Path: utzoo!utgpu!news-server.csri.toronto.edu!rpi!zaphod.mps.ohio-state.edu!cis.ohio-state.edu!pacific.mps.ohio-state.edu!linac!att!ucbvax!HPLMS2.HPL.HP.COM!neuron-request From: neuron-request@HPLMS2.HPL.HP.COM ("Neuron-Digest Moderator Peter Marvit") Newsgroups: comp.ai.neural-nets Subject: Neuron Digest V7 #26 Message-ID: <10819.674163907@hplpm.hpl.hp.com> Date: 13 May 91 19:45:07 GMT Sender: daemon@ucbvax.BERKELEY.EDU Reply-To: "Neuron-Request" Organization: Hewlett-Packard Laboratories Lines: 802 Neuron Digest Monday, 13 May 1991 Volume 7 : Issue 26 Today's Topics: 2 TRs: Categorical Perception and Neural Nets 2 TRs - Speaker Independent Vowel Recognition + Optimal Pruning proceedings 3rd NN & PDP + Air Mail Postage preprints and reports Journal of Ideas, Vol 2 #1 Abstracts Technical Report available: High-Level Perception TR - Investigating Fault Tolerance in Artificial Neural Networks Send submissions, questions, address maintenance and requests for old issues to "neuron-request@hplabs.hp.com" or "{any backbone,uunet}!hplabs!neuron-request" Use "ftp" to get old issues from hplpm.hpl.hp.com (15.255.176.205). ------------------------------------------------------------ Subject: 2 TRs: Categorical Perception and Neural Nets From: Stevan Harnad Date: Tue, 23 Apr 91 16:31:25 -0400 The following two tech reports are available by anonymous ftp from directory /pub/harnad on princeton.edu. Full ftp instructions follow the abstracts. ------------------------------------------------- (1) Categorical Perception and the Evolution of Supervised Learning in Neural Nets S Harnad*, SJ Hanson*,** & J Lubin* *Princeton University **Siemens Research Center [Presented at 1991 AAAI Symposium on Symbol Grounding: Problem and Practice] ABSTRACT: Some of the features of animal and human categorical perception (CP) for color, pitch and speech are exhibited by neural net simulations of CP with one-dimensional inputs: When a backprop net is trained to discriminate and then categorize a set of stimuli, the second task is accomplished by "warping" the similarity space (compressing within-category distances and expanding between-category distances). This natural side-effect also occurs in humans and animals. Such CP categories, consisting of named, bounded regions of similarity space, may be the ground level out of which higher-order categories are constructed; nets are one possible candidate for the mechanism that learns the sensorimotor invariants that connect arbitrary names (elementary symbols?) to the nonarbitrary shapes of objects. This paper examines how and why such compression/expansion effects occur in neural nets. [Retrieve by anonymous ftp in binary mode as (compressed) file harnad91.cpnets.Z from directory /pub/harnad on princeton.edu, instructions below] ----------------------------------------------------------------- (2) Connecting Object to Symbol in Modeling Cognition Stevan Harnad Department of Psychology Princeton University Princeton NJ 08544 [To appear in Clark, A. & Lutz, R. (Eds) (1992) "CONNECTIONISM IN CONTEXT," Springer-Verlag] Connectionism and computationalism are currently vying for hegemony in cognitive modeling. At first glance the opposition seems incoherent, because connectionism is itself computational, but the form of computationalism that has been the prime candidate for encoding the "language of thought" has been symbolic computationalism, whereas connectionism is nonsymbolic. This paper examines what is and is not a symbol system. A hybrid nonsymbolic/symbolic system will be sketched in which the meanings of the symbols are grounded bottom-up in the system's capacity to discriminate and identify the objects they refer to. Neural nets are one possible mechanism for learning the invariants in the analog sensory projection on which successful categorization is based. "Categorical perception," in which similarity space is "warped" in the service of categorization, turns out to be exhibited by both people and nets, and may mediate the constraints exerted by the analog world of objects on the formal world of symbols. [Retrieve by anonymous ftp in binary mode as (compressed) file harnad92.symbol.object.Z from directory /pub/harnad on princeton.edu] To retrieve a file by ftp from a Unix/Internet site, type: ftp princeton.edu When you are asked for your login, type: anonymous For your password, type your full name then change directories with: cd pub/harnad Then type: binary (This is for retrieving compressed files.) To show the available files, type: ls Next, retrieve the file you want with (for example): get filename.Z When you have the file(s) you want, type: quit Next uncompress the file with: uncompress filename.Z Now the file will be called, simply, filename ************* The above cannot be done from Bitnet directly, but there is a fileserver called bitftp@pucc.bitnet that will do it for you. Send it the one line message help for instructions (which will be similar to the above, but will be in the form of a series of lines in an email message that bitftp will then execute for you). ------------------------------ Subject: 2 TRs - Speaker Independent Vowel Recognition + Optimal Pruning From: sankar@bach.RUTGERS.EDU Date: Tue, 23 Apr 91 18:49:46 -0400 The following two papers are now available via FTP from the neuroprose archives. Both will be presented at IJCNN-Seattle, 1991. These papers describe a new approach that combines Neural Networks and Decision Trees to form a classifier that grows the neurons as it learns. **************************************************************************** SPEAKER INDEPENDENT VOWEL RECOGNITION USING NEURAL TREE NETWORKS Ananth Sankar and Richard Mammone CAIP Center and Dept. of Electrical Engg. Rutgers University, P.O. Box 1390 Piscataway, NJ 08855-1390 Speaker independent vowel recognition is a difficult pattern recognition problem. Recently there has been much research using Multi-Layer Perceptrons (MLP) and Decision Trees for this task. This paper presents a new approach to this problem. A new neural architecture and learning algorithm called Neural Tree Networks (NTN) are developed. This network uses a tree structure with a neural network at each tree node. The NTN architecture offers a very efficient hardware implementation as compared to MLPs. The NTN algorithm grows the neurons while learning as opposed to backpropagation, for which the number of neurons must be known before learning can begin. The new algorithm is guaranteed to converge on the training set whereas backpropagation can get stuck in local minima. A gradient descent technique is used to grow the NTN. This approach is more efficient than the exhaustive search techniques used in standard decision tree algorithms. We present simulation results on a speaker independent vowel recognition task. These results show that the new method is superior to both MLP and decision tree methods. ***************************************************************************** OPTIMAL PRUNING OF NEURAL TREE NETWORKS FOR IMPROVED GENERALIZATION Ananth Sankar and Richard Mammone CAIP Center and Dept. of Electrical Engg. Rutgers University, P.O. Box 1390 Piscataway, NJ 08855-1390 An optimal pruning algorithm for a Neural Network recently developed called Neural Tree Networks (NTN) is presented. The NTN is grown by a constructive learning algorithm that decreases the classification error on the training data recursively. The optimal pruning algorithm is then used to improve generalization. The pruning algorithm is shown to be computationally inexpensive. Simulation results on a speaker independent vowel recognition task are presented to show the improved generalization using the pruning algorithm. *************************************************************************** To retrieve: unix> ftp cheops.cis.ohio-state.edu (or 128.146.8.62) Name: anonymous Password: neuron ftp> cd pub/neuroprose ftp> binary ftp> get sankar.ijcnn91_1.ps.Z ftp> get sankar.ijcnn91_2.ps.Z ftp> quit unix> uncompress sankar.ijcnn*.ps unix> lpr sankar.ijcnn91_1.ps sankar.ijcnn91_2.ps Thanks to Jordan Pollack for making this service available! ------------------------------ Subject: proceedings 3rd NN & PDP + Air Mail Postage From: SAYEGH@CVAX.IPFW.INDIANA.EDU Date: Thu, 25 Apr 91 22:54:32 -0400 In the announcement of the proceedings of the third conference on Neural Networks and Parallel Distributed Processing at Indiana-Purdue University, there was a minor mix-up in the list of papers. We also received a number of requests from Europe and Japan inquiring about Air Mail costs. Here is the info: Proceedings can be obtained by writing to: Ms. Sandra Fisher Physics Department Indiana University-Purdue University Ft Wayne, IN 46805 and including $5 + mailing and handling costs as follows $1 for the US, $2 for Canada and Mexico, $8.50 for all others by Air Mail or $3.95 by surface mail. Checks should be made payable to The Indiana-Purdue Foundation. The 109 page proceedings contain the following papers: INTEGRATED AUTONOMOUS NAVIGATION BY ADAPTIVE NEURAL NETWORKS Dean A. Pomerleau Department of Computer Science Carnegie Mellon University APPLYING A HOPFIELD-STYLE NETWORK TO DEGRADED PRINTED TEXT RESTORATION Arun Jagota Department of Computer Science State University of New York at Buffalo RECENT STUDIES WITH PARALLEL, SELF-ORGANIZ- ING, HIERARCHICAL NEURAL NETWORKS O.K. Ersoy & D. Hong School of Electrical Engineering Purdue University INEQUALITIES, PERCEPTRONS AND ROBOTIC PATH- PLANNING Samir I. Sayegh Department of Physics Indiana University-Purdue University GENETIC ALGORITHMS FOR FEATURE SELECTION FOR COUNTERPROPAGATION NETWORKS F.Z. Brill & W.N. Martin Department of Computer Science University of Virginia MULTI-SCALE VISION-BASED NAVIGATION ON DIS- TRIBUTED-MEMORY MIMD COMPUTERS A.W. Ho & G.C. Fox Caltech Concurrent Computation Program California Institute of Technology A NEURAL NETWORK WHICH ENABLES SPECIFICATION OF PRODUCTION RULES N. Liu & K.J. Cios The University of Toledo PIECE-WISE LINEAR ESTIMATION OF MECHANICAL PROPERTIES OF MATERIALS WITH NEURAL NETWORKS I.H. Shin, K.J. Cios, A. Vary* & H.E. Kautz* The University of Toledo & NASA Lewis Re- search Center* INFLUENCE OF THE COLUMN STRUCTURE ON INTRA- CORTICAL LONG RANGE INTERACTIONS E. Niebur & F. Worgotter California Institute of Technology LEARNING BY GRADIENT DESCENT IN FUNCTION SPACE Ganesh Mani University of Wisconsin-Madison SUCCESSIVE REFINEMENT OF MULTI-RESOLUTION REPRESENTATIONS OF THE ENVIRONMENT IN CONNECTIONIST NETWORKS Vasant Honovar and Leonard Uhr Computer Sciences Department University of Wisconsin-Madison A NEURAL ARCHITECTURE FOR COGNITIVE MAPS Martin Sonntag Cognitive Science & Machine Intelligence Lab University of Michigan ------------------------------ Subject: preprints and reports From: Juergen Schmidhuber Date: 30 Apr 91 09:17:00 +0200 Recent preprints and technical reports are available via ftp: ------------------------------------------------------------------ ADAPTIVE DECOMPOSITION OF TIME Juergen Schmidhuber, TUM (Talk at ICANN'91, Helsinki, June 24-28, 1991) In this paper we introduce design principles for unsupervised detection of regularities (like causal relationships) in temporal sequences. One basic idea is to train an adaptive predictor module to predict future events from past events, and to train an additional confidence module to model the reliability of the predictor's predictions. We select system states at those points in time where there are changes in prediction reliability, and use them recursively as inputs for higher-level predictors. This can be beneficial for `adaptive sub-goal generation' as well as for `conventional' goal-directed (supervised and reinforcement) learning: Systems based on these design principles were successfully tested on tasks where conventional training algorithms for recurrent nets fail. Finally we describe the principles of the first neural sequence `chunker' which collapses a self-organizing multi-level predictor hierarchy into a single recurrent network. LEARNING TO GENERATE SUBGOALS FOR ACTION SEQUENCES Juergen Schmidhuber, TUM (Talk at ICANN'91) This paper extends the technical report FKI-129-90 (`Toward compositional learning with neural networks'). USING ADAPTIVE SEQUENTIAL NEUROCONTROL FOR EFFICIENT LEARNING OF TRANSLATION AND ROTATION INVARIANCE Juergen Schmidhuber and Rudolf Huber, TUM (Talk at ICANN'91) This paper is based on FKI-128-90 (announced earlier). ------------------------------------------------------------------- LEARNING TO CONTROL FAST-WEIGHT MEMORIES: AN ALTERNATIVE TO DYNAMIC RECURRENT NETWORKS Juergen Schmidhuber, TUM Technical report FKI-147-91, March 26, 1991 Previous algorithms for supervised sequence learning are based on dynamic recurrent networks. This paper describes alternative gradient-based systems consisting of two feed-forward nets which learn to deal with temporal sequences by using fast weights: The first net learns to produce context dependent weight changes for the second net whose weights may vary very quickly. One advantage of the method over the more conventional recurrent net algorithms is the following: It does not necessarily occupy full-fledged units (experiencing some sort of feedback) for storing information over time. A simple weight may be sufficient for storing temporal information. Since with most networks there are many more weights than units, this property represents a potential for storage efficiency. Various learning methods are derived. Two experiments with unknown time delays illustrate the approach. One experiment shows how the system can be used for adaptive temporary variable binding. NEURAL SEQUENCE CHUNKERS Juergen Schmidhuber, TUM Technical report FKI-148-91, April 26, 1991 This paper addresses the problem of meaningful hierarchical adaptive decomposition of temporal sequences. This problem is relevant for time-series analysis as well as for goal-directed learning. The first neural systems for recursively chunking sequences are described. These systems are based on a principle called the `principle of history compression'. This principle essentially says: As long as a predictor is able to predict future environmental inputs from previous ones, no additional knowledge can be obtained by observing these inputs in reality. Only unpredicted inputs deserve attention. The focus is on a 2-network system which tries to collapse a self-organizing multi-level predictor hierarchy into a single recurrent network (the automatizer). The basic idea is to feed everything that was not expected by the automatizer into a `higher-level' recurrent net (the chunker). Since the expected things can be derived from the unexpected things by the automatizer, the chunker is fed with a reduced description of the input history. The chunker has a comparatively easy job in finding possibilities for additional reductions, since it works on a slower time scale and receives less inputs than the automatizer. Useful internal representations of the chunker in turn are taught to the automatizer. This leads to even more reduced input descriptions for the chunker, and so on. Experimentally it is shown that the system can be superior to conventional training algorithms for recurrent nets: It may require fewer computations per time step, and in addition it may require fewer training sequences. A possible extension for reinforcement learning and adaptive control is mentioned. An analogy is drawn between the behavior of the chunking system and the apparent behavior of humans. ADAPTIVE CONFIDENCE AND ADAPTIVE CURIOSITY Juergen Schmidhuber Technical Report FKI-149-91, April, 26, 1991 Much of the recent research on adaptive neuro-control and reinforcement learning focusses on systems with adaptive `world models'. Previous approaches, however, do not address the problem of modelling the reliability of the world model's predictions in uncertain environments. Furthermore, with previous approaches usually some ad-hoc method (like random search) is used to train the world model to predict future environmental inputs from previous inputs and control outputs of the system. This paper introduces ways for modelling the reliability of the outputs of adaptive world models, and it describes more sophisticated and sometimes much more efficient methods for their adaptive construction by on-line state space exploration: For instance, a 4-network reinforcement learning system is described which tries to maximize the future expectation of the temporal derivative of the adaptive assumed reliability of future predictions. The system is `curious' in the sense that it actively tries to provoke situations for which it {\em learned to expect to learn} something about the environment. In a very limited sense the system learns how to learn. An experiment with a simple non-deterministic environment demonstrates that the method can be clearly faster than the conventional model-building strategy. -------------------------------------------------------- To obtain copies of the papers, do: unix> ftp 131.159.8.35 Name: anonymous Password: your name, please ftp> binary ftp> cd pub/fki ftp> get .ps.Z ftp> bye unix> uncompress .ps.Z unix> lpr .ps Here stands for any of the following six possibilities: icanndec (Adaptive Decomposition of Time) icannsub (Subgoal-Generator). This paper contains 5 partly hand-drawn figures which are not retrievable. Sorry. icanninv (Sequential Neuro-Control). fki147 (Fast Weights) fki148 (Sequence Chunkers) fki149 (Adaptive Curiosity) Please do not forget to leave your name. This will allow us to save paper if you are on our hardcopy mailing list. NOTE: icanninv.ps, fki148.ps, and fki149.ps are designed for European A4 paper format (20.9cm x 29.6cm). ----------------------------------------------------------- In case of ftp-problems contact Juergen Schmidhuber Institut fuer Informatik, Technische Universitaet Muenchen Arcisstr. 21 8000 Muenchen 2 GERMANY or send email to schmidhu@informatik.tu-muenchen.de DO NOT USE REPLY! ------------------------------ Subject: Journal of Ideas, Vol 2 #1 Abstracts From: Elan Moritz Date: Tue, 30 Apr 91 18:22:58 -0700 +=++=++=++=++=++=++=++=++=++=++=++=++=++=++=+ please post & circulate Announcement ......... Abstracts of papers appearing in Volume 2 # 1 of the Journal of Ideas THOUGHT CONTAGION AS ABSTRACT EVOLUTION Aaron Lynch Abstract: Memory abstractions, or mnemons, form the basis of a memetic evolution theory where generalized self-replicating ideas give rise to thought contagion. A framework is presented for describing mnemon propagation, combination, and competition. It is observed that the transition from individual level considerations to population level considerations can act to cancel individual variations and may result in population behaviors. Equations for population memetics are presented for the case of two-idea interactions. It is argued that creativity via innovation of ideas is a population phenomena. Keywords: mnemon, meme, evolution, replication, idea, psychology, equation. ................... CULTURE AS A SEMANTIC FRACTAL: Sociobiology and Thick Description Charles J. Lumsden Department of Medicine, University of Toronto Toronto, Ontario, Canada M5S 1A8 Abstract: This report considers the problem of modeling culture as a thick symbolic system: a system of reference and association possessing multiple levels of meaning and interpretation. I suggest that thickness, in the sense intended by symbolic anthropologists like Geertz, can be treated mathematically by bringing together two lines of formal development, that of semantic networks, and that of fractal mathematics. The resulting semantic fractals offer many advantages for modeling human culture. The properties of semantic fractals as a class are described, and their role within sociobiology and symbolic anthropology considered. Provisional empirical evidence for the hypothesis of a semantic fractal organization for culture is discussed, together with the prospects for further testing of the fractal hypothesis. Keywords: culture, culturgen, meme, fractal, semantic network. ................... MODELING THE DISTRIBUTION OF A "MEME" IN A SIMPLE AGE DISTRIBUTION POPULATION: I. A KINETICS APPROACH AND SOME ALTERNATIVE MODELS Matthew Witten Center for High Performance Computing University of Texas System, Austin, TX 78758-4497 Abstract. Although there is a growing historical body of literature relating to the mathematical modeling of social and historical processes, little effort has been placed upon modeling the spread of an idea element "meme" in such a population. In this paper we review some of the literature and we then consider a simple kinetics approach, drawn from demography, to model the distribution of a hypothetical "meme" in a population consisting of three major age groups. KEYWORDS: Meme, idea, age-structure, compartment, sociobiology, kinetics model. ................... THE PRINCIPIA CYBERNETICA PROJECT Francis Heylighen, Cliff Joslyn, and Valentin Turchin The Principia Cybernetica Project[dagger] Abstract: This note describes an effort underway by a group of researchers to build a complete and consistent system of philosophy. The system will address, issues of general philosophical concern, including epistemology, metaphysics, and ethics, or the supreme human values. The aim of the project is to move towards conceptual unification of the relatively fragmented fields of Systems and Cybernetics through consensually-based philosophical development. Keywords: cybernetics, culture, evolution, system transition, networks, hypermedia, ethics, epistemology. ................... Brain and Mind: The Ultimate Grand Challenge Elan Moritz The Institute for Memetic Research P. O. Box 16327, Panama City, Florida 32406 Abstract: Questions about the nature of brain and mind are raised. It is argued that the fundamental understanding of the functions and operation of the brain and its relationship to mind must be regarded as the Ultimate Grand Challenge problem of science. National research initiatives such as the Decade of the Brain are discussed. Keywords: brain, mind, awareness, consciousness, computers, artificial intelligence, meme, evolution, mental health, virtual reality, cyberspace, supercomputers. +=++=++=++=++=++=++=++=++=++=++=++=++=++=++=+ The Journal of Ides an archival forum for discussion of 1) evolution and spread of ideas, 2) the creative process, and 3) biological and electronic implementations of idea/knowledge generation and processing. The Journal of Ideas, ISSN 1049-6335, is published quarterly by the Institute for Memetic Research, Inc. P. O. Box 16327, Panama City Florida 32406-1327. >----------- FOR MORE INFORMATION -------> E-mail requests to Elan Moritz, Editor, at moritz@well.sf.ca.us. +=++=++=++=++=++=++=++=++=++=++=++=++=++=++=+ ------------------------------ Subject: Technical Report available: High-Level Perception From: David Chalmers Date: Wed, 01 May 91 23:05:31 -0500 The following paper is available electronically from the Center for Research on Concepts and Cognition at Indiana University. HIGH-LEVEL PERCEPTION, REPRESENTATION, AND ANALOGY: A CRITIQUE OF ARTIFICIAL INTELLIGENCE METHODOLOGY David J. Chalmers, Robert M. French, and Douglas R. Hofstadter Center for Research on Concepts and Cognition Indiana University CRCC-TR-49 High-level perception -- the process of making sense of complex data at an abstract, conceptual level -- is fundamental to human cognition. Via high-level perception, chaotic environmental stimuli are organized into mental representations which are used throughout cognitive processing. Much work in traditional artificial intelligence has ignored the process of high-level perception completely, by starting with hand-coded representations. In this paper, we argue that this dismissal of perceptual processes leads to distorted models of human cognition. We examine some existing artificial-intelligence models -- notably BACON, a model of scientific discovery, and the Structure-Mapping Engine, a model of analogical thought -- and argue that these are flawed precisely because they downplay the role of high-level perception. Further, we argue that perceptual processes cannot be separated from other cognitive processes even in principle, and therefore that such artificial-intelligence models cannot be defended by supposing the existence of a "representation module" that supplies representations ready-made. Finally, we describe a model of high-level perception and analogical thought in which perceptual processing is integrated with analogical mapping, leading to the flexible build-up of representations appropriate to a given context. N.B. This is not a connectionist paper in the narrowest sense, but the representational issues discussed are very relevant to connectionism, and the advocated integration of perception and cognition is a key feature of many connectionist models. Also, philosophical motivation for the "quasi-connectionist" Copycat architecture is provided. --------------------------------------------------- This paper may be retrieved by anonymous ftp from cogsci.indiana.edu (129.79.238.6). The file is cfh.perception.ps.Z, in the directory pub. To retrieve, follow the procedure below. unix> ftp cogsci.indiana.edu # (or ftp 129.79.238.6) ftp> Name: anonymous ftp> Password: [identification] ftp> cd pub ftp> binary ftp> get cfh.perception.ps.Z ftp> quit unix> uncompress cfh.perception.ps.Z unix> lpr -P(your_local_postscript_printer) cfh.perception.ps If you do not have access to ftp, hardcopies may be obtained by sending e-mail to dave@cogsci.indiana.edu. ------------------------------ Subject: TR - Investigating Fault Tolerance in Artificial Neural Networks From: george@minster.york.ac.uk Date: 02 May 91 14:17:33 +0000 [[ Editor's Note: I assume non-UK folks need to use eitehr international coupons or translate their own currency into pounds. Hmmm, maybe it is time to use the ecu? -PM ]] The following technical report is now available. For a copy, email "ilp@uk.ac.york.minster" and include your ordinary mail address. To cover postage, a charge of 50p is made to academic institutions else 1.50. Cheques should be made payable to "The University of York". Alternatively, email me (george@uk.ac.york.minster) and I can send you a photocopy, though you may have to wait a little longer. YCS 154: Investigating Fault Tolerance in Artificial Neural Networks G Bolt, University of York, UK Abstract A review of fault tolerance in neural networks is presented. Work relating to the various issues of redundancy, reliability, complexity and capacity are considered, as well as covering both empirical results and a general treatment of theoretical work. It is shown that in the majority of the work, few sound theoretical methods have been applied, and that conventional fault tolerant techniques cannot straightforwardly be transferred to neural networks. It is concluded that although neural networks are often cited as being fault tolerant, little substantial evidence is available to support this claim. A proposal for a framework which can be used to assess fault tolerance and robustness in neural networks, and also to guide work in this field is given. Various factors which might influence the reliability of such a system are discussed. To support this framework, two fundamental prerequisite stages are described in sections 4 and 5 which can act as a base for research into the fault tolerance of neural networks. Section 4 describes how fault models can be developed for neural networks visualised at the abstract level. This involves first locating where faults can occur, and then defining the attributes of the faults. Section 5 uses this fault model to develop various methods which can be used to measure the reliability of the neural network. Fault Injection and Mean-Time-Between-Failure methods are examined, and from these a more appropriate Service Degradation Method is developed. Two critical issues of how to measure the degree of failure within a neural network and how to choose a suitable timescale are discussed. The multi-layer perceptron network model is used in examples which illustrate how ideas described here can be applied. ____________________________________________________________ George Bolt, Advanced Computer Architecture Group, Dept. of Computer Science, University of York, Heslington, YORK. YO1 5DD. UK. Tel: [044] (0904) 432771 george@uk.ac.york.minster JANET george%minster.york.ac.uk@nsfnet-relay.ac.uk ARPA george!mcsun!ukc!minster!george UUCP ____________________________________________________________ ------------------------------ End of Neuron Digest [Volume 7 Issue 26] ****************************************