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Neuron Digest Volume 13 Number 14
Neuron Digest Sunday, 13 Mar 1994 Volume 13 : Issue 14
Today's Topics:
ISIKNH'94 (Knowledge + Neural Heuristics)
Tutorial - Neural Networks, Speech Technology, and Other Applications
New Submission Deadline for ISMB '94 Conference
Workshop: Constructive Induction and Change of Representation
CFP-Spl. issue of EJOR on Neural Nets and OR
Send submissions, questions, address maintenance, and requests for old
issues to "neuron-request@psych.upenn.edu". The ftp archives are
available from psych.upenn.edu (130.91.68.31) in pub/Neuron-Digest or by
sending a message to "archive-server@psych.upenn.edu".
----------------------------------------------------------------------
Subject: ISIKNH'94 (Knowledge + Neural Heuristics)
From: fu@cis.ufl.edu
Date: Tue, 01 Mar 1994 15:59:23 -0500
ISIKNH'94 (Advance Program and Registration Information)
(International Symposium on Integrating Knowledge and Neural Heuristics)
Sponsored by AAAI and University of Florida
Time: May 9-10 1994; Place: Pensacola Beach, Florida, USA.
MAY 9, 1994:
========================================================================
Keynote Speech:
May 9, 9:00-9:45 a.m.
"Representation, Cognitive Architectures and Knowledge and Symbol Levels"
B. Chandrasekaran
========================================================================
========================================================================
Plenary Speech:
May 9, 10:00-10:45 a.m.
"Fuzzy Logic as a Basis for Knowledge Representation in Neural Networks"
Ronald R. Yager
========================================================================
========================================================================
Plenary Speech:
May 9, 11:00-11:45 a.m.
"Hybrid Models for Fuzzy Control"
Jim Bezdek
========================================================================
**** Lunch Break ****
- ------------------------------------------------------------------------
Technical Session 1: (Integration Methodology I)
Chair: Armando F. da Rocha
May 9, 1:15-2:00 p.m.
``Integrating temporal symbolic knowledge and recurrent networks''
Christian W. Omlin, C. Lee Giles
``Implementing schemes and logics in connectionist models''
Ron Sun
``Integrating rules and neurocomputing for knowledge representation''
Ioannis Hatzilygeroudis
- ------------------------------------------------------------------------
- ------------------------------------------------------------------------
Technical Session 2: (Learning)
Chair: Ron Sun
May 9, 2:15-3:00 p.m.
``Symbolic knowledge from unsupervised learning''
Tharam S. Dillon, S. Sestito, M. Witten, M. Suing
``Genetically refining topologies of
knowledge-based neural networks''
David W. Opitz, Jude W. Shavlik
``On using decision tree as feature selector for feed-forward
neural networks''
Selwyn Piramuthu, Michael Shaw
- ------------------------------------------------------------------------
**** Snack Break ****
- ------------------------------------------------------------------------
Technical Session 3: (Fuzziness and Uncertainty)
Chair: Lee Giles
May 9, 3:30-4:15 p.m.
``Modifying network architectures for certainty-factor
rule-base revision''
Jeffrey Mahoney, Raymond Mooney
``Learning EMYCIN semantics''
K.D. Nguyen, R.C. Lacher
``Special fuzzy relational methods for the recognition of speech
with neural networks''
Carlos A. Reyes, Wyllis Bandler
- ------------------------------------------------------------------------
- ------------------------------------------------------------------------
Technical Session 4: (Integration Methodology II)
Chair: Ron Sun
May 9, 4:30-5:30 p.m.
``Learning knowledge and strategy of a generic neuro-expert system model''
Rajiv Khosla, T. Dillon
``Integrating symbolic and neural methods for building intelligent systems''
Ricardo Jose Machado, Armando Freitas da Rocha
``Modular integration of connectionist and symbolic processing
in knowledge-based systems''
Melanie Hilario
``Symbolic computation with monotonic maps of the interval''
Ron Bartlett, Max Garzon
- ------------------------------------------------------------------------
- ------------------------------------------------------------------------
Poster Session:
May 9, 1:30-4:30 p.m.
``The KoDiag system Case-based diagnosis with Kohonen networks''
Jurgen Rahmel, A. von Wangenheim
``Deriving conjunctive classification rules from neural networks''
Chris Nikolopoulos
``Generalization and fault tolerance in rule-based neural networks''
Hyeoncheol Kim, L. Fu
``Low level feature extraction and hidden layer neural network training''
T. Windeatt, R.G. Tebbs
``Sleeping staging by expert networks''
Hui-Huang Hsu, L. Fu, J. Principe
``Comparison of neural network and symbolic approaches
for predicting electricity generation requirements''
Terry Janssen, Eric Bleodorn, Ron Capone, Sue Kimbrough
``Reconciling connectionism with symbolism''
Roman Pozarlik
- ------------------------------------------------------------------------
MAY 10, 1994:
Registration: 7:30-11:00a.m.
========================================================================
Plenary Speech:
May 10, 9:00-9:45 a.m.
"Teaching the Multiplication Tables to a Neural Network: Flexibility vs. Accuracy"
James Anderson
========================================================================
========================================================================
Plenary Speech:
May 10, 10:00-10:45 a.m.
"Words and Weights: What the Network's Parameters
Tell the Network's Programmers"
Steve Gallant
========================================================================
########################################################################
Panel Discussions:
May 10, 11:00 a.m. - 12:20 p.m.
"The Future Direction of AI"
Chair: Chris Lacher
Panelists: James Anderson, Steve Gallant, Ronald Yager, Ron Sun,
Lawrence Bookman.
########################################################################
**** Lunch Break ****
- ------------------------------------------------------------------------
Technical Session 5: (Application Methodology I--Finance and Medicine)
Chair: Sylvian R. Ray
May 10, 1:30-2:15 p.m.
``Building a knowledge base from on-line corpora''
Lawrence A. Bookman
``Multivariate prediction using prior knowledge and
neural heuristics''
Kazuhiro Kohara, Tsutomu Ishikawa
``Applying artificial neural networks to medical knowledge domain''
Harry Burke, Philip Goodman, David Rosen
- ------------------------------------------------------------------------
- ------------------------------------------------------------------------
Technical Session 6: (Application Methodology II--Engineering)
Chair: Lawrence Bookman
May 10, 2:30-3:15 p.m.
``Integrating knowledge from multichannel signals''
Sylvian R. Ray
``Using partitioned neural nets and heuristics for
optical character recognition''
Kai Bolik, Steven Shoemaker, Divyendu Sinha, Miriam Tausner
``An expert network approach for material selection''
Vivek Goel, Jianhua Chen
- ------------------------------------------------------------------------
**** Snack Break ****
- ------------------------------------------------------------------------
Technical Session 7: (Language, Psychology, and Cognitive Science)
Chair: Steven Walczak
May 10, 3:45-4:30 p.m.
``From biological learning to machine learning''
Iver H. Iversen
``RAAMs that can learn to encode words from
a continuous stream of letters''
Kenneth A. Hester, Michael Bringmannm,
David Langan, Marino Niccolai, William Nowack
``The psychology of associative and symbolic reasoning''
Steven Sloman
- ------------------------------------------------------------------------
- ------------------------------------------------------------------------
Technical Session 8: (Integration Methodology III)
Chair: Ioannis Hatzilygeroudis
May 10, 4:45-5:30 p.m.
``Situation awareness assessments as a means of defining
learning tasks for neural networks''
Thomas English
``Integrating neural networks and expert systems for
intelligent resource allocation in academic admissions''
Steven Walczak
``Rule constraint and game playing heuristic embedded
into a feed forward neural network''
Walter H. Johnson
- ------------------------------------------------------------------------
*******
Wrap-Up
*******
- ---------------------------------------------------------------------
Please send your registration including a registration fee to:
Rob Francis
ISIKNH'94
DOCE/Conferences
2209 NW 13th Street, STE E
University of Florida
Gainesville, FL 32609-3476
USA
(Phone: 904-392-1701; fax: 904-392-6950)
[Registration fee: $250 by April 8, $300 on site, $150 for students]
- ---------------------------------------------------------------------
- ---------------------------------------------------------------------
For registration, please submit the following
information to the above address:
NAME: _______________________________________
ADDRESS: ____________________________________
_____________________________________________
_____________________________________________
_____________________________________________
_____________________________________________
INSTITUTION/COMPANY: ________________________
PHONE: ______________________________________
FAX: ________________________________________
E-MAIL: _____________________________________
- ---------------------------------------------------------------------
------------------------------
Subject: Tutorial - Neural Networks, Speech Technology, and Other Applications
From: agate!msuinfo!harbinger.cc.monash.edu.au!bunyip.cc.uq.oz.au!news.qut.edu.au!raymond@ucbvax.Berkeley.EDU (Raymond Lister)
Organization: Queensland University of Technology
Date: 10 Mar 1994 08:00:53 +0000
**************************************************************
NEURAL NETWORKS, SPEECH TECHNOLOGY, AND OTHER APPLICATIONS
-- A TUTORIAL --
Thursday April 28 and Friday April 29 1994
Queensland University of Technology, Brisbane, AUSTRALIA
Featured Speaker: PROF. NELSON MORGAN, International Computer Science
Institute Berkeley, California, USA.
PROF. MORGAN is co-author, with Herve Bourlard, of the
recent Kluwer Academic Press book ``Connectionist Speech
Recognition, A Hybrid Approach''
PROF. MORGAN will be giving a tutorial similar to the one
he gave at the NIPS-6 conference at Denver, Colorado in
December 1993. NIPS is the premier international
conference for research in artificial neural networks.
Other speakers: PROF. TOM DOWNS/AH CHUNG TSOI and co-workers from the
Speaker Verification Project,
Department of Electrical and Computer Engineering,
University of Queensland, Australia
PROF. JOACHIM DIEDERICH, Professor of Neurocomputing,
and other members of the Queensland University of
Technology Neurocomputing Research Centre.
Venue: 12th Floor,
Building ITE ("Information Technology and Engineering"),
Gardens Point Campus,
Queensland University of Technology,
2 George Street,
Brisbane, AUSTRALIA
(A short walk from the Brisbane Central Business District.)
Day 1 (2-6PM)
1. An Introduction to Neural Networks. This session will serve
as a primer for those attendees with no prior background in
artificial neural networks.
2. Demonstrations of Applications of Neural Networks at QUT. The
Neurocomputing Research Centre at QUT is developing a number
of applications, including systems for: predicting blue-green
algae blooms; advising in dairy breeding programs; predicting
the bleeding rate of patients undergoing heart bypass surgery;
and a computer assistant for the handling of electronic mail.
Day 2 (full day):
1. Connectionist Continuous Speech Recognition, by Nelson Morgan.
This will consist of three 90 minute sessions.
2. Speaker Verification Research at the University of Queensland,
by Professors Tom Downs/Ah Chung Tsoi and co-workers.
1. Overview
2. Neural Networks applied to speaker verification
3. Dynamic time warping applied to speaker verification
4. Vector quantization applied to speaker verification
5. Demonstration of a speaker verification system
Cost: Registration $A200,
Pre-Registration $150 (offer expires 1 week prior to tutorial)
Full time postgraduate students are eligible for a 50% discount
on the full fee. Proof of enrollment is required: either a
photocopy of a current student card, or a letter from the Head
of Department.
Lunch $A30 Optional, and second day only. Must be accompanied
by early registration fee, up to one week prior to tutorial.
It will be possible to register on the day, but only cash and
cheques will be acceptable. Credit cards cannot be accepted.
*******************************************************************
REGISTRATION FORM
NEURAL NETWORKS, SPEECH TECHNOLOGY, AND OTHER APPLICATIONS
Thursday April 28 and Friday April 29 1994
NAME:_______________________________
AFFILIATION:________________________________________________
ADDRESS:__________________________
__________________________
__________________________
__________________________
TEL: ____________________________ (office hours)
FAX: ____________________________
EMAIL: ____________________________
REGISTRATION (tick as appropriate)
Full Fee: $200
Early Fee: $150
Student: $100
Lunch: $ 30
________________
Total: $
- or -
I expect to attend but will pay on the day (tick)
(Notification of an expectation to attend would be
appreciated, as it will aid in making tutorial
arrangements. Such notification may be made by
electronic mail, along with above particulars.)
Make cheques payable to "Faculty Research - Neurocomputing". Credit cards
cannot be accepted.
Send the registration form and remittance to:
Neural Network Tutorial
Neurocomputing Research Centre
School of Computing Science
Queensland University of Technology
GPO Box 2434
Brisbane
Australia 4001
*******************************************************************
Connectionist Continuous Speech Recognition: A Tutorial
by Professor Nelson Morgan
Automatic Speech Recognition (ASR) has been a major topic of research
for over 40 years. While there has been much progress in this time, it
is still a difficult task, and the best systems are still quite
limited. Since computers have rapidly grown much more powerful,
statistically-oriented data-driven approaches have received much more
attention over the last 10 years. These approaches automatically learn
speech model parameters from the data, and have proven to be very successful.
The dominant approach for such systems uses Hidden Markov Models
(commonly based on an assumption of Gaussian or mixture Gaussian
probability densities for the data in each sound class) to represent
speech. However, over the last 5 years, a set of techniques have been
developed at Berkeley and elsewhere using a hybrid of connectionist
probability estimators and Hidden Markov Models. In this tutorial, the
basics of automatic speech recognition, Hidden Markov Models, and
probability estimation with layered connectionist networks will be
reviewed, followed by a more detailed explanation of the current state
of development for this class of approaches. The goal of the tutorial
will be to acquaint the participants with the major issues of
connectionist speech recognition, rather than to exhaustively review
the range of approaches under investigation worldwide.
Brief Notes about the Instructor:
Nelson Morgan is the leader of a research group at the
International Computer Science Institute whose charter is a mixture of
connectionist computational engine design and the incorporation of such
engines into research into speech and hearing in order to improve
auditory machine perception. Together with Herve Bourlard, he is the
author of the recent Kluwer Academic Press book ``Connectionist Speech
Recognition, A Hybrid Approach'', and was the co-developer (with
Bourlard) of many of these techniques. He is also on the faculty at
the University of California at Berkeley.
*******************************************************************
For further information please contact:
Dr Raymond Lister
email: raymond@fitmail.fit.qut.edu.au
or
Prof. Joachim Diederich
Tel: +617 864 2143
email: joachim@fitmail.fit.qut.edu.au
or
either by Fax: +617 864 1801
------------------------------
Subject: New Submission Deadline for ISMB '94 Conference
From: dog.ee.lbl.gov!agate!headwall.Stanford.EDU!nntp!brutlag@ucbvax.Berkeley.EDU (Doug Brutlag)
Organization: Stanford University
Date: 10 Mar 1994 13:35:30 +0000
*************** CHANGE IN SUBMISSION DEADLINE *****************
Due to late advertisement and numerous requests, we are
changing the deadline for submission of papers to March 25, 1994.
Papers must be received by March 25 to be considered.
The Second International Conference on
Intelligent Systems for Molecular Biology
August 15-17, 1994
Stanford University
Organizing Committee New Deadlines
Russ Altman, Stanford U, Stanford Papers due: March 25, 1994
Doug Brutlag, Stanford U, Stanford Replies to authors: May 6, 1994
Peter Karp, SRI, Menlo Park Revised papers due: June 3, 1994
Richard Lathrop, MIT, Cambridge
David Searls, U Penn, Philadelphia
Program Committee
K. Asai, ETL, Tsukuba A. Lapedes, LANL, Los Alamos
D. Benson, NCBI, Bethesda M. Mavrovouniotis, Northwestern U, Evanston
B. Buchanan, U of Pittsburgh G. Michaels, George Mason U, Fairfax
C. Burks, LANL, Los Alamos G. Myers, U. Arizona, Tucson
D. Clark, ICRF, London K. Nitta, ICOT, Tokyo
F. Cohen, UCSF, San Francisco C. Rawlings, ICRF, London
T. Dietterich, OSU, Corvallis J. Sallatin, LIRM, Montpellier
S. Forrest, UNM, Albuquerque C. Sander, EMBL, Heidelberg
J. Glasgow, Queen's U., Kingston J. Shavlik, U Wisconsin, Madison
P. Green, Wash U, St. Louis D. States, Wash U, St. Louis
M. Gribskov, SDSC, San Diego G. Stormo, U Colorado, Boulder
D. Haussler, UCSC, Santa Cruz E. Uberbacher, ORNL, Oak Ridge
S. Henikoff, FHRC, Seattle M. Walker, Stanford U, Stanford
L. Hunter, NLM, Bethesda T. Webster, Stanford U, Stanford
T. Klein, UCSF, San Francisco X. Zhang, TMC, Cambridge
The Second International Conference on Intelligent Systems for Molecular
Biology will take place at Stanford University in the San Francisco Bay
Area, August 14-17, 1994. The ISMB conference, held for the first time
last summer in Bethesda, MD, attracted an overflow crowd, yielded an
excellent offering of papers, invited speakers, posters and tutorials,
provided an exciting opportunity for researchers to meet and exchange
ideas, and was an important forum for the developing field. We will
continue the tradition of pre-published, rigorously refereed proceedings,
and opportunities for fruitful personal interchange.
The conference will bring together scientists who are applying the
technologies of advanced data modeling, artificial intelligence, machine
learning, probabilistic reasoning, massively parallel computing, robotics,
and related computational methods to problems in molecular biology. We
invite participation from both developers and users of any novel system,
provided it supports a biological task that is cognitively challenging,
involves a synthesis of information from multiple sources at multiple
levels, or in some other way exhibits the abstraction and emergent
properties of an "intelligent system." The four-day conference will
feature introductory tutorials (August 14), presentations of original
refereed papers and invited talks (August 15-17).
Paper submissions should be single-spaced, 12 point type, 12 pages
maximum including title, abstract, figures, tables, and bibliography with
titles. The first page should include the full postal address, electronic
mailing address, telephone and FAX number of each author. Also, please
list five to ten keywords describing the methods and concepts discussed
in the paper. State whether you wish the paper to be considered for oral
presentation only, poster presentation only or for either presentation
format. Submit 6 copies to the address below. For more information,
please contact ismb@camis.stanford.edu.
Proposals for introductory tutorials must be well documented, including
the purpose and intended audience of the tutorial as well as previous
experience of the author in presenting such material. Those considering
submitting tutorial proposals are strongly encouraged to submit a one-page
outline, before the deadline, to enable early feed-back regarding topic
and content suitability. The conference will pay an honorarium and
support, in part, the travel expenses of tutorial speakers.
Limited funds are available to support travel to ISMB-94 for those students,
post-docs, minorities and women who would otherwise be unable to attend..
Please submit papers and tutorial proposals to:
Intelligent Systems for Molecular Biology
c/o Dr. Douglas L. Brutlag
Beckman Center, B400
Department of Biochemistry
Stanford University School of Medicine
Stanford, California 94305-5307
------------------------------
Subject: Workshop: Constructive Induction and Change of Representation
From: dog.ee.lbl.gov!agate!howland.reston.ans.net!news.ans.net!news.nynexst.com!fawcett@ucbvax.Berkeley.EDU (Tom Fawcett)
Organization: NYNEX Science and Technology
Date: 10 Mar 1994 16:49:11 +0000
ML/COLT '94 WORKSHOP
Constructive Induction and Change of Representation
This workshop is part of the 1994 ML/COLT (Machine Learning /
Computational Learning Theory) Conference. Workshops will be held on
Sunday, July 10th, on the New Brunswick campus of Rutgers University.
- ------------------------------------------------------------
An appropriate representation is critical to the success of an inductive
learning task. In difficult learning problems (eg protein folding, word
pronunciation, gene identification), considerable human effort is often
required to identify useful terms of the representation language. In an
effort to make learning more autonomous, researchers have investigated the
problem of generating or modifying new representations automatically.
The past five years have seen a significant increase in the amount of work in
this area. Some methods developed have been able to effect increases in
classification accuracy. Others are able to derive features similar to those
discovered previously by humans. Still other systems have demonstrated
impressive performance improvement through the construction of new
representations.
In spite of these successes, we are still far from understanding the range and
limitations of current methods, or the kind of representation change that
real-world domains may require. The objective of this workshop is to examine
issues in current work and to review progress made so far. The workshop will
also serve as a forum for the exchange of ideas among researchers actively
working on these issues. Topics of interest include, but are not limited to,
the following:
- Empirical approaches and the use of inductive biases
- Use of domain knowledge in the construction and evaluation of new terms
- Construction of or from relational predicates
- Introduction of new terms by analytic theory revision systems
- Unsupervised learning and credit assignment in constructive induction
- Interpreting hidden units as constructed features
- New terms as indices in instance-based learning or case-based reasoning
- Constructive induction in human learning
- Experimental studies of constructive induction systems
- Theoretical proofs, frameworks, and comparative analyses
- Comparison of techniques from empirical learning, analytical
learning, classifier systems, and neural networks
WORKSHOP FORMAT
Attendance to the workshop will be open. The workshop will consist of
presentations of accepted papers and a final panel discussion. The
panel will recap the workshop and discuss the state of constructive
induction and current open questions.
SUBMISSIONS
Paper submissions should not exceed 3000 words (about six single-spaced pages,
including figures and tables, but excluding bibliography). Four copies of
each paper should be sent to the contact address below. Alternatively, one
copy of a postscript file may be sent via e-mail. Each paper should include
an e-mail contact address of one of the authors. The papers will comprise a
set of working notes, copies of which will be available at the workshop.
We encourage descriptions of work in progress as well as position papers.
Authors are encouraged to evaluate their systems on real-world domains and
to critique their methods with respect to the following questions:
- - In your system, what is the relationship between the feature generation and
induction? Can the feature generation method be adapted to other forms of
induction?
- - How does the method evaluate or select the features that it generates?
- - Is the method sensitive to the cost of the features? Can it create
features of unbounded expense?
- - Whasensitive to the cost of the features? Can it create
features of unbounded expense?
- - What real-world domain(s) has the method been applied to? What
characteristics of each domain makes feature generation useful or necessary?
For what general class of domain might the method be useful?
- - Can the method exploit existing domain knowledge? What forms of domain
knowledge can be exploited?
- - What features are already known for the domain being addressed? Can the
method re-derive any of them?
- - What forms (eg, propositional, relational, numerically weighted) can the
generated features take? Does this limit the method?
SCHEDULE
Paper submissions due 25 April
Decisions made, submitters get feedback 22 May
Final working-note submissions due 15 June
Workshop 10 July
PROGRAM COMMITTEE
Tom Fawcett (chair), NYNEX Science and Technology
James Callan, University of Massachusetts at Amherst
Chris Matheus, GTE Laboratories Inc.
Ryszard Michalski, George Mason University
Michael Pazzani, University of California at Irvine
Larry Rendell, University of Illinois at Urbana-Champaign
Rich Sutton, GTE Laboratories Inc.
CONTACT ADDRESS
Tom Fawcett
NYNEX Science and Technology
500 Westchester Ave.
White Plains, NY 10604
e-mail: fawcett@nynexst.com
------------------------------
Subject: CFP-Spl. issue of EJOR on Neural Nets and OR
From: sharda@orcs.bus.okstate.edu (Ramesh Sharda)
Date: Sat, 12 Mar 1994 14:28:33 -0600
Pl. post the follwoing CFP on your digest. Thanks
CALL FOR PAPERS
Special issue on
Neural Networks and Operations Research/Management
Science
European Journal of Operational Research
Original papers are sought for publication (in early 1996) in
a special issue of the European Journal of Operational Research on
neural networks and operations research/management science. The
purpose of this special issue is to address the recent advances in
the fields of neural networks and operations research/management
science (OR/MS). From an operations research/management science
perspective, the neural networks offer three opportunities:
alternatives to traditional statistical methods, new approaches for
optimization, and a problem domain to apply OR/MS algorithms. The
objectives of this special issue are to present the latest research
activities in all three dimensions of the interface between neural
networks and operations research/management science. The papers
could describe the use of OR/MS methodology for developing neural
networks or the use of neural networks for solving problems where
the traditional OR/MS techniques have been employed. Papers
reporting applications of neural networks in cooperation with OR/MS
techniques are especially welcome.
Authors are invited to submit four copies of their papers to
either of the guest editors listed below by August 1, 1994. All
submitted papers will be strictly reviewed according to the
procedures of the Journal to ensure the relevance to the special
issue and originality/quality of the contributions.
Guest Editors:
Professor Ramesh Sharda
College of Business Administration
Oklahoma State University
Stillwater, OK 74078 USA
(405) 744-8638 / Fax: (405) 744-5180
email: sharda@vm1.ucc.okstate.edu
Professor Jun Wang
Dept. of Industrial Technology
University of North Dakota
Grand Forks, ND 58202 USA
(701) 777-2201 / Fax: (701) 777-4320
email: jwang@plains.nodak.edu
- --
Ramesh Sharda
College of Business Administration, Oklahoma State University
Stillwater, OK 74078 USA
email: sharda@orcs.bus.okstate.edu or sharda@vm1.ucc.okstate.edu
------------------------------
End of Neuron Digest [Volume 13 Issue 14]
*****************************************
