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Neuron Digest Volume 11 Number 49
Neuron Digest Friday, 27 Aug 1993 Volume 11 : Issue 49
Today's Topics:
`Statistical Aspects of Neural Networks'
Thesis proposal, comments solicted
cybernetics & AI
Basins of Attraction of Cellular Automata
S/w for forecast
new member hello
RFP Research - McDonnell-Pew Program
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). Back issues requested by
mail will eventually be sent, but may take a while.
----------------------------------------------------------------------
Subject: `Statistical Aspects of Neural Networks'
From: ripley@stats.ox.ac.uk (Prof. Brian Ripley)
Date: 19 Aug 93 10:45:09 +0000
Unfortunately, Neuron Digest has just posted an announcement of this
paper dated 20 July 1992 (note, 13 months ago). The paper is now
published, and the publisher quite reasonably wants people to buy the
book, so the electronic version is no longer available on our ftp server.
The details are:
B.D. Ripley (1993) Statistical aspects of neural networks. In
`Networks and Chaos -- Statistical and Probabilistic Aspects'
eds O.E. Barndorff-Nielsen, J.L. Jensen and W.S. Kendall. Chapman & Hall.
ISBN 0 412 46530 2. pp. 40-123.
I'm sorry that several people have been misled by the very belated
announcement, completely outside my control.
Brian Ripley
[[ Editor's Note: I have replied personally to Brian Ripley. As I
thought I had mentioned in an earlier Digest, I felt the policy of
"better late than never" was a useful one. At least one author felt
differently. I am *very* interested in comments from you, the *readers*,
about my editorial policy. ]]
------------------------------
Subject: Thesis proposal, comments solicted
From: "ANTHONY ROBINS." <COSCAVR@rivendell.otago.ac.nz>
Date: Fri, 13 Aug 93 16:14:00 +1200
Dear moderator
Could you circulate on Neuron Digest the following thesis proposal,
regarding Information Spaces.
Many thanks.
>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
To whoever is interested:
Following is a simplified version of my proposal for a PhD thesis. This may or
may not be of interest to the current audience, but if it is, I should very
much appreciate comments and/or pointers into relevant areas of available
literature - or maybe pointers at others who are doing similar things.
In anticipation of your hints, pointers, flames, or whatever - many thanks!
Till Noever
SnailMail: Department of Computer Science, EMail: cosctill@otago.ac.nz
University of Otago,
PO Box 56, Dunedin,
New Zealand
SYNOPSIS OF PROPOSAL:
"In my M.Sc. thesis I proposed a generalised view of computation and cognition
as a process of projection of points in high-dimensional state spaces into
lower-dimensional subspaces of these. The space containing all possible
computational state spaces has been dubbed 'Information Space', and the process
of projection into subspaces 'Information Space Reduction'. The properties of
computational processes and the objects they interact with, may therefore be
represented in terms of the topological and geometrical properties of of this
space, or its relevant subspaces. Approaches to computation, especially with
neural nets, which use a similar kind of framework, have already been proposed;
particularly by Amari and his co-workers.
One result of applying the Information Space paradigm is the notion that
'neural' and 'symbolic' computation must be understood as being qualitatively
equivalent, in spite of their perceived differences. One of the reasons why the
apparent differences are understood as somehow fundamental, rather than
essentially quantitative, is that researchers who attempt to generate abstract
models for computational systems begin with the systems themselves - their
physical or logical structure, or the processes that are conjectured to take
place within them. The particular properties of those systems thereby assume
excessive importance, and the resulting models will be correspondingly limited.
The limitation of those models in turn, makes it difficult to explain how - in
a biological cognitive system like the human brain, for example - high-level
symbolic constructs and representations can exist and interact on a matrix
which appears to be essentially a very complex neural network.
In my PhD thesis and research I intend to take the reverse approach, and
propose a model which begins by considering a, recursively definable,
high-dimensional Information Space, whose subspaces may have various topologies
and metrics. Initially this will be done theoretically, to be followed by a
computational implementation. This model should be self-consistent, and, in its
basic form, require no references to vocabulary other than the limited set
which it is based upon.
Computational and psychological concepts such as 'representation', 'learning',
and 'memory' will subsequently be explicitly defined within this framework.
Using these definitions, the system will then be presented with a simple
computational or cognitive task, such as representing, learning about, and
re-representing - that is, providing some output information about - a simple
object; such as, for example, a Necker Cube.With the aim of identifying just
how the Information Space Reductions map onto standard computing and
psychological vocabulary.
Success in this venture would be a significant step along the way towards
producing a fundamental theory of all that computational and cognitive activity
which may take place in physically realisable systems."
------------------------------
Subject: cybernetics & AI
From: cavonius@ifado.arb-phys.uni-dortmund.de
Date: Fri, 13 Aug 93 09:51:12 +0100
I suspect that a large part of the answer to Galley's question
on what happened to cybernetics is that time plays a role:
it's unfortunate, but in this field - to a greater extent than
in science in general - activity is dictated by what happens
to be fashionable at any given moment. Cybernetics was all the
rage in the late 40s and 50s. Too much enthusiasm was generated,
and when it failed to achieve everything that was expected of
it was renounced in favor of AI. To a certain extent the same
is now happening to AI, although AI will be harder to kill off
because our investment in it is much larger than the investment
in cybernetics was.
Dick Cavonius
------------------------------
Subject: Basins of Attraction of Cellular Automata
From: Andrew Wuensche <100020.2727@CompuServe.COM>
Date: 13 Aug 93 06:46:08 -0500
Basins of Attraction of Cellular Automata
ref recent enquiry from John Boller..
>I am looking for references to the comparison of
>Basins of Attraction of Cellular Automata and
>Neural Networks.
>I would greatly appreciate anyone who could point
>me in the correct direction.
The book "The Global Dynamics of Cellular Automata" and pre-print "The
Ghost in the Machine" detailed below may be of interest..
The pre-print describes recent work on the basins of attraction of
random Boolean networks (disorderd cellular automata), and implications
on memory and learning (the abstract was posted in Volume 11:Issue 44 of
Neuron Digest). Currently only hard-copies are available. To
request copies, send email to:
andywu@cogs.susx.ac.uk, or write to
Andy Wuensche, 48 Esmond Road, London W4 1JQ, UK
dont forget to give a surface mail address.
The Global Dynamics of Cellular Automata
========================================
An Atlas of Basin of Attraction Fields of
One-Dimensional Cellular Automata.
Andrew Wuensche
Mike Lesser
Foreword by Chris Langton
Diskette included for PC-compatible computers.
Santa Fe Institute Studies in the Sciences of Complexity
Reference Vol 1 Addison-Wesley IBSN 0-201-55740-1 1992
The Ghost in the Machine
========================
Basins of Attraction of Random Boolean Networks
Andrew Wuensche
Cognitive Science Research Paper 281, University of Sussex, 1993 (to be
published in Artificial Life III, Santa Fe Institute Studies in the
Sciences of Complexity).
------------------------------
Subject: S/w for forecast
From: okoks@pc.ibt.dk
Date: Fri, 13 Aug 93 11:39:00 -0800
Hello,
I have for some time now read about the use of ANN in economics, and would
like to do some estimations/forecasts myself.
The problem is I do not have a program for this purpose. Can anyone recommend
such a program, preferably free- or shareware, as my funding is limited?
TIA,
Karsten Strobek
Institute of Economics Phone: +45 35 32 30 25
University of Copenhagen Fax: +45 35 32 30 00
Studiestraede 6 Internet: Okoks@pc.ibt.dk
DK-1455 Copenhagen K
Denmark
------------------------------
Subject: new member hello
From: anich@cordmc.dnet.etn.com (Steve Anich, Eaton Corporation, Milwaukee)
Date: Mon, 16 Aug 93 11:31:22 -0500
Hi,
I'm new to Neural Networks. I'm currently trying to get a ANN to
recognize the difference between a good or bad weld based
on a voltage & current signature. I was using a product
from HNC to do this, but it has been crashing like crazy. I'm
going start rolling my own (after I'm done with this message),
in case the HNC tech support people can't help.
My main interest in ANN for signal/siganture classification. I expect
to be using it also with multiple sensor shortly. I am
interested (but blissfully ignorant) in ANN which can
optimize themselves over time (while be used).
Question: Does anyone know of some existing code (say C++
classes for ANN) that I can ftp from somewhere? I'd rather not
start from scratch.
FYI: I heard about this digest on a FAQ from somewhere aboyt AI.
Thanks,
- --steve
..................................................................
Steve Anich
Eaton Corporation R&D Center | email: anich@cordmc.dnet.etn.com
Systems & Software Technologies | stevea48@aol.com
4201 N. 27th Street | voice: 414-449-6457
Milwaukee WI 53216, USA | fax: 414-449-6221
..................................................................
"I eat kludges for breakfast, Buckwheat!"
------------------------------
Subject: RFP Research - McDonnell-Pew Program
From: Cognitive Neuroscience <cns@clarity.Princeton.EDU>
Date: Tue, 17 Aug 93 11:30:02 -0500
McDonnell-Pew Program
in Cognitive Neuroscience
SEPTEMBER 1993
Individual Grants-in-Aid
for Research
Program supported jointly by the
James S. McDonnell Foundation
and The Pew Charitable Trusts
INTRODUCTION
The McDonnell-Pew Program in Cognitive Neuroscience has been
created jointly by the James S. McDonnell Foundation and The Pew Charitable
Trusts to promote the development of cognitive neuroscience. The foundations
have allocated $20 million over a five-year period for this program.
Cognitive neuroscience attempts to understand human mental events by
specifying how neural tissue carries out computations. Work in cognitive
neuroscience is interdisciplinary in character, drawing on developments in
clinical and basic neuroscience, computer science, psychology, linguistics,
and philosophy. Cognitive neuroscience excludes descriptions of psychological
function that do not address the underlying brain mechanisms and
neuroscientific descriptions that do not speak to psychological function.
The program has three components.
(1) Institutional grants, which have already been awarded,
for the purpose of creating centers where cognitive
scientists and neuroscientists can work together.
(2) Small grants-in-aid, presently being awarded, for individual
research projects to encourage Ph.D. and M.D. investigators
in cognitive neuroscience.
(3) Small grants-in-aid, presently being awarded, for individual
training projects to encourage Ph.D. and M.D. investigators
to acquire skills for interdisciplinary research.
This brochure describes the individual grants-in-aid for research.
RESEARCH GRANTS
The McDonnell-Pew Program in Cognitive Neuroscience will issue a
limited number of awards to support collaborative work by cognitive
neuroscientists. Applications are sought for projects of exceptional merit
that are not currently fundable through other channels and from investigators
who are not at institutions already funded by an institutional grant from
the program. In order to distribute available funds as widely as possible,
preference will be given to applicants who have not received previous grants
under this program.
Preference will be given to projects that are interdisciplinary in
character. The goals of the program are to encourage broad participation
in the development of the field and to facilitate the participation of
investigators outside the major centers of cognitive neuroscience.
There are no U.S. citizenship restrictions or requirements, nor does
the proposed work need to be conducted at a U.S. institution, providing the
sponsoring organization qualifies as tax-exempt as described in the
"Applications" section of this brochure. Ph.D. thesis research of graduate
students will not be funded.
Grant support under the research component is limited to $30,000
per year for two years. Indirect costs are to be included in the $30,000
maximum and may not exceed 10 percent of total salaries and fringe
benefits. These grants are not renewable after two years.
The program is looking for innovative proposals that would, for
example:
* combine experimental data from cognitive psychology and neuroscience;
* explore the implications of neurobiological methods for the study
of the higher cognitive processes;
* bring formal modeling techniques to bear on cognition, including
emotions and higher thought processes;
* use sensing or imaging techniques to observe the brain during
conscious activity;
* make imaginative use of patient populations to analyze cognition;
* develop new theories of the human mind/brain system.
This list of examples is necessarily incomplete but should suggest the
general kind of proposals desired. Ideally, a small grant-in-aid for
research should facilitate the initial exploration of a novel or risky
idea, with success leading to more extensive funding from other sources.
APPLICATIONS
Applicants should submit five copies of the following information:
* a brief, one-page abstract describing the proposed work;
* a brief, itemized budget that includes direct and indirect
costs (indirect costs may not exceed 10 percent of total
salaries and fringe benefits);
* a budget justification;
* a narrative proposal that does not exceed 5,000 words; the
5,000-word proposal should include:
1) a description of the work to be done and where
it might lead;
2) an account of the investigator's professional
qualifications to do the work;
3) an account of any plans to collaborate with other
cognitive neuroscientists;
4) a brief description of the available research
facilities;
* curriculum(a) vitae of the participating investigator(s);
* an authorized document indicating clearance for the use of
human and animal subjects;
* an endoresement letter from the officer of the sponsoring
institution who will be responsible for administering the
grant.
One copy of the following items must also be submitted along with the
proposal. These documents should be readily available from the sponsoring
institution's grants or development office.
* A copy of the IRS determination letter, or the international
equivalent, stating that the sponsoring organization is a nonprofit,
tax-exempt institution classified as a 501(c)(3) organization.
* A copy of the IRS determination letter stating that your organization
is not listed as a private foundation under section 509(a) of the
Internal Revenue Service Code.
* A statement on the sponsoring institution's letterhead, following
the wording on Attachment A and signed by an officer of the
institution, certifying that the status or purpose of the
organization has not changed since the issuance of the IRS
determinations. (If your organization's name has changed, include
a copy of the IRS document reflecting this change.)
* An audited financial statement of the most recently completed fiscal
year of the sponsoring organization.
* A current list of the names and professional affiliations of the
members of the organization's board of trustees and the names and
titles of the principal officers.
Other appended documents will not be accepted for evaluation and will be
returned to the applicant. Any incomplete proposals will also be returned
to the applicant.
Submissions will be reviewed by the program's advisory board.
Applications must be postmarked on or before FEBRUARY 1 to be considered
for review.
INFORMATION
McDonnell-Pew Program in Cognitive Neuroscience
Green Hall 1-N-6
Princeton University
Princeton, New Jersey 08544-1010
Telephone: 609-258-5014
Facsimile: 609-258-3031
Email: cns@clarity.princeton.edu
ADVISORY BOARD
Emilio Bizzi, M.D.
Eugene McDermott Professor in the Brain
Sciences and Human Behavior
Chairman, Department of Brain and Cognitive Sciences
Massachusetts Institute of Technology, E25-526
Cambridge, Massachusetts 02139
Sheila E. Blumstein, Ph.D.
Professor of Cognitive and Linguistic Sciences
Dean of the College
Brown University
University Hall, Room 218
Providence, Rhode Island 02912
Stephen J. Hanson, Ph.D.
Head, Learning Systems Department
Siemens Corporate Research
755 College Road East
Princeton, New Jersey 08540
Jon H. Kaas, Ph.D.
Centennial Professor
Department of Psychology
Vanderbilt University
301 Wilson Hall
111 21st Avenue South
Nashville, Tennessee 37240
George A. Miller, Ph.D.
Director, McDonnell-Pew Program in Cognitive Neuroscience
James S. McDonnell Distinguished University Professor of Psychology
Department of Psychology
Princeton University
Princeton, New Jersey 08544-1010
Mortimer Mishkin, Ph.D.
Chief, Laboratory of Neurpsychology
National Institute of Mental Health
9000 Rockville Pike
Building 49, Room 1B80
Bethesda, Maryland 20892
Marcus E. Raichle, M.D.
Professor of Neurology and Radiology
Division of Radiation Sciences
Washington University School of Medicine
Campus Box 8225
510 S. Kingshighway Boulevard
St. Louis, Missouri 63110
Endel Tulving, Ph.D.
Tanenbaum Chair in Cognitive Neuroscience
Rotman Research Institute of Baycrest Centre
3560 Bathurst Street
North York, Ontario M6A 2E1
Canada
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End of Neuron Digest [Volume 11 Issue 49]
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