain-like than those of classical connectionist theory, which is characterized by pre-defined nets, local learning laws and memoryless learning (no storing of actual training examples for learning). Judging by the above characteristics, classical connectionist learning is not very powerful or robust. First of all, it does not even address the issue of network design, a task that should be central to any neural network/connectionist learning theory. It is also plagued by efficiency (lack of polynomial time complexity, need for excessive number of teaching examples) and robustness problems (local minima, oscillation, catastrophic forgetting, lost memories), problems that are partly acquired from its attempt to learn without using memory. Classical connectionist learning, therefore, is not very brain-like at all. As far as I know, there is no biological evidence for any of the premises of classical connectionist learning. Without having to reach into biology, simple common sense arguments can show that the ideas of local learning, memoryless learning and predefined nets are impractical even for the brain! For example, the idea of local learning requires a predefined network. Classical connectionist learning forgot to ask a very fundamental question - who designs the net for the brain? The answer is very simple: Who else, but the brain itself! So, who should construct the net for a neural net algorithm? The answer again is very simple: Who else, but the algorithm itself! (By the way, this is not a criticism of constructive algorithms that do design nets.) Under classical connectionist learning, a net has to be constructed (by someone, somehow - but not by the algorithm!) prior to having seen a single training example! I cannot imagine any system, biological or otherwise, being able to construct a net with zero information about the problem to be solved and with no knowledge of the complexity of the problem. (Again, this is not a criticism of constructive algorithms.) A good test for a so-called "brain-like" algorithm is to imagine it actually being part of a human brain. Then examine the learning phenomenon of the algorithm and compare it with that of the human's. For example, pose the following question: If an algorithm like back propagation is "planted" in the brain, how will it behave? Will it be similar to human behavior in every way? Look at the following simple "model/algorithm" phenomenon when the back- propagation algorithm is "fitted" to a human brain. You give it a few learning examples for a simple problem and after a while this "back prop fitted" brain says: "I am stuck in a local minimum. I need to relearn this problem. Start over again." And you ask: "Which examples should I go over again?" And this "back prop fitted" brain replies: "You need to go over all of them. I don't remember anything you told me." So you go over the teaching examples again. And let's say it gets stuck in a local minimum again and, as usual, does not remember any of the past examples. So you provide the teaching examples again and this process is repeated a few times until it learns properly. The obvious questions are as follows: Is "not remembering" any of the learning examples a brain- like phenomenon? Are the interactions with this so-called "brain- like" algorithm similar to what one would actually encounter with a human in a similar situation? If the interactions are not similar, then the algorithm is not brain-like. A so-called brain-like algorithm's interactions with the external world/teacher cannot be different from that of the human. In the context of this example, it should be noted that storing/remembering relevant facts and examples is very much a natural part of the human learning process. Without the ability to store and recall facts/information and discuss, compare and argue about them, our ability to learn would be in serious jeopardy. Information storage facilitates mental comparison of facts and information and is an integral part of rapid and efficient learning. It is not biologically justified when "brain-like" algorithms disallow usage of memory to store relevant information. Another typical phenomenon of classical connectionist learning is the "external tweaking" of algorithms. How many times do we "externally tweak" the brain (e.g. adjust the net, try a different parameter setting) for it to learn? Interactions with a brain-like algorithm has to be brain-like indeed in all respect. The learning scheme postulated above does not specify how learning is to take place - that is, whether memory is to be used or not to store training examples for learning, or whether learning is to be through local learning at each node in the net or through some global mechanism. It merely defines broad computational characteristics and tasks (i.e. fundamental learning principles) that are brain-like and that all neural network/connectionist algorithms should follow. But there is complete freedom otherwise in designing the algorithms themselves. We have shown that robust, reliable learning algorithms can indeed be developed that satisfy these learning principles (see references below). Many constructive algorithms satisfy many of the learning principles defined above. They can, perhaps, be modified to satisfy all of the learning principles. The learning theory above defines computational and learning characteristics that have always been desired by the neural network/connectionist field. It is difficult to argue that these characteristics are not "desirable," especially for self-learning, self- contained systems. For neuroscientists and neuroengineers, it should open the door to development of brain-like systems they have always wanted - those that can learn on their own without any external intervention or assistance, much like the brain. It essentially tries to redefine the nature of algorithms considered to be brain- like. And it defines the foundations for developing truly self- learning systems - ones that wouldn't require constant intervention and tweaking by external agents (human experts) for it to learn. It is perhaps time to reexamine the foundations of the neural network/connectionist field. This mailing list/newsletter provides an excellent opportunity for participation by all concerned throughout the world. I am looking forward to a lively debate on these matters. That is how a scientific field makes real progress. Asim Roy Arizona State University Tempe, Arizona 85287-3606, USA Email: ataxr@asuvm.inre.asu.edu References 1. Roy, A., Govil, S. & Miranda, R. 1995. A Neural Network Learning Theory and a Polynomial Time RBF Algorithm. IEEE Transactions on Neural Networks, to appear. 2. Roy, A., Govil, S. & Miranda, R. 1995. An Algorithm to Generate Radial Basis Function (RBF)-like Nets for Classification Problems. Neural Networks, Vol. 8, No. 2, pp. 179-202. 3. Roy, A., Kim, L.S. & Mukhopadhyay, S. 1993. A Polynomial Time Algorithm for the Construction and Training of a Class of Multilayer Perceptrons. Neural Networks, Vol. 6, No. 4, pp. 535- 545. 4. Mukhopadhyay, S., Roy, A., Kim, L.S. & Govil, S. 1993. A Polynomial Time Algorithm for Generating Neural Networks for Pattern Classification - its Stability Properties and Some Test Results. Neural Computation, Vol. 5, No. 2, pp. 225-238. From juergen@idsia.ch Thu May 23 05:34:15 1996 Received: from lucy.cs.wisc.edu (lucy.cs.wisc.edu [128.105.2.11]) by sea.cs.wisc.edu (8.6.12/8.6.12) with ESMTP id FAA11223 for ; Thu, 23 May 1996 05:34:06 -0500 Received: from TELNET-1.SRV.CS.CMU.EDU (TELNET-1.SRV.CS.CMU.EDU [128.2.254.108]) by lucy.cs.wisc.edu (8.6.12/8.6.12) with SMTP id FAA03659 for ; Thu, 23 May 1996 05:34:04 -0500 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id aa00792; 22 May 96 3:15:50 EDT Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa00790; 22 May 96 3:04:38 EDT Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa06213; 22 May 96 3:04:15 EDT Received: from CS.CMU.EDU by B.GP.CS.CMU.EDU id aa07897; 20 May 96 3:11:13 EDT Received: from fava.idsia.ch by CS.CMU.EDU id aa03922; 20 May 96 3:10:40 EDT Received: from kraut.idsia.ch by fava.idsia.ch (4.1/SMI-4.1) id AA13354; Mon, 20 May 96 09:10:26 +0200 Date: Mon, 20 May 96 09:10:26 +0200 From: Juergen Schmidhuber Message-Id: <9605200710.AA13354@fava.idsia.ch> To: connectionists@cs.cmu.edu Subject: R Richard Long writes: There may be another reason for the brain to construct networks that are 'minimal' having to do with Chaitin and Kolmogorov computational complexity. If a minimal network corresponds to a 'minimal algorithm' for implementing a particular computation, then that particular network must utilize all of the symmetries and regularities contained in the problem, or else these symmetries could be used to reduce the network further. Chaitin has shown that no algorithm for finding this minimal algorithm in the general case is possible. However, if an evolutionary programming method is used in which the fitness function is both 'solves the problem' and 'smallest size' (i.e. Occam's razor), then it is possible that the symmetries and regularities in the problem would be extracted as smaller and smaller networks are found. I would argue that such networks would compute the solution less by rote or brute force, and more from a deep understanding of the problem. I would like to hear anyone else's thoughts on this. Some comments: Apparently, Kolmogorov was the first to show the impossibility of finding the minimal algorithm in the general case (but Solomonoff also mentions it in his early work). The reason is the halting problem, of course - you don't know the runtime of the minimal algorithm. For all practical applications, runtime has to be taken into account. Interestingly, there is an ``optimal'' way of doing this, namely Levin's universal search algorithm, which tests solution candidates in order of their Levin complexities: L. A. Levin. Universal sequential search problems, Problems of Information Transmission 9:3,265-266,1973. For finding Occam's razor neural networks with minimal Levin complexity, see J. Schmidhuber: Discovering solutions with low Kolmogorov complexity and high generalization capability. In A.Prieditis and S.Russell, editors, Machine Learning: Proceedings of the 12th International Conference, 488--496. Morgan Kaufmann Publishers, San Francisco, CA, 1995. For Occam's razor solutions of non-Markovian reinforcement learning tasks, see M. Wiering and J. Schmidhuber: Solving POMDPs using Levin search and EIRA. In Machine Learning: Proceedings of the 13th International Conference. Morgan Kaufmann Publishers, San Francisco, CA, 1996, to appear. --- Juergen Schmidhuber, IDSIA http://www.idsia.ch/~juergen From cabestan@petrus.upc.es Thu May 23 05:34:18 1996 Received: from lucy.cs.wisc.edu (lucy.cs.wisc.edu [128.105.2.11]) by sea.cs.wisc.edu (8.6.12/8.6.12) with ESMTP id FAA11226 for ; Thu, 23 May 1996 05:34:10 -0500 Received: from TELNET-1.SRV.CS.CMU.EDU (TELNET-1.SRV.CS.CMU.EDU [128.2.254.108]) by lucy.cs.wisc.edu (8.6.12/8.6.12) with SMTP id FAA03661 for ; Thu, 23 May 1996 05:34:08 -0500 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id aa01899; 23 May 96 1:41:04 EDT Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa01897; 23 May 96 1:30:36 EDT Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa07102; 23 May 96 1:29:38 EDT Date: Wed, 22 May 1996 17:01:52 UTC+0200 From: JOAN CABESTANY To: "'iwann-list@eel.upc.es'" Message-ID: <01BB4800.52349C00@maripili.upc.es> Subject: IWANN'97 preliminary announce MIME-Version: 1.0 (Generated by Ean X.400 to MIME gateway) Content-Type: text/plain; charset="iso-8859-1" Content-Transfer-Encoding: quoted-printable This message has been sent to several lists of distribution. I apologize its multiple reception. Thank you. Preliminary Announcement and First Call for Papers IWANN'97 INTERNATIONAL WORK-CONFERENCE ON ARTIFICIAL AND NATURAL NEURAL NETWORKS Biological and Artificial Architectures, Technologies and Applications Lanzarote - Canary Islands, Spain June 4-6, 1997 Contact URL http://petrus.upc.es/iwann97.html for an on line information. ORGANIZED BY Universidad Nacional de Educacion a Distancia (UNED), Madrid Universidad de Las Palmas de Gran Canarias=20 Universidad Politecnica de Catalunya Universidad de Malaga Universidad de Granada IWANN'97. The fourth International Workshop on Artificial Neural Networks, now changed to International Work-Conference on Artificial and Natural Neural Networks, will take place in Lanzarote, Canary Islands (Spain) from 4 to 6 of June, 1997. This biennial meeting with focus on biologically inspired and more realistic models of natural neurons and neural nets and new hybrid computing paradigms, was first held in Granada (1991), Sitges (1993) and Torremolinos, Malaga (1995) with a growing number of participants from more than 20 countries and with high quality papers published by Springer-Verlag (LNCS 540, 686 and 930). SCOPE Neural computation is considered here in the dual perspective of analysis (as science) and synthesis (as engineering). As a science of analysis, neural computation seeks to help neurology, brain theory, and cognitive psychology in the understanding of the functioning of the Nervous Systems by means of computational models of neurons, neural nets and subcelular processes, with the possibility of using electronics and computers as a "laboratory" in which cognitive processes can be simulated and hypothesis proven without having to act directly upon living beings. As a synthesis engineering, neural computation seeks to complement the symbolic perspective of Artificial Intelligence (AI), using the biologically inspired models of distributed, self-programming and self-organizing networks, to solve those non-algorithmic problems of function approximation and pattern classification having to do with changing and only partially known environments. Fault tolerance and dynamic reconfiguration are other basic advantages of neural nets. In the sea of meetings, congresses and workshops on ANN's, IWANN'97 focus on the three subjects that most worry us: (1) The seeking of biologically inspired new models of local computation architectures and learning along with the organizational principles behind of the complexity of intelligent behavior. (2) The searching for some methodological contributions in the analysis and design of knowledge-based ANN's, instead of "blind nets", and in the reduction of the knowledge level to the sub-symbolic implementation level. (3) The cooperation with symbolic AI, with the integration of connectionist and symbolic processing in hybrid and multi-strategy approaches for perception, decision and control tasks, as well as for case-based reasoning, concepts formation and learning. To contribute in the posing and partially solving of these global topics, IWANN'97 offer a brain-storming interdisciplinary forum in advanced Neural Computation for scientists and engineers from biology neuroanatomy, computational neurophysiology, molecular biology, biophysics, linguistics, psychology, mathematics and physics, computer science, artificial intelligence, parallel computing, analog and digital electronics, advanced computer architectures, reverse engineering, cognitive sciences and all the concerned applied domains (sensory systems and signal processing, monitoring, diagnosis, classification and decision making, intelligent control and supervision, perceptual robotics and communication systems). Contributions on the following and related topics are welcome. TOPICS 1. Biological Foundations of Neural Computation: Principles of brain organization. Neuroanatomy and Neurophysiological of synapses, dendro-dendritic contacts, neurons and neural nets in peripheral and central areas. Plasticity, learning and memory in natural neural nets. Models of development and evolution. The computational perspective in Neuroscience. 2. Formal Tools and Computational Models of Neurons and Neural Nets Architectures: Analytic and logic models. Object oriented formulations. Hybrid knowledge representation and inference tools (rules and frames with analytic slots). Probabilistic, bayesian and fuzzy models. Energy related models. 3. Plasticity Phenomena (Maturing, Learning and Memory): Biological mechanisms of learning and memory. Computational formulations using correlational, reinforcement and minimization strategies. Conditioned reflex and associative mechanisms. Inductive-deductive and abductive symbolic-subsymbolic formulations. Generalization. 4. Complex Systems Dynamics: Self-organization, cooperative processes, autopoiesis, emergent computation, synergetic, evolutive optimization and genetic algorithms. Self-reproducing nets. Self-organizing feature maps. Simulated evolution. Social organization phenomena. 5. Cognitive Science and IA: Hybrid knowledge based system. Neural networks for knowledge modeling, acquisition and refinement. Natural language understanding. Concepts formation. Spatial and temporal planning and scheduling. Intentionality. 6. Neural Nets Simulation, Emulation and Implementation: Environments and languages. Parallelization, modularity and autonomy. New hardware implementation strategies (FPGA's, VLSI, neurodevices). Evolutive architectures. Real systems validation and evaluation. 7. Methodology for Data Analysis, Task Selection and Nets Design. 8. Neural Networks for Perception: Biologically inspired preprocessing. Low level processing, source separation, sensor fusion, segmentation, feature extraction, adaptive filtering, noise reduction, texture, stereo correspondence, motion analysis, speech recognition, artificial vision, and hybrid architectures for multisensorial perception. 9. Neural Networks for Communications Systems: Modems and codecs, network management, digital communications. 10. Neural Networks for Control and Robotics: Systems identification, motion planning and control, adaptive, predictive and model-based control systems, navigation, real time applications, visuo-motor coordination. LOCATION BEATRIZ Hotel Lanzarote - Canary Islands, June 4-6, 1997 Lanzarote, the most northerly and easterly island of the Canarian archipelago, is at the same time the most unusual one and produces a strange fascination on those who visit it because the fast succession of fire, sea and colors contrasts with craters, green valleys and unforgettable golden and warm beaches. LANGUAGE English will be the official language of IWANN'97. Simultaneous translation will not be provided. CALL FOR PAPERS The Programme Committee seeks for original papers on the above mentioned Topics. Authors should pay special attention to explanation of theoretical and technical choices involved, point out possible limitations and describe the current state of their work.=20 All received papers will be reviewed by the Programme Committee. Accepted papers may be presented orally or as poster panels, however all accepted contributions will be published in full length (Springer-Verlag Proceedings are expected). INSTRUCTIONS TO AUTHORS Five copies (one original and four copies) of the paper must be submitted. The paper must not exceed 10 pages, including figures, tables and references. It should be written in English on A4 paper, in a Roman font, 12 point in size, without page numbers. If possible, please make use of the latex/plaintex style file available in the WWW page: http://petrus.upc.es/iwann97.html . In addition, one sheet must be attached including: Title and authors names, list of five keywords, the Topic the paper fits best, preferred presentation (oral or poster) and the corresponding author (name, postal and e-mail address, phone and fax numbers). CONTRIBUTIONS MUST BE SENT TO: Prof. Jose Mira Dpto. Informatica y Automatica, UNED Senda del Rey, s/n Phone: + 34 1 3987155 E- 28040 MADRID, Spain Fax: + 34 1 3986697 IMPORTANT DATES Second and Final Call for Papers September 1996 Final Date for Submission January 15, 1997 Notification of Acceptance March 1997 Workshop June 4-6, 1997 STEARING COMMITTEE Prof. Joan Cabestany , Universidad Politecnica de Catalunya (E) Prof. Jose Mira Mira, UNED (E) Prof. Alberto Prieto, Universidad de Granada (E) Prof. Francisco Sandoval, Universidad de Malaga (E) TENTATIVE ORGANIZATION COMMITTEE Michael Arbit, University of Southern California (USA) Senen Barro, Universidad de Santiago (E) Trevor Clarkson, King's College London (UK) Ana Delgado, UNED (E) Dante DelCorso, Politecnico de Torino (I) Tamas D. Gedeon, University of New South Wales (AUS) Karl Goser, Universit=E4t Dortmund (G) Jeanny Herault, Institute National Polytechnique de Grenoble (F) Jaap Hoekstra, Delft University of Technology (NL) Roberto Moreno, Universidad de las Palmas de Gran Canaria (E) Shunsuke Sato, Osaka University (Jp) Igor Shevelev, Russian Academy of Science(R) Cloe Taddei-Ferretti, Istituto di Cibernetica, CNR (I) Marley Vellasco, Pontificia Universidade Catolica do Rio de Janeiro (Br) Michel Verleysen, Universite Catholique de Louvain-la-Neuve (B) From nq6@columbia.edu Fri May 24 06:27:06 1996 Received: from lucy.cs.wisc.edu (lucy.cs.wisc.edu [128.105.2.11]) by sea.cs.wisc.edu (8.6.12/8.6.12) with ESMTP id GAA01089 for ; Fri, 24 May 1996 06:26:56 -0500 Received: from TELNET-1.SRV.CS.CMU.EDU (TELNET-1.SRV.CS.CMU.EDU [128.2.254.108]) by lucy.cs.wisc.edu (8.6.12/8.6.12) with SMTP id GAA19046 for ; Fri, 24 May 1996 06:26:54 -0500 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id ab02505; 23 May 96 13:06:27 EDT Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id ab02503; 23 May 96 12:50:44 EDT Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa07544; 23 May 96 12:50:27 EDT Received: from RI.CMU.EDU by B.GP.CS.CMU.EDU id aa05211; 23 May 96 11:01:10 EDT Received: from konichiwa.cc.columbia.edu by RI.CMU.EDU id aa16029; 23 May 96 11:00:12 EDT Received: (from nq6@localhost) by konichiwa.cc.columbia.edu (8.7.5/8.7.3) id KAA01297; Thu, 23 May 1996 10:59:23 -0400 (EDT) Date: Thu, 23 May 1996 10:59:23 -0400 (EDT) From: Ning Qian Message-Id: <199605231459.KAA01297@konichiwa.cc.columbia.edu> To: Connectionists@cs.cmu.edu Subject: Papers available: disparity tuning and motion-stereo integration Reply-to: nq6@columbia.edu The following two papers on disparity tuning of binocular cells and on motion-stereo integration are available from our WWW homepage at: http://brahms.cpmc.columbia.edu ----------------------------------------------------------------------- Binocular receptive field models, disparity tuning, and characteristic disparity Yudong Zhu and Ning Qian Columbia University (To appear in Neural Computation) Disparity tuning of visual cells in the brain depends on the structure of their binocular receptive fields (RFs). Freeman and coworkers have found that binocular RFs of a typical simple cell can be quantitatively described by two Gabor functions with the same Gaussian envelope but different phase parameters in the sinusoidal modulations \cite{Freeman90}. This phase-parameter based RF description, however, has recently been questioned by \citeasnoun{Wagner93} based on their identification of a so-called characteristic disparity (CD) in some cells' disparity tuning curves. They concluded that their data favor the traditional binocular RF model which assumes an overall positional shift between a cell's left and right RFs. Here we set to resolve this issue by studying the dependence of cells' disparity tuning on their underlying RF structures through mathematical analyses and computer simulations. We model the disparity tuning curves in Wagner and Frost's experiments and demonstrate that the mere existence of approximate CDs in real cells cannot be used to distinguish the phase-parameter based RF description from the traditional position-shift based RF description. Specifically, we found that model simple cells with either type of RF description do not have a CD. Model complex cells with the position-shift based RF description have a precise CD, and those with the phase-parameter based RF description have an approximate CD. We also suggest methods for correctly distinguishing the two types of RF descriptions. A hybrid of the two RF models may be required to fit the behavior of some real cells and we show how to determine the relative contributions of the two RF models. This paper is also available from NEUROPROSE: FTP-host: archive.cis.ohio-state.edu FTP-filename: /pub/neuroprose/qian.cd.ps.Z ....................................................................... A Physiological Model for Motion-stereo Integration and a Unified Explanation of the Pulfrich-like Phenomena Ning Qian and Richard A. Andersen Columbia University and Caltech (To appear in Vision Research) Many psychophysical and physiological experiments indicate that visual motion analysis and stereoscopic depth perception are processed together in the brain. However, little computational effort has been devoted to combining these two visual modalities into a common framework based on physiological mechanisms. We present such an integrated model in this paper. We have previously developed a physiologically realistic model for binocular disparity computation \cite{Qian94e}. Here we demonstrate that under some general and physiological assumptions, our stereo vision model can be combined naturally with motion energy models to achieve motion-stereo integration. The integrated model may be used to explain a wide range of experimental observations regarding motion-stereo interaction. As an example, we show that the model can provide a unified account of the classical Pulfrich effect \cite{Morgan75} and the generalized Pulfrich phenomena to dynamic noise patterns \cite{Tyler74,Falk80} and stroboscopic stimuli \cite{Burr79}. ----------------------------------------------------------------------- From carmesin@schoner.physik.uni-bremen.de Fri May 24 06:27:07 1996 Received: from lucy.cs.wisc.edu (lucy.cs.wisc.edu [128.105.2.11]) by sea.cs.wisc.edu (8.6.12/8.6.12) with ESMTP id GAA01087 for ; Fri, 24 May 1996 06:26:55 -0500 Received: from TELNET-1.SRV.CS.CMU.EDU (TELNET-1.SRV.CS.CMU.EDU [128.2.254.108]) by lucy.cs.wisc.edu (8.6.12/8.6.12) with SMTP id GAA19044 for ; Fri, 24 May 1996 06:26:52 -0500 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id aa02505; 23 May 96 13:05:14 EDT Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa02503; 23 May 96 12:50:39 EDT Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa07539; 23 May 96 12:50:02 EDT Received: from CS.CMU.EDU by B.GP.CS.CMU.EDU id ab04358; 23 May 96 9:48:45 EDT Received: from [134.102.160.50] by CS.CMU.EDU id aa04275; 23 May 96 9:47:56 EDT Received: (from carmesin@localhost) by schoner.physik.uni-bremen.de (8.7.1/8.7.1) id PAA12918; Thu, 23 May 1996 15:49:18 +0200 Date: Thu, 23 May 1996 15:49:18 +0200 Message-Id: <199605231349.PAA12918@schoner.physik.uni-bremen.de> From: Hans-Otto Carmesin To: Connectionists@cs.cmu.edu Subject: BOOK: Neuronal Adaptation Theory The new book NEURONAL ADAPTATION THEORY is now available. ISBN 3-631-30039-5, US-ISBN 0-8204-3172-9 AUTHOR: Hans-Otto Carmesin, Institute for Theoretical Physics, University Bremen, 28334 Bremen, Germany, Fax 0421 218 4869, email: carmesin@theo.physik.uni-bremen.de, www: http://schoner.physik.uni-bremen.de/~carmesin/ PUBLISHER: Peter Lang, Frankfurt/M., Berlin, Bern, New York, Paris, Wien; ---> ---> Please send your order to: Peter Lang GmbH, Europischer Verlag der Wissenschaften, Abteilung WB, Box 940225, 60460 Frankfurt/M., Germany PRICE: 59DM; PAGES: 236 (23x16cm), num.fig. FEATURES: The book includes 29 exercises with solutions, 43 essential ideas, 108 partially coloured figures, experiment explanations and general theorems. ABSTRACT: The human genotype represents at most ten billion pieces of binary information, whereas the human brain contains more than a million times a billion synapses. So a differentiated brain structure is due to synaptic self-organization and adaptation. The goal is to model the formation of observed global brain structures and cognitive properties from local synaptic dynamics sometimes supervised by the limbic system. A general neuro-synaptic dynamics is solved with a novel field theory in a comprehensible manner and in quantitative agreement with many observations. Novel results concern for instance thermal membrane fluctuations, fluctuation dissipation theorems, cortical maps, topological charges, operant conditioning, transitive inference, learning hidden structures, behaviourism, attention focus, Wittgenstein paradox, infinite generalization, schizophrenia dynamics, perception dynamics, non-equilibrium phase transitions, emergent valuation. Also the formation of advanced cognitive properties is modeled. CONTENTS: 1 Introduction 13 1.1 The role of theory 13 2 Neuronal Association Patterns 17 2.1 Classical conditioning 17 2.2 Typical nerve cell 18 2.3 Neuronal dynamics 20 2.3.1 Two-valued neurons 20 2.3.2 Two alternative formulations 21 2.4 Coupling dynamics 23 2.4.1 Usage dependent couplings 25 2.4.2 Neuronal activity patterns 25 2.5 Network model for classical conditioning 29 2.6 Pattern recognition 32 2.6.1 Task 32 2.6.2 One pattern 32 2.6.3 Several patterns 34 2.7 Pattern retrieval with stochastic dynamics 39 2.7.1 Dynamical equilibrium for a single neuron 40 2.7.2 Dynamical equilibrium for configurations 40 2.8 A physiological basis of stochastic dynamics 44 2.8.1 Biophysics of action potentials 44 2.8.2 Spherical capacitor cell model 45 2.8.3 Nyquist formula 46 2.8.4 Thermodynamic membrane potential fluctuations 50 2.8.5 Resulting stochastic neuronal dynamics 51 2.8.6 Discussion 53 2.9 Pattern retrieval with effectively continuous time 53 2.9.1 Continuous spike response function 54 2.9.2 Network model 54 2.9.3 Model analysis 55 2.10 Discussion of chapter 2 58 3 Self-Organizing Networks 60 3.1 Basic principle 61 3.2 Retinotopy as model system 61 3.3 General two-valued neuron coupling rules 63 3.3.1 Locality principle 63 3.3.2 Additive membrane potential rule, AMPR 64 3.3.3 Coupling transfer rule, CTR 64 3.3.4 Local linear coupling dynamics, LLCD 65 3.3.5 Limited neuronal couplings, LNCR 65 3.4 A 1D self-organizing network with Hebb-rule 65 3.4.1 Network architecture 65 3.4.2 Coupling dynamics 67 3.4.3 Transformed couplings 68 3.4.4 Single stimulation potential 68 3.5 Field theory of neurosynaptic dynamics 69 3.5.1 A general solution method 69 3.5.2 Ergodicity 69 3.5.3 Neurosynaptic states and transitions 70 3.5.4 Averaged neurosynaptic change field 70 3.5.5 Differential equation for neurosynaptic change field 71 3.5.6 Adiabatic principle 71 3.5.7 Differential equation for synaptic change field 72 3.5.8 Change potential field 73 3.5.9 Fluctuation dissipation theorems 77 3.5.10 Discussion 81 3.6 Field theory of topology preservation 81 3.6.1 Emergence of an injective mapping 81 3.6.2 Single neuron separation 83 3.6.3 Coincidence stabilization 84 3.6.4 Emergence of 1D topology preservation 86 3.6.5 Emergence of clusters and topology preservation 87 3.6.6 Discussion 92 3.7 Field theory of orientation preference emergence 92 3.7.1 Network model 92 3.7.2 Change potentials 94 3.7.3 Potential minima 95 3.7.4 Discussion 97 3.8 Field theory of orientation pattern emergence 98 3.8.1 Phenomenon of pinwheel structures 98 3.8.2 Network model 98 3.8.3 Effective iso-orientation interaction 100 3.8.4 Continuous orientation interaction 101 3.8.5 Orientation fluctuations 102 3.8.6 Instability of the ground state 103 3.8.7 Topological singularities according to the Poisson equation 104 3.8.8 Greens function solution 106 3.8.9 Energy of a planar system of charges 108 3.8.10 Prediction: Plasma phase transition 109 3.9 Overview for formal temperatures 110 3.10 Discussion of chapter 3 111 4 Supervised & Self-Organized Adaptation 113 4.1 Forms of supervised adaptation 113 4.2 Operant conditioning 114 4.2.1 The phenomenon of transitive inference 114 4.2.2 Network model 116 4.2.3 Analysis of the network model 117 4.2.4 Transitive inference 119 4.2.5 Symbolic distance effect 119 4.2.6 Network parameters for various species 121 4.3 Generalized quantitative dynamical analysis 122 4.3.1 General valuation dynamics 122 4.3.2 Transitive inference with general valuation dynamics 123 4.3.3 Necessary and sufficient conditions for learning the Piaget task 123 4.3.4 Transitive inference as a consequence of successful learning 124 4.3.5 General set of tasks 125 4.3.6 Network model with minimization of complexity 126 4.3.7 Complete neurosynaptic dynamics and empirical data 127 4.3.8 Discussion of operant conditioning 130 4.4 Supervised Hebb-rule 131 4.4.1 Network model 131 4.4.2 Network analysis 131 4.4.3 Discussion on convergence with Hebb-rules 134 4.5 Perceptron 134 4.5.1 Network and task definition 134 4.5.2 Network architecture capabilities 135 4.5.3 Perceptron convergence theorem 136 4.6 Discussion of chapter 4 137 5 Advanced Adaptations 138 5.1 Learning of charges 139 5.1.1 An especially simple experiment 140 5.1.2 Necessary inner neurons 141 5.1.3 Definition of frameworks 141 5.1.4 Network model 142 5.1.5 Analysis of the network model 143 5.1.6 Discussion 146 5.2 Attention 147 5.2.1 Network model with attention 148 5.2.2 Potential field theorem 148 5.2.3 Attentional learning of charges 150 5.2.4 Attentional adaptation convergence theorem 151 5.2.5 Emergence of network architectures 153 5.2.6 Generalized perceptron 153 5.2.7 Neuronal dynamics with signum function 155 5.2.8 Discussion 156 5.3 Reversal 156 5.3.1 A reversal experiment 157 5.3.2 Network model 157 5.3.3 Discussion of reversal 159 5.4 Learning of counting 159 5.4.1 Generalization without limitation 159 5.4.2 Network architecture and dynamics 160 5.4.3 Analysis of the network 161 5.4.4 An instructive network model 162 5.4.5 Advanced network dynamics 164 5.4.6 Analysis of the advanced network model 165 5.4.7 A solution of Wittgenstein's paradox 166 5.4.8 Discussion 168 5.5 Convergence theorem for inner feedback 168 5.5.1 Idea of adaptation via short dimension increase 169 5.5.2 Specification of the learning situation 170 5.5.3 Learning algorithm for inner feedback 171 5.5.4 Convergence theorem 174 5.5.5 Optimal correspondence via short dimension increase 177 5.5.6 Generalizations 178 5.5.7 Discussion 179 5.6 Correspondence deficit compensation: Schizophrenia model? 180 5.6.1 Starting point 180 5.6.2 Network model 181 5.6.3 Network characteristics 182 5.6.4 Transfer to schizophrenia 185 5.6.5 Therapy 187 5.6.6 Empirical findings 188 5.6.7 Discussion 194 5.7 A mesoscopic perception model 195 5.7.1 External stimulations 195 5.7.2 Network model 197 5.7.3 Field theoretic solution of the network 201 5.7.4 Modeling phenomena 203 5.7.5 Discussion 210 5.8 Emergent valuation 211 5.8.1 Emergence of a valuating field 211 5.8.2 Effect of a valuating stimulation 213 5.9 General adaptation dynamics 214 5.9.1 Definition of microscopic dynamics 214 5.9.2 Resulting macroscopic dynamics 216 5.9.3 Some special cases 218 5.10 Discussion of chapter 5 219 5.11 No Laplace demon 220 6 Summary 221 6.1 Overview 221 6.2 Predictions 222 6.3 List of ideas 224 6.4 Open questions 225 From trevor@mallet.Stanford.EDU Fri May 24 06:27:08 1996 Received: from lucy.cs.wisc.edu (lucy.cs.wisc.edu [128.105.2.11]) by sea.cs.wisc.edu (8.6.12/8.6.12) with ESMTP id GAA01093 for ; Fri, 24 May 1996 06:26:59 -0500 Received: from TELNET-1.SRV.CS.CMU.EDU (TELNET-1.SRV.CS.CMU.EDU [128.2.254.108]) by lucy.cs.wisc.edu (8.6.12/8.6.12) with SMTP id GAA19048 for ; Fri, 24 May 1996 06:26:56 -0500 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id aa02518; 23 May 96 13:17:51 EDT Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa02507; 23 May 96 12:51:42 EDT Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa07550; 23 May 96 12:51:17 EDT Received: from RI.CMU.EDU by B.GP.CS.CMU.EDU id aa05421; 23 May 96 11:16:54 EDT Received: from mallet.Stanford.EDU by RI.CMU.EDU id aa16107; 23 May 96 11:16:11 EDT Received: (from trevor@localhost) by mallet.Stanford.EDU (8.7.1/8.7.1) id IAA09756 for Connectionists@cs.cmu.edu; Thu, 23 May 1996 08:16:09 -0700 (PDT) From: Trevor Hastie Message-Id: <199605231516.IAA09756@mallet.Stanford.EDU> Subject: Modern Regression and Classification To: Connectionists@cs.cmu.edu Date: Thu, 23 May 1996 08:16:09 -0700 (PDT) Content-Type: text ************* SECOND COURSE DATES *************** MODERN REGRESSION AND CLASSIFICATION June 10-11, 1996 May 9-10, 1996 (course filled) Stanford Park Hotel, Menlo Park ************************************************* A two-day course on widely applicable statistical methods for modelling and prediction, featuring Professor Trevor Hastie and Professor Robert Tibshirani Stanford University University of Toronto Our May 9-10 course was enthusiastically received by a sold-out audience, coming from all over the US, Canada and Mexico. This two day course covers modern tools for statistical prediction and classification. We start from square one, with a review of linear techniques for regression and classification, and then take attendees through a tour of: o Flexible regression techniques o Classification and regression trees o Neural networks o Projection pursuit regression o Nearest Neighbor methods o Learning vector quantization o Wavelets o Bootstrap and cross-validation We will also illustrate software tools for implementing the methods. Our objective is to provide attendees with the background and knowledge necessary to apply these modern tools to solve their own real-world problems. The course is geared for: o Statisticians o Financial analysts o Industrial managers o Medical and Quantitative researchers o Scientists o others interested in prediction and classification Attendees should have an undergraduate degree in a quantitative field, or have knowledge and experience working in such a field. PRICE: $700 per attendee. Full time registered students receive a 40% discount. Cancellation fee is $100 after registration is received. Attendance is limited to the first 50 applicants, so sign up soon! The first course filled up quickly. TO REGISTER: Send a cheque (payable to T. Hastie) along with your name, company, address, phone and FAX numbers, and email address to Professor T. Hastie 538 Campus Drive Stanford CA 94305 FAX: (415) 326-0854 For more details on the course and the instructors: o point your web browser to: http://playfair.stanford.edu/~trevor/mrc.html OR send a request by o FAX to Prof. T. Hastie at (415) 326-0854, OR o email to trevor@playfair.stanford.edu -------------------------------------------------------------------- Trevor Hastie trevor@stat.stanford.edu Phone: 415-725-2231 Fax: 415-725-8977 ftp://stat.stanford.edu/pub/hastie/ http://stat.stanford.edu/~trevor paper: Statistics Department, Stanford University, CA94305 office: Margaret Jacks Hall, rm 362 -------------------------------------------------------------------- From ruppin@math.tau.ac.il Fri May 24 19:10:56 1996 Received: from lucy.cs.wisc.edu (lucy.cs.wisc.edu [128.105.2.11]) by sea.cs.wisc.edu (8.6.12/8.6.12) with ESMTP id TAA09975 for ; Fri, 24 May 1996 19:10:52 -0500 Received: from TELNET-1.SRV.CS.CMU.EDU (TELNET-1.SRV.CS.CMU.EDU [128.2.254.108]) by lucy.cs.wisc.edu (8.6.12/8.6.12) with SMTP id TAA27483 for ; Fri, 24 May 1996 19:10:50 -0500 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id aa02825; 23 May 96 19:29:50 EDT Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa02823; 23 May 96 19:16:24 EDT Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa07789; 23 May 96 19:15:43 EDT Received: from CS.CMU.EDU by B.GP.CS.CMU.EDU id aa10170; 23 May 96 16:30:40 EDT Received: from taurus.math.tau.ac.il by CS.CMU.EDU id aa08226; 23 May 96 16:29:42 EDT Received: from gemini.math.tau.ac.il (ruppin@gemini.math.tau.ac.il [132.67.64.2]) by taurus.math.tau.ac.il (8.7.3/8.7.3) with ESMTP id XAA11348 for ; Thu, 23 May 1996 23:31:00 +0300 (GMT+0300) From: Eytan Ruppin Received: (from ruppin@localhost) by gemini.math.tau.ac.il (8.7.3/8.7.3) id XAA11605 for Connectionists@CS.CMU.EDU; Thu, 23 May 1996 23:30:59 +0300 (GMT+0300) Date: Thu, 23 May 1996 23:30:59 +0300 (GMT+0300) Message-Id: <199605232030.XAA11605@gemini.math.tau.ac.il> To: Connectionists@cs.cmu.edu Subject: Neural modeling papers Hi, 1. A few recent neural modeling papers are now available on my homepage, http://www.math.tau.ac.il/~ruppin/. Their abstracts are enclosed below. 2. Abstracts of the talks to be given in the TAU workshop on `Memory organization and consolidation: cognitive and computational perspectives' (Tel-Aviv, 28 - 30'th of May), will be available after the workshop via http://www.brain.tau.ac.il and via my homepage. Both homepages currently include the workshop program. Best wishes, Eytan Ruppin. %%%%%%%%%%%%%%%%%%%%%%%%%%% Abstracts: ----------- 1. Neuronal-Based Synaptic Compensation: A Computational Study in Alzheimer's Disease --------------------------------------------- David Horn, Nir Levy and Eytan Ruppin (to appear in Neural Computation 1996) In the framework of an associative memory model, we study the interplay between synaptic deletion and compensation, and memory deterioration, a clinical hallmark of Alzheimer's disease. Our study is motivated by experimental evidence that there are regulatory mechanisms that take part in the homeostasis of neuronal activity and act on the {\em neuronal} level. We show that, following synaptic deletion, synaptic compensation can be carried out efficiently by a {\em local, dynamic} mechanism, where each neuron maintains the profile of its incoming post-synaptic current. Our results open up the possibility that the primary factor in the pathogenesis of cognitive deficiencies in Alzheimer's disease is the failure of local neuronal regulatory mechanisms. Allowing for neuronal death, we observe two pathological routes in AD, leading to different correlations between the levels of structural damage and functional decline. 2. Optimal Firing in Sparsely-connected Low-activity Attractor Networks -------------------------------------------------------------------------- Isaac Meilijson and Eytan Ruppin (to appear in Biological Cybernetics 1996) We examine the performance of Hebbian-like attractor neural networks, recalling stored memory patterns from their distorted versions. Searching for an activation (firing-rate) function that maximizes the performance in sparsely-connected low-activity networks, we show that the optimal activation function is a {\em Threshold-Sigmoid} of the neuron's input field. This function is shown to be in close correspondence with the dependence of the firing rate of cortical neurons on their integrated input current, as described by neurophysiological recordings and conduction-based models. It also accounts for the decreasing-density shape of firing rates that has been reported in the literature. 3. Pathogenesis of Schizophrenic Delusions and Hallucinations: A Neural Model --------------------------------------------------------------------------- Eytan Ruppin, James Reggia and David Horn ({\em Schizophrenia Bulletin}, 22(1), 105-123, 1996 ) We implement and study a computational model of Stevens' theory of the pathogenesis of schizophrenia [1992]. This theory hypothesizes that the onset of schizophrenia is associated with reactive synaptic regeneration occurring in brain regions receiving degenerating temporal lobe projections. Concentrating on one such area, the frontal cortex, we model a frontal module as an associative memory neural network whose input synapses represent incoming temporal projections. Modeling Stevens' hypothesized pathological synaptic changes in this framework results in adverse side effects reminiscent of hallucinations and delusions seen in schizophrenia: spontaneous, stimulus-independent retrieval of stored memories focused on just a few of the stored patterns. These could account for the occurrence of schizophrenic delusions and hallucinations without any apparent external trigger, and for their tendency to concentrate on a few central cognitive and perceptual themes. The model explains why schizophrenic positive symptoms tend to wane as the disease progresses, why delayed therapeutical intervention leads to a much slower response, and why delusions and hallucinations may persist for a long duration when they occur. 4. Synaptic Runaway in Associative Networks ------------------------------------------- (Submitted to NIPS*96) Asnat Greenstein-Messica and Eytan Ruppin Synaptic runaway, the formation of erroneous synapses in the process of learning new patterns, is studied both analytically and numerically in binary associative neural networks. It is found that under normal biological conditions synaptic runaway in these networks is of fairly moderate magnitude, and is thus different from the extensive synaptic runaway found previously in analog-firing associative networks. However, synaptic runaway may become extensive if the threshold for Hebbian learning is reduced. The implications of these findings to the possible role of N-methyl-D-aspartate (NMDA) alterations in the pathogenesis of schizophrenia are discussed. 5. Neuronal Homeostasis and the Art of Synaptic Maintenance ------------------------------------------------------------- David Horn, Nir Levy and Eytan Ruppin (Submitted to NIPS*96) We propose a novel mechanism of synaptic maintenance whose goal is to preserve the performance of an associative memory network undergoing synaptic degradation, and to prevent the development of pathologic attractors. This mechanism is demonstrated by simulations performed in a low-activity neural model that implements local neuronal homeostasis. It works well even in a network undergoing strongly inhomogeneous synaptic alterations, and when input patterns are consecutively stored in the network. Our synaptic maintenance method strongly supports the idea that memory consolidation and synaptic maintenance should occur in separate periods of time, in a repetitive manner. Consequently, we hypothesize that synaptic maintenance occurs during REM sleep, while memory consolidation occurs during slow wave sleep. 6. Neural modeling of psychiatric disorders (A review paper) ------------------------------------------------------------- Eytan Ruppin ({\em Network}, 6, 635-656, 1995) This paper reviews recent neural modeling studies of psychiatric disorders. Numerous aspects of psychiatric disturbances have been investigated, such as the role of synaptic changes in the pathogenesis of Alzheimer's disease, the study of spurious attractors as possible neural correlates of schizophrenic positive symptoms, and the exploration of the ability of feed-forward and recurrent networks to quantitatively model the cognitive performance of schizophrenic patients. Current models all employ considerable simplifications, both on the level of the behavioral phenomenology they seek to explore, and on the level of their structure and dynamics. However, it is encouraging to realize that the disruption of just a few simple computational mechanisms can lead to behaviors which correspond to some of the clinical features of psychiatric disorders, and can shed light on their pathogenesis. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% From risto@cs.utexas.edu Sat May 25 04:22:58 1996 Received: from lucy.cs.wisc.edu (lucy.cs.wisc.edu [128.105.2.11]) by sea.cs.wisc.edu (8.6.12/8.6.12) with ESMTP id EAA13857 for ; Sat, 25 May 1996 04:22:49 -0500 Received: from TELNET-1.SRV.CS.CMU.EDU (TELNET-1.SRV.CS.CMU.EDU [128.2.254.108]) by lucy.cs.wisc.edu (8.6.12/8.6.12) with SMTP id EAA00377 for ; Sat, 25 May 1996 04:22:46 -0500 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id aa04129; 24 May 96 18:53:12 EDT Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa04127; 24 May 96 18:36:29 EDT Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa08685; 24 May 96 18:35:48 EDT Received: from CS.CMU.EDU by B.GP.CS.CMU.EDU id aa21617; 24 May 96 11:28:31 EDT Received: from mail.cs.utexas.edu by CS.CMU.EDU id aa13828; 24 May 96 11:27:57 EDT Received: from cascais.cs.utexas.edu (risto@cascais.cs.utexas.edu [128.83.122.5]) by mail.cs.utexas.edu (8.7.1/8.7.1) with ESMTP id KAA24548; Fri, 24 May 1996 10:27:46 -0500 (CDT) From: Risto Miikkulainen Received: by cascais.cs.utexas.edu (8.7.1/Client-1.4) id KAA29219; Fri, 24 May 1996 10:27:45 -0500 Date: Fri, 24 May 1996 10:27:45 -0500 Message-Id: <199605241527.KAA29219@cascais.cs.utexas.edu> To: connectionists@cs.cmu.edu, neuron@cattell.psych.upenn.edu, psyc@pucc.princeton.edu, cogneuro@ptolemy-ethernet.arc.nasa.gov Subject: Electronic book: Lateral Interactions in the Cortex Reply-to: risto@cs.utexas.edu Organization: Department of Computer Sciences, UT Austin We are pleased to announce the publication of the book LATERAL INTERACTIONS IN THE CORTEX: STRUCTURE AND FUNCTION This book is entirely electronic, in the HTML format, and can be accessed through the World Wide Web at the address below. It makes extensive use of the hypertext structure of HTML documents, including hyperlinks to researchers, institutions, and publications around the world, many color illustrations, and even a few mpeg movies. Please read the Preface for hints on how to get the most out of the book. Below is a short abstract of the book and table of contents. Enjoy! -- The Editors ------------------------------------------------------------------------ LATERAL INTERACTIONS IN THE CORTEX: STRUCTURE AND FUNCTION Electronic book, ISBN 0-9647060-0-8 http://www.cs.utexas.edu/users/nn/web-pubs/htmlbook96/ http://eris.wisdom.weizmann.ac.il/~edelman/htmlbook96/ (mirror site) Austin, TX: The UTCS Neural Network Research Group Joseph Sirosh, Risto Miikkulainen, and Yoonsuck Choe (editors) In the last few years, several new results on the structure, development, and functional role of lateral connectivity in the cortex have emerged. These results have led to a new understanding of the cortex as a continuously-adapting dynamic system shaped by competitive and cooperative lateral interactions. Many of the results and their interpretations are still controversial, and computational and analytical investigations can serve a pivotal role in establishing this new model. This book brings together eleven such investigations, each from a slightly different perspective and level, aiming at explaining what function the lateral interactions could play in the development and information processing in the cortex. The book serves as an overview of the kinds of processes that may be going on, laying the groundwork for understanding information processing in the laterally connected cortex. Table of Contents: Preface 1. Introduction - Risto Miikkulainen and Joseph Sirosh 2. The Pattern and Functional Significance of Long-Range Interactions in Human Visual Cortex - Uri Polat, Anthony M. Norcia, and Dov Sagi 3. Recurrent Inhibition and Clustered Connectivity as a Basis for Gabor-like Receptive Fields in the Visual Cortex - Silvio P. Sabatini 4. Variable Gain Control in Local Cortical Circuitry Supports Context-Dependent Modulation by Long-Range Connections - David C. Somers, Louis J. Toth, Emanuel Todorov, S. Chenchal Rao, Dae-Shik Kim, Sacha B. Nelson, Athanassios G. Siapas, and Mriganka Sur 5. The Role of Lateral Connections in Visual Cortex: Dynamics and Information Processing - Marius Usher, Martin Stemmler, and Ernst Niebur 6. Synchronous Oscillations Based on Lateral Connections - DeLiang Wang 7. A Basis for Long-Range Inhibition Across Cortex - J. G. Taylor and F. N. Alavi 8. Self-Organization of Orientation Maps, Lateral Connections, and Dynamic Receptive Fields in the Primary Visual Cortex - Joseph Sirosh, Risto Miikkulainen, and James A. Bednar 9. Associative Decorrelation Dynamics in Visual Cortex - Dawei W. Dong 10. A Self-Organizing Neural Network That Learns to Detect and Represent Visual Depth from Occlusion Events - Jonathan A. Marshall and Richard Alley 11. Face Recognition by Dynamic Link Matching - Laurenz Wiskott and Christoph von der Malsburg 12. Why Have Lateral Connections in the Visual Cortex? - Shimon Edelman From mike@psych.ualberta.ca Sat May 25 04:22:59 1996 Received: from lucy.cs.wisc.edu (lucy.cs.wisc.edu [128.105.2.11]) by sea.cs.wisc.edu (8.6.12/8.6.12) with ESMTP id EAA13861 for ; Sat, 25 May 1996 04:22:52 -0500 Received: from TELNET-1.SRV.CS.CMU.EDU (TELNET-1.SRV.CS.CMU.EDU [128.2.254.108]) by lucy.cs.wisc.edu (8.6.12/8.6.12) with SMTP id EAA00381 for ; Sat, 25 May 1996 04:22:49 -0500 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id aa04288; 24 May 96 22:14:04 EDT Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa04286; 24 May 96 22:04:40 EDT Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa08792; 24 May 96 22:04:08 EDT Date: Fri, 24 May 1996 13:10:53 -0600 (MDT) Sender: Mike Dawson From: "Dr. Michael R.W. Dawson" Subject: Cognitive Neuroscience Job To: Connectionists Message-ID: MIME-Version: 1.0 Content-Type: TEXT/PLAIN; charset=US-ASCII The University of Alberta, Department of Psychology, is pleased to announce that it is continuing its expansion into the Cognitive Neurosciences in 1997. Canadians and Non-Canadians are encouraged to apply for a tenure-track position. Details are described below. Additional information, including profiles of two cognitive neuroscientists hired by our Department last year, can be found at our web-site: http://web.psych.ualberta.ca/Neuroscience_Positions.htmld/index.html ========================================================== DEPARTMENT OF PSYCHOLOGY, UNIVERSITY OF ALBERTA Tenure-Track Assistant Professor Position in Cognitive Neuroscience The Department of Psychology, Faculty of Science at the University of Alberta, is seeking to expand its development in the Cognitive Neurosciences. A tenure-track position in Cognitive Neuroscience at the assistant professor level will be open to competition (salary range $39,230 - $55,526). The appointment will be effective July 1, 1997. Candidates should have a strong interest in neuroscience with demonstrated excellence and ongoing research programs. The expectation is that the successful candidate will secure NSERC, MRC, or equivalent funding. Hiring decisions will be made on the basis of demonstrated research capability, teaching ability, and the potential for interactions with colleagues. Applicants should have an expertise in any of the following or related areas: perception, language, neural plasticity, development and aging, attention, motor control, emotion, or memory. The applicant should send a curriculum vitae, a statement of current and future research plans, recent publications, and arrange to have at least three letters of reference forwarded, to the Chair of the Cognitive Neuroscience Search Committee, Department of Psychology, P-220 Biological Sciences Building, University of Alberta, Edmonton, Alberta, Canada, T6G 2E9. Applications for the competition should be received by November 1, 1996. PhD must be completed by July 1, 1997. The University of Alberta is committed to the principle of equity in employment. As an employer we welcome diversity in the workplace and encourage applications from all qualified women and men, including Aboriginal peoples, persons with disabilities, and members of visible minorities. From jung@service1.uky.edu Sat May 25 04:23:02 1996 Received: from lucy.cs.wisc.edu (lucy.cs.wisc.edu [128.105.2.11]) by sea.cs.wisc.edu (8.6.12/8.6.12) with ESMTP id EAA13859 for ; Sat, 25 May 1996 04:22:50 -0500 Received: from TELNET-1.SRV.CS.CMU.EDU (TELNET-1.SRV.CS.CMU.EDU [128.2.254.108]) by lucy.cs.wisc.edu (8.6.12/8.6.12) with SMTP id EAA00379 for ; Sat, 25 May 1996 04:22:48 -0500 Received: from TELNET-1.SRV.CS.CMU.EDU by telnet-1.srv.cs.CMU.EDU id aa04149; 24 May 96 19:02:47 EDT Received: from DST.BOLTZ.CS.CMU.EDU by TELNET-1.SRV.CS.CMU.EDU id aa04133; 24 May 96 18:37:24 EDT Received: from DST.BOLTZ.CS.CMU.EDU by DST.BOLTZ.CS.CMU.EDU id aa08692; 24 May 96 18:36:34 EDT Received: from CS.CMU.EDU by B.GP.CS.CMU.EDU id aa26113; 24 May 96 17:01:58 EDT Received: from service1.cc.uky.edu by CS.CMU.EDU id aa16757; 24 May 96 17:01:46 EDT Received: from [] ([128.163.176.21]) by service1.cc.uky.edu (8.7.Beta.13/8.7.Beta.13) with SMTP id RAA23996; Fri, 24 May 1996 17:01:40 -0400 (EDT) Message-Id: <199605242101.RAA23996@service1.cc.uky.edu> Sender: jung@service1.uky.edu MMDF-Warning: Parse error in original version of preceding line at DST.BOLTZ.CS.CMU.EDU From: "Dr. Ranu Jung" To: cneuro@bbb.caltech.edu Date: Fri, 24 May 1996 15:58:51 +0000 Subject: Graduate Research Asst. Reply-to: jung@service1.uky.edu CC: Connectionists@cs.cmu.edu Priority: normal X-mailer: Pegasus Mail/Windows (v1.22) GRADUATE RESEARCH ASSISTANTSHIPS (PLEASE FORWARD) A graduate research assistantship is available for up to 3 years to conduct research in the "Neural Control of Locomotion" at the Center for Biomedical Engineering, University of Kentucky. Students have to be accepted into the Ph.D./MS program starting Fall 1996 (August). The assistantship is available to citizens of all nations. The research is to examine the dynamical interaction between the brain and the spinal cord in the control of locomotion, in particular, swimming in a lower vertebrate. Traditional neurophysiological experimental techniques will be complimented by techniques from non-linear signal processing and control. In conjunction, the behavior of connectionist/biophysical neural network models will be examined and analyzed using tools from dynamical systems theory. If interested, send CV, and names of two references, preferably by email or Fax to: Ranu Jung, Ph.D. Center for Biomedical Engineering 21 Wenner-Gren Research Lab. University of Kentucky, Lexington 40506-0070 Tel. 606-257-5931 email:jung@pop.uky.edu Fax: 606-257-1856 The University of Kentucky is located in the rolling hills of the Bluegrass Country and has a diverse campus. The Center for Biomedical Engineering is a multidisciplinary center in the Graduate School. We have strong ties to the Medical Center and the School of Engineering. Details about the University of Kentucky and the Center for Biomedical Engineering can be obtained on the web at http://www.uky.edu; http://www.uky.edu/RGS/CBME.