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Neuron Digest   Saturday, 25 Jan 1997                Volume 97 : Issue 4

Today's Topics:
              Prof. Zadeh's article of 1965 on "fuzzy sets"
                    PhD/Masters Research Assistantship
                 Job Openings in Computer Vision Research
                  Job Openings in Intelligent Agent R&D
                            NIPS*96 preprints
                 Papers: Canonical momenta indicators ...
              Network: CNS, Table of Contents, Vol. 8, 1,97
      Technical Report: Visual recognition and robust Kalman filters


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----------------------------------------------------------------------

Subject: Prof. Zadeh's article of 1965 on "fuzzy sets"
From:    Andre Gheventer <ghevente@ism.com.br>
Date:    Sat, 25 Jan 1997 19:07:23 -0200

We are preparing a document on fuzzy logic and need "badly"this text. Anyone
who has access to the article please mail to me. Thank you. 
Note:
are there documents on fuzzy logic on line ? 


------------------------------

Subject: PhD/Masters Research Assistantship
From:    Wray Buntine <wray@Ultimode.com>
Date:    Sat, 25 Jan 1997 10:50:52 -0800


PhD/Masters Research Assistantships

Field:  probabilistic algorithms, data analysis/mining and 
        optimization for CAD
Place:  Electrical Engineering and Computer Science
        University of California, Berkeley

The CAD group in the EECS Dept. at UC Berkeley is offering research support
for its Masters and Doctoral program.  Research areas include but are not
limited to the use of data mining/analysis/engineering techniques in CAD or
optimization, and probabilistic methods for optimization or specialized
compilation.

The Electronic Design Technology (EDT) field is concerned with computer
automated or computer-assisted design of complex electronic systems.  With
current hardware capabilities advancing rapidly, a key bottleneck is the
development of advanced algorithms for optimization and simulation of
partial, abstract or completed designs.  Our task is to design, code and
experiment with new algorithms, methodologies, and software technologies for
alleviating this bottleneck.  The task can include the use of data
mining/analysis to understand the nature of the optimization task, or in
order to develop adaptive optimization methods.

The ideal candidate should have a background in computer science, electrical
engineering or related disciplines, should be an accomplished or developing
programmer, and should have an interest in the theory and mathematical
techniques used in optimization, data analysis, or probabilistic methods.
Candidates who wish to apply are invited to respond with a copy of their CV
to:

Professor R. Newton     URL:  http://www.eecs.berkeley.edu/~newton
Dr. Wray Buntine        URL:  http://www.eecs.berkeley.edu/~wray
Dr. Andrew Mayer        URL:  http://www.eecs.berkeley.edu/~mayer

Dept. of Electrical Engineering and Computer Sciences
520 Cory Hall
University of California at Berkeley
Berkeley, CA, 94720

The CAD Group           URL:  http://www-cad.eecs.berkeley.edu
EECS, UC Berkeley       URL:  http://www.eecs.berkeley.edu

------------------------------

Subject: Job Openings in Computer Vision Research
From:    Joseph Atick <atick@monaco.rockefeller.edu>
Date:    Tue, 21 Jan 1997 14:50:31 -0500

FYI

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
                        JOB OPENINGS
                              IN
                    Pattern Recognition Research



Join a growing team of scientists and software engineers developing
real world applications of visual pattern recognition technology
(e.g. face recognition systems).  The openings are at various
levels and will be at  Visionics' research facility in New Jersey (about 20
minutes outside New York City).

The candidate is expected to have experience in pattern recognition
research, numerical analysis, C/C++ programming. Strong computer
programming abilities are a must. A research track record in computer
vision, artificial neural network,  image processing, or scene understanding
is a definite plus.

If you are interested in an exciting job opportunity and would like
a chance for rapid career development and significant financial rewards,
please fax your resume to (908) 549 5323, Re: Job Posting, for consideration.
Alternatively, you can email it to jobs@faceit.com. Additional information
can be found at http://www.faceit.com. Visionics is an equal opportunity
employer. Minority and women candidates are encouraged to apply.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

- -- 
Joseph J. Atick
Rockefeller University
1230 York Avenue
New York, NY 10021

Tel: 212 327 7421
Fax: 212 327 7422

------------------------------

Subject: Job Openings in Intelligent Agent R&D
From:    Stephen Lane <lane@katrix.com>
Date:    Fri, 24 Jan 1997 14:48:39 -0500


                               JOB OPENINGS IN

                             INTELLIGENT AGENT
                      RESEARCH AND DEVELOPMENT 


OVERVIEW

Katrix Inc. has developed technology that enables intelligent agents to be
embodied as fully articulated three-dimensional human and animal-like
interactive characters in computer games, virtual reality simulations and
distributed interactive network applications. Intelligent agents created
with Katrix Technology think, learn and act, and as a result, can adapt
their behavior and movement in real-time based upon interactions with
human users and the 3D virtual environment. Katrix currently is developing
a suite of products that support the creation and control of such
intelligent agents for use as fully interactive Internet Avatars, Digital
Actors and Virtual Creatures.  These products include point and click
behavioral animation authoring tools, libraries of off-the-shelf
interactive characters and intelligent behaviors, as well as a totally new
single-hand computer input device particularly well suited for virtual
reality and gaming applications.


STAFF POSITIONS AVAILABLE

The positions available involve core technology development in the areas
of intelligent control, robotics, behavioral animation, neural networks,
knowledge-based systems, distributed interactive simulation and visual
programming language design. Prospective candidates should have a strong
math background and be self-motivated. Required programming skills include
proficiency in C and C++ on PC and/or Unix platforms. A research track
record in controls and dynamics, robotics or neural networks is a definite
plus.  Familiarity with design and implementation of graphical user
interfaces, 3D computer animation, interactive simulation or 3D games also
is desirable.  Staff positions are available at various levels at Katrix
facility located in Princeton, New Jersey (about 60 minutes from both New
York City and Philadelphia).


CONTACT

If you are interested in an exciting career opportunity with a rapidly growing
company in an industry poised for explosive growth, please send your resume
immediately to:

        Stephen H. Lane, President
        FAX: (609) 921-7547 
        Email: lane@katrix.com

        Katrix Inc.
        31 Airpark Road
        Princeton, NJ 08540
        (609) 921-7544
       


------------------------------

Subject: NIPS*96 preprints
From:    michael@salk.edu
Date:    Wed, 22 Jan 1997 18:19:02 -0800

Connectionists - 

This is an announcement of several NIPS*96 preprints from the
Computational Neurobiology Lab at the Salk Institute in San Diego.
These will appear in "Advances in Neural Information Processing
Systems 9" (available May 1997), edited by Mozer, M.C., Jordan, M.I.,
and Petsche, T., and published by MIT Press of Cambridge, MA.  We
enclose the abstracts and ftp addresses of these papers.  Full
citations are at the bottom of each abstract.  Comments and feedback
are welcome.

- - Marni Stewart Bartlett, Tony Bell, Michael Gray, Mike Lewicki, 
        Terry Sejnowski, Magnus Stensmo, Akaysha Tang

**************************************************************

VIEWPOINT INVARIANT FACE RECOGNITION USING INDEPENDENT COMPONENT
             ANALYSIS AND ATTRACTOR NETWORKS
        Bartlett, M. Stewart & Sejnowski, T.J.

EDGES ARE THE `INDEPENDENT COMPONENTS' OF NATURAL SCENES
        Bell A.J. & Sejnowski T.J.

DYNAMIC FEATURES FOR VISUAL SPEECHREADING: A SYSTEMATIC COMPARISON 
        Gray, M.S., Movellan, J.R., & Sejnowski, T.J.

SELECTIVE INTEGRATION: A MODEL FOR DISPARITY ESTIMATION
        Gray, M.S., Pouget, A., Zemel, R., Nowlan, S., & Sejnowski, T.J. 

BLIND SEPARATION OF DELAYED AND CONVOLVED SOURCES
        Lee T-W., Bell A.J. & Lambert R.

BAYESIAN UNSUPERVISED LEARNING OF HIGHER ORDER STRUCTURE
        Lewicki, M.S. & Sejnowski, T.J.

LEARNING DECISION THEORETIC UTILITIES THROUGH REINFORCEMENT LEARNING 
        Stensmo, M. & Sejnowski, T.J.

CHOLINERGIC MODULATION PRESERVES SPIKE TIMING UNDER PHYSIOLOGICALLY
                REALISTIC FLUCTUATING INPUT
        Tang, A.C., Bartels, A.M., & Sejnowski, T.J.

**************************************************************

VIEWPOINT INVARIANT FACE RECOGNITION USING INDEPENDENT COMPONENT
                ANALYSIS AND ATTRACTOR NETWORKS

            Bartlett, M. Stewart & Sejnowski, T.J.

We have explored two approaches to recognizing faces across changes in
pose.  First, we developed a representation of face images based on
independent component analysis (ICA) and compared it to a principal
component analysis (PCA) representation for face recognition.  The ICA
basis vectors for this data set were more spatially local than the PCA
basis vectors and the ICA representation had greater invariance to
changes in pose.  Second, we present a model for the development of
viewpoint invariant responses to faces from visual experience in a
biological system.  The temporal continuity of natural visual
experience was incorporated into an attractor network model by Hebbian
learning following a lowpass temporal filter on unit activities.  When
combined with the temporal filter, a basic Hebbian update rule became
a generalization of Griniasty et al. (1993), which associates
temporally proximal input patterns into basins of attraction.  The
system acquired representations of faces that were largely independent
of pose.

ftp://ftp.cnl.salk.edu/pub/marni/nips96_bartlett.ps
http://www.cnl.salk.edu/~marni/publications.html

Bartlett, M. Stewart & Sejnowski, T. J. (in press).  Viewpoint
invariant face recognition using independent component analysis and
attractor networks.  In Mozer, M.C., Jordan, M.I., Petsche, T.(Eds.),
Advances in Neural Information Processing Systems 9.  MIT Press,
Cambridge, MA, U.S.A.

**************************************************************

EDGES ARE THE `INDEPENDENT COMPONENTS' OF NATURAL SCENES

              Bell A.J. & Sejnowski T.J.

Field (1994) has suggested that neurons with line and edge
selectivities found in primary visual cortex of cats and monkeys form
a sparse, distributed representation of natural scenes, and Barlow
(1989) has reasoned that such responses should emerge from an
unsupervised learning algorithm that attempts to find a factorial code
of independent visual features. We show here that non-linear
`infomax', when applied to an ensemble of natural scenes, produces
sets of visual filters that are localised and oriented.  Some of these
filters are Gabor-like and resemble those produced by the
sparseness-maximisation network of Olshausen \& Field (1996).  In
addition, the outputs of these filters are as independent as possible,
since the infomax network is able to perform Independent Components
Analysis (ICA).  We compare the resulting ICA filters and their
associated basis functions, with other decorrelating filters produced
by Principal Components Analysis (PCA) and zero-phase whitening
filters (ZCA).  The ICA filters have more sparsely distributed
(kurtotic) outputs on natural scenes.  They also resemble the
receptive fields of simple cells in visual cortex, which suggests that
these neurons form an information-theoretic co-ordinate system for
images.

ftp://ftp.cnl.salk.edu/pub/tony/edge.ps.Z

Bell A.J. & Sejnowski T.J. (In press). Edges are the `Independent
Components' of Natural Scenes. In Mozer, M.C., Jordan, M.I., Petsche,
T. (Eds.), Advances in Neural Information Processing Systems 9.  MIT
Press, Cambridge, MA, U.S.A.

**************************************************************

DYNAMIC FEATURES FOR VISUAL SPEECHREADING: A SYSTEMATIC COMPARISON 

       Gray, M. S., Movellan, J. R., & Sejnowski, T. J.

Humans use visual as well as auditory speech signals to recognize
spoken words.  A variety of systems have been investigated for
performing this task.  The main purpose of this research was to
systematically compare the performance of a range of dynamic visual
features on a speechreading task.  We have found that normalization of
images to eliminate variation due to translation, scale, and planar
rotation yielded substantial improvements in generalization
performance regardless of the visual representation used.  In
addition, the dynamic information in the difference between successive
frames yielded better performance than optical-flow based approaches,
and compression by local low-pass filtering worked surprisingly better
than global principal components analysis (PCA).  These results are
examined and possible explanations are explored.

ftp://ftp.cnl.salk.edu/pub/michael/nips_lips.ps
ftp://ftp.cnl.salk.edu/pub/michael/nips_lips-abs.text

Gray, M. S., Movellan, J. R., & Sejnowski, T. J. (In press).  Dynamic
features for visual speechreading: A systematic comparison.  In Mozer,
M.C., Jordan, M.I., Petsche, T. (Eds.), Advances in Neural Information
Processing Systems 9.  MIT Press, Cambridge, MA, U.S.A.

**************************************************************

SELECTIVE INTEGRATION: A MODEL FOR DISPARITY ESTIMATION

        Gray, M. S., Pouget, A., Zemel, R., 
           Nowlan, S., & Sejnowski, T. J. 

Local disparity information is often sparse and noisy, which creates
two conflicting demands when estimating disparity in an image region:
the need to spatially average to get an accurate estimate, and the
problem of not averaging over discontinuities.  We have developed a
network model of disparity estimation based on disparity-selective
neurons, such as those found in the early stages of processing in
visual cortex.  The model can accurately estimate multiple disparities
in a region, which may be caused by transparency or occlusion, in real
images and random-dot stereograms.  The use of a selection mechanism
to selectively integrate reliable local disparity estimates results in
superior performance compared to standard back-propagation and
cross-correlation approaches.  In addition, the representations
learned with this selection mechanism are consistent with recent
neurophysiological results of von der Heydt, Zhou, Friedman, and
Poggio (1995) for cells in cortical visual area V2.  Combining
multi-scale biologically-plausible image processing with the power of
the mixture-of-experts learning algorithm represents a promising
approach that yields both high performance and new insights into
visual system function.

ftp://ftp.cnl.salk.edu/pub/michael/nips_stereo.ps
ftp://ftp.cnl.salk.edu/pub/michael/nips_stereo-abs.text

Gray, M. S., Pouget, A., Zemel, R., Nowlan, S., & Sejnowski, T. J. (In
press).  Selective Integration: A Model for Disparity Estimation.  In
Mozer, M.C., Jordan, M.I., Petsche, T. (Eds.), Advances in Neural
Information Processing Systems 9.  MIT Press, Cambridge, MA, U.S.A.

**************************************************************

BLIND SEPARATION OF DELAYED AND CONVOLVED SOURCES
        
        Lee T-W., Bell A.J. & Lambert R.

We address the difficult problem of separating multiple speakers with
multiple microphones in a real room. We combine the work of Torkkola
and Amari, Cichocki and Yang, to give Natural Gradient information
maximisation rules for recurrent (IIR) networks, blindly adjusting
delays, separating and deconvolving mixed signals. While they work
well on simulated data, these rules fail in real rooms which usually
involve non-minimum phase transfer functions, not-invertible using
stable IIR filters. An approach that sidesteps this problem is to
perform infomax on a feedforward architecture in the frequency domain
(Lambert 1996). We demonstrate real-room separation of two natural
signals using this approach.

ftp://ftp.cnl.salk.edu/pub/tony/twfinal.ps.Z

Lee T-W., Bell A.J. & Lambert R. (In press). Blind separation of
delayed and convolved sources. In Mozer, M.C., Jordan, M.I., Petsche,
T. (Eds.), Advances in Neural Information Processing Systems 9.  MIT
Press, Cambridge, MA, U.S.A.

**************************************************************

BAYESIAN UNSUPERVISED LEARNING OF HIGHER ORDER STRUCTURE

            Lewicki, M. S. & Sejnowski, T. J.

Multilayer architectures such as those used in Bayesian belief
networks and Helmholtz machines provide a powerful framework for
representing and learning higher order statistical relations among
inputs.  Because exact probability calculations with these models are
often intractable, there is much interest in finding approximate
algorithms. We present an algorithm that efficiently discovers higher
order structure using EM and Gibbs sampling. The model can be
interpreted as a stochastic recurrent network in which ambiguity in
lower-level states is resolved through feedback from higher levels. We
demonstrate the performance of the algorithm on benchmark problems.

ftp://ftp.cnl.salk.edu/pub/lewicki/nips96.ps.Z
ftp://ftp.cnl.salk.edu/pub/lewicki/nips96-abs.text

Lewicki, M.S. and Sejnowski, T.J. (In press). Bayesian unsupervised
learning of higher order structure.  In Mozer, M.C., Jordan, M.I., and
Petsche, T. (Eds.), Advances in Neural and Information Processing
Systems 9.  MIT Press, Cambridge, MA, U.S.A.

**************************************************************

LEARNING DECISION THEORETIC UTILITIES THROUGH REINFORCEMENT LEARNING 

                Stensmo, M. & Sejnowski, T. J.

Probability models can be used to predict outcomes and compensate for
missing data, but even a perfect model cannot be used to make
decisions unless the values of the outcomes, or preferences between
them, are also provided. This arises in many real-world problems, such
as medical diagnosis, where the cost of the test as well as the
expected improvement in the outcome must be considered. Relatively
little work has been done on learning the utilities of outcomes for
optimal decision making. In this paper, we show how
temporal-difference (TD($\lambda$)) reinforcement learning can be used
to determine decision theoretic utilities within the context of a
mixture model and apply this new approach to a problem in medical
diagnosis. TD($\lambda$) learning reduces the number of tests that
have to be done to achieve the same level of performance with the
probability model alone, which result in significant cost savings and
increased efficiency.

http://www.cs.berkeley.edu/~magnus/papers/nips96.ps.Z

Stensmo, M. and Sejnowski, T. J. (in press). Learning decision
theoretic utilities through reinforcement learning. In: Mozer, M.C.,
Jordan, M.I. and Petsche, T., (Eds.), Advances in Neural Information
Processing Systems, Vol. 9. MIT Press, Cambridge, MA, U.S.A.

**************************************************************

CHOLINERGIC MODULATION PRESERVES SPIKE TIMING UNDER PHYSIOLOGICALLY
                REALISTIC FLUCTUATING INPUT

        Tang, A. C., Bartels, A. M., & Sejnowski, T. J.

Neuromodulation can change not only the mean firing rate of a neuron,
but also its pattern of firing.  Therefore, a reliable neural coding
scheme, whether a rate coding or a spike time based coding, must be
robust in a dynamic neuromodulatory environment.  The common
observation that cholinergic modulation leads to a reduction in spike
frequency adaptation implies a modification of spike timing, which
would make a neural code based on precise spike timing difficult to
maintain.  In this paper, the effects of cholinergic modulation were
studied to test the hypothesis that precise spike timing can serve as
a reliable neural code.  Using the whole cell patch-clamp technique in
rat neocortical slice preparation and compartmental modeling
techniques, we show that cholinergic modulation, surprisingly,
preserved spike timing in response to a fluctuating inputs that
resembles {\em in vivo} conditions.  This result suggests that in vivo
spike timing may be much more resistant to changes in neuromodulator
concentrations than previous physiological studies have implied.

ftp://ftp.cnl.salk.edu/pub/tang/ach_timing.ps.gz
ftp://ftp.cnl.salk.edu/pub/tang/ach_timing_abs.txt

Akaysha C. Tang, Andreas M. Bartels, and Terrence J Sejnowski. (In
press). Cholinergic Modulation Preserves Spike Timing Under
Physiologically Realistic Fluctuating Input.  In Mozer, M.C., Jordan,
M.I., Petsche, T. (Eds.), Advances in Neural Information Processing
Systems 9.  MIT Press, Cambridge, MA, U.S.A.

**************************************************************

------------------------------

Subject: Papers: Canonical momenta indicators ...
From:    Lester Ingber <ingber@ingber.com>
Date:    Thu, 23 Jan 1997 14:58:56 -0500

Below are URLs and abstracts for 4 papers utilizing canonical momenta
indicators (CMI), in analyses of neocortical EEG, financial markets,
combat simulation, and data mining/knowledge discovery.  Below these
are instructions for retrieval of files.

As noted by a Physical Review E referee for the EEG paper,
        ... the paper ... has potential value for a wide variety of
        systems, especially for very complex systems.
Its filename [and size] is smni97_cmi.ps.Z [170K]
          %A L. Ingber
          %T Statistical mechanics of neocortical interactions:
             Canonical momenta indicators of electroencephalography
          %J Physical Review E
          %P (to be published)
          %D 1997
          %O URL http://www.ingber.com/smni97_cmi.ps.Z

ABSTRACT:  A series of papers has developed a statistical mechanics of
neocortical interactions (SMNI), deriving aggregate behavior of
experimentally observed columns of neurons from statistical
electrical-chemical properties of synaptic interactions.  While not
useful to yield insights at the single neuron level, SMNI has
demonstrated its capability in describing large-scale properties of
short-term memory and electroencephalographic (EEG) systematics.  The
necessity of including nonlinear and stochastic structures in this
development has been stressed.  Sets of EEG and evoked potential data
were fit, collected to investigate genetic predispositions to
alcoholism and to extract brain "signatures" of short-term memory.
Adaptive Simulated Annealing (ASA), a global optimization algorithm,
was used to perform maximum likelihood fits of Lagrangians defined by
path integrals of multivariate conditional probabilities.  Canonical
momenta indicators (CMI) are thereby derived for individual's EEG
data.  The CMI give better signal recognition than the raw data, and
can be used to advantage as correlates of behavioral states.  These
results give strong quantitative support for an accurate intuitive
picture, portraying neocortical interactions as having common algebraic
or physics mechanisms that scale across quite disparate spatial scales
and functional or behavioral phenomena, i.e., describing interactions
among neurons, columns of neurons, and regional masses of neurons.

The markets file is the final version of a preprint posted in March '96.
   markets96_momenta.ps.Z [45K]
          %A L. Ingber
          %T Canonical momenta indicators of financial markets and
             neocortical EEG
          %B International Conference on Neural Information Processing
             (ICONIP'96)
          %I Springer
          %C New York
          %P 777-784
          %D 1996
          %O Invited paper to the 1996 International Conference on Neural
             Information Processing (ICONIP'96), Hong Kong, 24-27 September
             1996. URL http://www.ingber.com/markets96_momenta.ps.Z

ABSTRACT:  A paradigm of statistical mechanics of financial markets
(SMFM) is fit to multivariate financial markets using Adaptive
Simulated Annealing (ASA), a global optimization algorithm, to perform
maximum likelihood fits of Lagrangians defined by path integrals of
multivariate conditional probabilities.  Canonical momenta are thereby
derived and used as technical indicators in a recursive ASA
optimization process to tune trading rules.  These trading rules are
then used on out-of-sample data, to demonstrate that they can profit
from the SMFM model, to illustrate that these markets are likely not
efficient.  This methodology can be extended to other systems, e.g.,
electroencephalography.  This approach to complex systems emphasizes
the utility of blending an intuitive and powerful mathematical-physics
formalism to generate indicators which are used by AI-type rule-based
models of management.

   combat97_cmi.ps.Z [55K]
          %A M. Bowman
          %A L. Ingber
          %T Canonical momenta of nonlinear combat
          %B Proceedings of the 1997 Simulation Multi-Conference,
             6-10 April 1997, Atlanta, GA
          %I Society for Computer Simulation
          %C San Diego, CA
          %P (to be published)
          %D 1997
          %O URL http://www.ingber.com/combat97_cmi.ps.Z

ABSTRACT:  The context of nonlinear combat calls for more sophisticated
measures of effectiveness.  We present a set of tools that can be used
as such supplemental indicators, based on stochastic nonlinear
multivariate modeling used to benchmark Janus simulation to exercise
data from the U.S. Army National Training Center (NTC).  As a prototype
study, a strong global optimization tool, adaptive simulated annealing
(ASA), is used to explicitly fit Janus data, deriving coefficients of
relative measures of effectiveness, and developing a sound intuitive
graphical decision aid, canonical momentum indicators (CMI), faithful
to the sophisticated algebraic model.  We argue that these tools will
become increasingly important to aid simulation studies of the
importance of maneuver in combat in the 21st century.

   path97_datamining.ps.Z [90K]
          %A L. Ingber
          %T Data mining and knowledge discovery via
             statistical mechanics in nonlinear stochastic systems
          %P (submitted)
          %D 1997
          %O URL http://www.ingber.com/path97_datamining.ps.Z

ABSTRACT:  A modern calculus of multivariate nonlinear multiplicative
Gaussian-Markovian systems provides models of many complex systems
faithful to their nature, e.g., by not prematurely applying
quasi-linear approximations for the sole purpose of easing analysis.
To handle these complex algebraic constructs, sophisticated numerical
tools have been developed, e.g., methods of adaptive simulated
annealing (ASA) global optimization and of path integration (PATHINT).
In-depth application to three quite different complex systems have
yielded some insights into the benefits to be obtained by application
of these algorithms and tools, in statistical mechanical descriptions
of neocortex (electroencephalography), financial markets (interest-rate
and trading models), and combat analysis (baselining simulations to
exercise data).

The latest Adaptive Simulated Annealing (ASA) optimization code may be
retrieved at no charge from this archive in several formats:
   http://www.ingber.com/ASA-shar   [1350K]
   http://www.ingber.com/ASA-shar.Z [500K]
   http://www.ingber.com/ASA.tar.Z  [450K]
   http://www.ingber.com/ASA.tar.gz [320K]
   http://www.ingber.com/ASA.zip    [330K]

The archive can be accessed via WWW path
        http://www.ingber.com/
        http://www.alumni.caltech.edu/~ingber/
where the last address is a mirror homepage for the full archive.

Code and reprints can be retrieved via anonymous ftp from
ftp.ingber.com.  Interactively [brackets signify machine prompts]:
        [your_machine%] ftp ftp.ingber.com
        [Name (...):] anonymous
        [Password:] your_e-mail_address
        [ftp>] binary
        [ftp>] ls
        [ftp>] get file_of_interest
        [ftp>] quit

If you do not have ftp access, get information on the FTPmail service
by: mail ftpmail@ftpmail.ramona.vix.com (was ftpmail@decwrl.dec.com),
and send only the word "help" in the body of the message.

Limited help assisting people with queries on my codes and papers is
available only by electronic mail correspondence.  Sorry, I cannot mail
out hardcopies of code or papers.

 /*             RESEARCH                            ingber@ingber.com *
  *       INGBER                                 ftp://ftp.ingber.com *
  * LESTER                                     http://www.ingber.com/ *
  * Prof. Lester Ingber __ PO Box 857 __ McLean, VA 22101-0857 __ USA */


------------------------------

Subject: Network: CNS, Table of Contents, Vol. 8, 1,97
From:    Joseph Atick <atick@monaco.rockefeller.edu>
Date:    Fri, 24 Jan 1997 11:56:11 -0500

                Network: Computation in Neural Systems
                        Table of Contents
                        Volume 8, 1, 1997

As you may know, the journal has adopted incremental publishing in its online
edition, which means a paper is immediately published as soon as it is accepted
and processed. Every 3 months we finalize an issue for archival reasons
and issue a table of contents.

Online journal and information can be found at

                http://www.iop.org/Journals/ne

(limited access to non-subcribers, access to full length articles
to insitutional subscribers)

Table of contents of latest issue:
%%%%%%%%%%%%%
Editorial: Thank you to all our referees

TOPICAL REVIEW

R1
On the use of computation in modelling behaviour
F van der Velde

PAPERS

1
Nitric oxide: what can it compute?
B Krekelberg and J G Taylor

17
Analysis of ocular dominance pattern formation in a high-dimensional
self-organizing-map model
H-U Bauer, D Brockmann and T Geisel

35
Capacity and information efficiency of the associative net
B Graham and D Willshaw

55
A neural net model of the adaptation of binocular vertical eye alignment
J W McCandless and C M Schor

71
Quality and efficiency of retrieval for Willshaw-like autoassociative
networks: III. Willshaw--Potts model
A Kartashov, A Frolov, A Goltsev and R Folk

87
Stereo vision using a microcanonical mean field annealing neural network
Jeng-Sheng Huang and Hsiao-Chung Liu

104
Abstracts of Topical reviews published during 1996
Mutual information maximization: models of cortical self-organization
S. Becker

The development of topography in the visual cortex: a review of models
N. Swindale

Auditory cortical representation of complex acoustiv spectra as
inferred from the ripple analysis method
S. Shamma

Human colour perception and its adaptation
M. Webster
%%%%%%%%%%
                    Coming up in the May issue

(1) Metric-space analysis of spike trains: theory, algorithm and application

Jonathon Victor, & Keith Purpura

(2) A neural model of the stroboscopic alternative motion

A Bartschl and J L van Hemmen


and much much more...

%%%%%%%%%%%%%%
Network:CNS would like to welcome its new editorial board:

Larry Abott, Brandeis
Peter Dayan, MIT
Peter Hancock, University of Stirling
David Heeger, Stanford University
Leo van Hemmen, University of Munich
Tony Movshon, NYU
Markus Meister, Harvard
Dan Ruderman, Salk Insitute
Jonathon Victor, Cornell Univeristy
David Willshaw, University of Edinburgh


As always, we are happy to hear your suggestions and receive
your submissions. We hope to continue to make Network:CNS an
indispensible research tool for the computational and neuroscience
community.

Best regards
joseph atick
Editor-in-chief


Joseph J. Atick
Rockefeller University
1230 York Avenue
New York, NY 10021

Tel: 212 327 7421
Fax: 212 327 7422

------------------------------

Subject: Technical Report: Visual recognition and robust Kalman filters
From:    Rajesh Rao <rao@cs.rochester.edu>
Date:    Sat, 25 Jan 1997 00:36:49 -0500

The following paper on appearance-based visual recognition and robust
Kalman filtering is now available for retrieval via ftp.

Comments and suggestions welcome (This message has been cross-posted -
my apologies to those who received it more than once).

Rajesh Rao                       Internet: rao@cs.rochester.edu
Dept. of Computer Science        VOX:  (716) 275-2527              
University of Rochester          FAX:  (716) 461-2018
Rochester  NY  14627-0226        WWW:  http://www.cs.rochester.edu/u/rao/

===========================================================================

     Robust Kalman Filters for Prediction, Recognition, and Learning

                           Rajesh P.N. Rao
                   Department of Computer Science
                      University of Rochester
                      Rochester, NY 14627-0226

                        Technical Report 645
                           December, 1996

  Using results from the field of robust statistics, we derive a class
  of Kalman filters that are robust to structured and unstructured
  noise in the input data stream. Each filter from this class
  maintains robust optimal estimates of the input process's hidden
  state by allowing the measurement covariance matrix to be a
  non-linear function of the prediction errors. This endows the filter
  with the ability to reject outliers in the input stream.
  Simultaneously, the filter also learns an internal model of input
  dynamics by adapting its measurement and state transition matrices
  using two additional Kalman filter-based adaptation rules.  We
  present experimental results demonstrating the efficacy of such
  filters in mediating appearance-based segmentation and recognition
  of objects and image sequences in the presence of varying degrees of
  occlusion, clutter, and noise.


Retrieval information:

FTP-host:       ftp.cs.rochester.edu
FTP-pathname:   /pub/u/rao/papers/robust.ps.Z
URL:            ftp://ftp.cs.rochester.edu/pub/u/rao/papers/robust.ps.Z

15 pages; 296K compressed, 1015K uncompressed
- -------------------------------------------------------------------------
Anonymous ftp instructions:

>ftp ftp.cs.rochester.edu
Connected to anon.cs.rochester.edu.
220 anon.cs.rochester.edu FTP server (Version wu-2.4(3)) ready.

Name: [type 'anonymous' here]
331 Guest login ok, send your complete e-mail address as password.

Password: [type your e-mail address here]

ftp> cd /pub/u/rao/papers/
ftp> get robust.ps
ftp> bye


------------------------------

End of Neuron Digest [Volume 97 Issue 4]
****************************************