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Neuron Digest Volume 11 Number 08
Neuron Digest Tuesday, 2 Feb 1993 Volume 11 : Issue 8
Today's Topics:
building energy predictor shootout -- data available by anon ftp
How to build a Boltzmann machine?
new cluster version available
The Hunt for Info
back prop nn refs request
Is there a Fuzzy systems digest?
Wet brains as constraints on neural networks
Postdoc - computational neurobiology
Job Offer: Research on Financial Analysis in Santa Fe NM
Computational Biology Faculty Position
Send submissions, questions, address maintenance, and requests for old
issues to "neuron-request@cattell.psych.upenn.edu". The ftp archives are
available from cattell.psych.upenn.edu (130.91.68.31). Back issues
requested by mail will eventually be sent, but may take a while.
----------------------------------------------------------------------
Subject: building energy predictor shootout -- data available by anon ftp
From: "Michael C. Mozer" <mozer@dendrite.cs.colorado.edu>
Date: Tue, 19 Jan 93 12:35:00 -0700
Data for the building energy predictor shootout announced recently is
available by anonymous ftp from ftp.cs.colorado.edu A sample script to
access the data follows below.
The files in the energy-shootout directory, all ASCII format, include:
rules.asc The shoot out rules and details of the competition
atrain.dat The training portion of data set A.
atest.dat The testing portion of data set A.
btrain.dat The training portion of data set B.
btest.dat The testing portion of data set B.
dataform.at Details of the format of these four data files along
with units of all data.
- -------------------------------------------------------------------------------
% ftp ftp.cs.colorado.edu
Connected to bruno.cs.colorado.edu.
220 bruno FTP server (SunOS 4.1) ready.
Name (ftp.cs.colorado.edu:mozer): anonymous
331 Guest login ok, send ident as password.
Password:
230-Guest login ok, access restrictions apply.
ftp> cd pub/cs/energy-shootout
250 CWD command successful.
ftp> ls
200 PORT command successful.
150 ASCII data connection for /bin/ls (128.138.204.25,2207) (0 bytes).
atest.dat
atrain.dat
btest.dat
btrain.dat
dataform.at
read.me
rules.asc
226 ASCII Transfer complete.
79 bytes received in 11 seconds (0.0073 Kbytes/s)
ftp> get atest.dat
200 PORT command successful.
150 ASCII data connection for atest.dat (128.138.204.25,2208) (93657 bytes).
226 ASCII Transfer complete.
local: atest.dat remote: atest.dat
94940 bytes received in 1.1 seconds (82 Kbytes/s)
ftp> bye
221 Goodbye.
------------------------------
Subject: How to build a Boltzmann machine?
From: Ulf Rimkus <RIMKUS_U@DMRHRZ11.HRZ.Uni-Marburg.DE>
Date: Wed, 20 Jan 93 17:24:45 +0700
Dear Neural Netters!
Since a few weeks I am working on some kind of a stochastic net (see
McClelland, James L., Stochastic Interactive Processes and the Effect of
Context on Perception, Cognitive Psychology 23, 1-44, (1991)). Now, I
would like to try out the Boltzmann machine, but I can't find any easy to
use step-by-step introduction to this thing (I am a psychologist NOT a
mathematician). However, I am looking for a piece of C or PASCAL-source
(with tons of comments) implementing a Boltzmann machine. It should be a
version without learning, because the net has 'hardwired' connections (of
course, with learning could be interesting, too, but don't matches my
prefered goal).
Maybe there is a kind soul, who likes to share his knowledge with me?
Greetings, Ulf.
INTERNET: rimkus_u@dmrhrz11.hrz.uni-marburg.de-----------------------------
Hey Fremder, meine besten Freunde sind Fremde, aber Du warst noch nie hier!
BITNET: rimkus_u@dmrhrz11---------------------------------------------------
------------------------------
Subject: new cluster version available
From: Andreas Stolcke <stolcke@ICSI.Berkeley.EDU>
Date: Wed, 20 Jan 93 11:22:01 -0800
I'm releasing a new version of the time-honored cluster program (that
also does PCA). I recently made a small change to the algorithm that
speeds clustering up by a factor of n (the number of data points). The
algorithm now runs in time O(n^2) (formerly O(n^3)) and uses memory O(n)
(formerly O(n^2)). On a sparcstation2, this means you can cluster a
1000-by-10 data set in 39 secs as opposed to 230 secs. Systems short on
memory should see even more dramatic improvements due to reduced paging.
As before, the source code is availabe by ftp:
% mkdir cluster; cd cluster
% ftp ftp.icsi.berkeley.edu
ftp> cd pub/ai
ftp> binary
ftp> get cluster-2.5.tar.Z
ftp> quit
% zcat cluster-2.5.tar.Z | tar xf -
% make # after looking over the Makefile
--Andreas
------------------------------
Subject: The Hunt for Info
From: okoks@pc.ibt.dk
Date: Wed, 20 Jan 93 14:49:00 -0800
[[ Editor's Note: Readers may remember a very extensive bibliography
published in Neuron DIgest last year in V10 #11 with an addendum in V10
#15. Any additions? -PM ]]
Dear Sir,
As I am new to the wonderful world of electronic mail and lists, I don't
know if I am following the right procedure, so please look kindly on the
mistakes I might/will make.
I am studying economics, and have just started to learn about neural net-
works. What I would like to do, is implement neural networks on
economics, to see if the efficiency of forecasting is better that with
the traditional macroeconomic models.
If you know of any literature on this subject or of people who have
worked in this area, I would be most great full for the information. Any
help will be greatly appreciated. Please excuse my bad English.
I remain,
Karsten Strobek
Institute of Economics Phone: +45 33 91 21 66/377
University of Copenhagen Fax: +45 33 12 00 01
Studiestraede 6 Internet: okoks@pc.ibt.dk
DK-1455 Copenhagen K
Denmark
------------------------------
Subject: back prop nn refs request
From: PRACHTWE%MEMSTVX1.bitnet@CUNYVM.CUNY.EDU
Date: Thu, 21 Jan 93 11:18:00 -0600
I have not kept up with the latest developments in the use of back prop
nn's for the classification problem. Could someone help with the full
references to work on justifing the use of a single hidden layer, e.g.
Hecht-Nielsen, Cybenko, Fukushima, Hornik? Also references for guidance
in selection the number of hidden nodes, e.g. Andrew Barron or Moody.
Finally, work in intrepreting the weights on the hidden nodes in some
sort of approximate causal analysis.
William E. Pracht
Dept MIS
Memphis State University
Memphis, TN 38152
PRACHTWE@MEMSTVX1
------------------------------
Subject: Is there a Fuzzy systems digest?
From: Mounir Ben Ghalia <MBG9991@TNTECH.EDU>
Date: 25 Jan 93 14:03:19 -0600
[[ Editor's Note: In general, perhaps someone might like to submit a list
of other lists which readers may find tangentially interesting? Hmmm,
maybe that would mean all of Internet... -PM ]]
I would like to ask if any body knows of a Fuzzy systems digest (or
listserv) similar to this digest.
Thank you,
M. Ben Ghalia
Tennessee Tech. University
Dept. Electrical Enginneering
Cookeville, TN 38505
E-mail: mbg9991@tntech.bitnet
------------------------------
Subject: Wet brains as constraints on neural networks
From: Harry Jerison <IJC1HJJ@MVS.OAC.UCLA.EDU>
Date: Mon, 25 Jan 93 15:03:00 -0800
[[ Editor's Note: Many thanks to Harry for this contribution. I'm ready
(in my copious spare time, of course) to go and investigate this book on
the basis of this recommendation. I'm not sure I would know about it
with this review. As always, reviews of books, articles, or conferences
are welcome. -PM ]]
From some recent contributions in Neuron Digest, I sense an
increasing interest in wet brains as constraints on neural networks.
Last year I listed: V. Braitenberg and A. Schuez's ANATOMY OF THE CORTEX:
STATISTICS AND GEOMETRY (New York, Berlin, Heidelberg: Springer Verlag,
1991. 249 pp. $39.00) as a useful book for theoreticians and mentioned
that I had a review of it in press in CONTEMPORARY PSYCHOLOGY, a journal
of reviews published by psychologists. With apologies to my AI and NN
friends (see the concluding lines), and in the hope that they (and
non-friends as well) will let me know if they are interested in this sort
of communication, here are extracts (sans italics) from that review:
"Primarily determined from data on the mouse brain, (Braitenberg &
Schuez) is a magnificent achievement in anatomical analysis and
integration that deepens our understanding of the way a brain can work to
generate a mind. That "mind" was described by Hebb (ORGANIZATION OF
BEHAVIOR, New York, Wiley, 1949), and Braitenberg and Schuez provide the
anatomical background for Hebb's cell assemblies and "Hebbian neurons" as
living things rather than theoretical constructs or computer artifacts....
"The anatomy in Braitenberg & Schuez is a description of the
geometry and statistics of the cortex (as promised in the subtitle) that
would be required for understanding a brain as an information-processing
system. We learn many new things. We learn something about the size of
the brain as a system of connections. We learn, for example, that there
are about 100 billion (10^11) synapses in the mouse's cortex and about
100 trillion (10^14) in humans. We learn that information-processing
capacity is about the same PER UNIT VOLUME in brains of all mammals, and
we are shown how these quantities are estimated. We learn that the
majority of synapses in the mouse cortex are excitatory and not
inhibitory. By comparing brains of altricial mice with those of
precocial guinea pigs, . . .Braitenberg and Schuez (p.137) 'conclude
(their) search for the anatomical traces of learning with two likely
candidates, the number of vesicles on the presynaptic side of a synapse,
and, for synapses involving spines, the thickness of the spine.'
"Traces of learning are about engrams, and the preponderance of
excitatory synapses is a necessary element for a nervous system that
would produce Hebbian cell assemblies. Braitenberg and Schuez also
estimate the connectivity of neuronal systems, to suggest how cell
assemblies would, in fact, be assembled.
"Why do psychologists need such information? They need it to write
acceptable theories of the mind of the sort that Hebb wrote. Some years
ago, I criticized Dalbir Bindra in these pages (CONTEMPORARY PSYCHOLOGY
22:417-419, 1977) for relying heavily on Hebb's model, which I thought
too speculative to support Bindra's theorizing. Hebb himself came to
Bindra's defense, criticizing me as seeing the brain "from afar," and
implying that cell assemblies had already been demonstrated. I argued
with Hebb (CONTEMPORARY PSYCHOLOGY 22:849-50, 1977), but after reading
Braitenberg and Schuez I am less inclined to argue. The assemblies may
remain theoretical, but they are close to the status of atoms as
understood at the turn of the century, when the skeptical physicist-
philosopher, Ernst Mach, would challenge other physicists to show him
one. I am still put off by the artificial intelligencers, by 'neural
networks' that exist only in computer programs, and by cognitive
scientists who have become true believers in these silicon figments.
Braitenberg and Schuez have begun to convert me."
(From CONTEMPORARY PSYCHOLOGY 1992, 37:927-928.)
------------------------------
Subject: Postdoc - computational neurobiology
From: "Read Montague" <read@helmholtz.sdsc.edu>
Date: Tue, 19 Jan 93 21:34:39 -0800
POSTDOCTORAL POSITION
DIVISION OF NEUROSCIENCE
BAYLOR COLLEGE OF MEDICINE
A postdoctoral position is available beginning after July, 1993. The
position is for one to three years. I am seeking individuals interested
in the function of the vertebrate brain. In particular, individuals
interested in the problem of how three dimensional neuroanatomy
self-organizes into functioning neuronal networks, the range of
mechanisms required to explain this self-organizing capability, and the
behaviors of the developed networks. I am interested in theoreticians
who have a committment to dealing with the facts of biological life
and/or experimentalists interested in theory and experiment. A more
explicit description of the interests of the lab is given below.
Interested parties should send a C.V. and a brief statement of research
interests to the address listed below.
Present address:
P. Read Montague
Computational Neurobiology Lab
The Salk Institute
10010 North Torrey Pines Rd
La Jolla, CA 92037
e-mail: read@helmholtz.sdsc.edu
fax: (619) 587-0417
RESEARCH INTERESTS OF THE LAB
The primary focus of this laboratory is how three dimensional
neuroanatomy self-organizes into functioning neuronal networks, the
range of mechanisms required to explain this self-organizing
capability, and the behaviors of the developed networks. The approach
focuses on dendritic and axonal development as this development
relates to the systems-level functions of the developed network. A
particular emphasis is placed on computational and theoretical
approaches, but experimental techniques are also employed. The goal is
not to make the theories simply biologically plausible, but to ground
them initially with reliable biological facts so that the synthesized
network behavior has a chance both to explain and extend experiments.
We are particularly interested in correlational mechanisms of neural
development and learning. A separate but related interest of the lab
is the role of reinforcement signals in the activity-dependent
self-organization of the cortex. Recent work has focused on recasting
activity-dependent development in a manner which gives reinforcement
signals a natural role during the development of cortical maps and
sensory-motor transformations.
To place proposed mechanisms of synaptic plasticity and transmission
into a more realistic context, we are exploring both
activity-dependent and activity-independent mechanisms through which
three dimensional dendritic structure develops. We are interested in
the contribution such development makes to computational theories of
cortical map formation and function.
Our experimental efforts are focused upon the function of synapses in
the mammalian cerebral cortex with particular interest in how a
synapse's local environment modulates its function. Recent
experimental efforts have focused on the role of N-methyl-D-aspartate
(NMDA) receptors and nitric oxide production in synaptic transmission
in the mammalian cerebral cortex. These experiments have utilized in
vitro brain slice physiology, electrochemistry, immunocytochemistry,
and standard biochemical methods.
- ------------------------------------------------------
The Division of Neuroscience at Baylor offers many possibilities
for collaboration with a number of excellent laboratories exploring
questions ranging from the modulation of ionic channel function to
visual processing in the mammalian cortex. Listed below are some of the
faculty members and their areas of interest.
John Maunsell :
Processing of visual information by cerebral cortex with a particular
interest in neural representations contributing to higher functions
such as memory or visual guidance of behaviors.
Nikos Logothetis:
Physiological mechanisms mediating visual perception and object
recognition.
Dan Ts'o :
Neuronal mechanisms of information processing and visual
perception through a combination of conventional electrophysiological
and anatomical techniques and more novel methods such as optical
imaging and cross-correlation analysis.
Sarah Pallas :
Functional development of the central visual system; focusing on the
relative roles of sensory input and intrinic connectivity in
establishing the response properties of target neurons.
Dan Johnston :
Cellular and molecular mechanisms of long-term synaptic
plasticity.
Peter Saggau :
Mechanisms that control the behavior of populations of
nerve cells and in vitro optical recording methods.
James W. Patrick :
Molecular mechanisms responsible for the function
and modification of synapses in the central nervous system.
John A Dani :
Synaptic communication and the structure and function of
ion channels.
David Sweatt :
Biochemical mechanisms of long-term changes in neuronal
function with particular emphasis on long-term potentiation.
Paul Pfaffinger :
Mechanisms involved in regulating neuronal excitability and synaptic
strength.
Mark Perin :
Molecular events in neurotransmitter release from presynaptic terminals.
------------------------------
Subject: Job Offer: Research on Financial Analysis in Santa Fe NM
From: n <n@santafe.edu:n@predict.com (Norman Packard)>
Date: Wed, 20 Jan 93 15:25:28 -0700
Job Opening for Research Scientist
Prediction Company
Financial Forecasting
Prediction Company is a small Santa Fe, NM based startup firm utilizing
the latest nonlinear forecasting technologies for prediction and
computerized trading of derivative financial instruments. The senior
technical founders of the firm are Doyne Farmer and Norman Packard, who
have worked for over ten years in the fields of chaos theory and
nonlinear dynamics. The technical staff includes other senior researchers
in the field. The company has the backing of a major technically based
trading firm and their partner, a major European bank.
There is currently an opening at the company for a research scientist
to assist in modeling and related data analysis research.
The successful applicant will be a talented scientist with experience
in one or more of the following areas: (i) learning algorithms, such
as neural networks, decision trees, and genetic algorithms, (ii) time
series forecasting, (iii) statistics.
Experience in applying learning algorithms to real data and a strong
computer programming background, preferably in C++, are essential. A
sound background in statistics and experience with financial
applications are desirable.
Applicants should send resumes to Prediction Company, 234 Griffin Street,
Santa Fe, NM 87501 or to Laura Barela at laura%predict.com@santafe.edu.
------------------------------
Subject: Computational Biology Faculty Position
From: George Berg <berg@cs.albany.edu>
Date: Fri, 22 Jan 93 17:55:01 -0500
FACULTY POSITION
COMPUTATIONAL BIOLOGIST
THE UNIVERSITY AT ALBANY - SUNY
The Departments of Biological Sciences and Biomedical Sciences invite
applicants for a joint, tenure-track position at the rank of assistant
professor. Candidates should have an active research program (with
promise of extramural funding) that involves the development and use of
computational techniques, preferably in the fields of macromolecular
structure or sequence analysis, prediction and/or modeling. The
successful candidate will have the opportunity to interact with a large
number of biophysicists and molecular biologists at the University at
Albany and will have access to excellent computing and other core
research facilities. The candidate will develop a course in
computational approaches in structural biology, to be offered to upper
level undergraduate students from various science and
mathematics/computer science departments, as well as to graduate students
from the host departments. The expected start date is September 1, 1993.
Applicants should send a letter of intent, curriculum vitae, statement of
research plans, and arrange for at least three reference letters to be
sent by February 15, 1993 to:
David Shub, Ph.D.
Chair, Search Committee
Department of Biological Sciences
University at Albany y SUNY
1400 Washington Avenue
Albany NY 12222
The University at Albany, SUNY, is an Equal Opportunity, Affirmative Action
employer.
Applications from women, minority, handicapped persons and special disabled or
Vietnam era veterans are especially welcome.
era veterans are especially welcome.
< Please do not enquire by replying directly to this email. Rather address
enquiries to Dr. Shub. Thank you. >
------------------------------
End of Neuron Digest [Volume 11 Issue 8]
****************************************
