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<div class="moz-cite-prefix">Dear all,<br>
<br>
It has been very interesting to follow the discussion on the
functioning of ART, stability-plasticity dilemma and the related
issues. In that context, I would like to point to an exciting
property of the practopoietic theory, which enables us to
understand what is needed for a general solution to the problems
similar to the stability-plasticity dilemma. <br>
<br>
The issue of stability-plasticity dilemma can be described as a
problem of deciding when a new category of a stimulus needs to be
created and the system has to be adjusted as opposed to deciding
to treat the stimulus as old and familiar and thus, not needing to
adjust. Practopoietic theory helps us understand how a general
solution can be implemented for deciding whether to use old types
of behavior or to come up with new ones. This is possible in a
so-called "T_3 system" in which a process called "anapoiesis"
takes place. When a system is organized in such a T_3 way, every
stimulus, old or new, is treated in the same fashion, i.e., as
new. The system always adjusts--to everything(!)--even to stimuli
that have been seen thousands of times. There is never a simple
direct categorization (or pattern recognition) in which a
mathematical mapping would take place from input vectors to output
vectors, as traditionally implemented in multi-layer neural
networks. <br>
<br>
Rather the system readjusts itself continuously to prepare for
interactions with the surrounding world. The only simple
input-output mappings that take place are the sensory-motor loops
that execute the actual behavior. The internal processes
corresponding to perception, recognition, categorization etc. are
implemented by the mechanisms of internal system adjustments
(based on anapoiesis). These mechanisms create new sensory-motor
loops, which are then most similar to the traditional mapping
operations. The difference between old and new stimuli (i.e.,
familiar and unfamiliar) is detectable in the behavior of the
system only because the system adjusts quicker to the older that
to the newer stimuli. <br>
<br>
The claimed advantage of such a T_3 practopoietic system is that
only such a system can become generally intelligent as a whole and
behave adaptively and consciously with understanding of what is
going on around; The system forms a general "adjustment machine"
that can become aware of its surroundings and can be capable of
interpreting the situation appropriately to decide on the next
action. Thus, the perceptual dilemma of stability vs. plasticity
is converted into a general understanding of the current situation
and the needs of the system. If the current goals of the system
requires treating a slightly novel stimulus as new, it will be
treated as "new". However, if a slight change in the stimulus
features does not make a difference for the current goals and the
situation, than the stimulus will be treated as "old".<br>
<br>
Importantly, practopoietic theory is not formulated in terms of
neurons (inhibition, excitation, connections, changes of synaptic
weights, etc.). Instead, the theory is formulated much more
elegantly--in terms of interactions between cybernetic control
mechanisms organized into a specific type of hierarchy (poietic
hierarchy). This abstract formulation is extremely helpful for two
reasons. First, it enables one to focus on the most important
functional aspects and thus, to understand much easier the
underlying principles of system operations. Second, it tells us
what is needed to create intelligent behavior using any type of
implementation, neuronal or non-neuronal.<br>
<br>
I hope this will be motivating enough to give practopoiesis a
read.<br>
<br>
With best regards,<br>
<br>
Danko<br>
<br>
<br>
<br>
Link:<br>
<a class="moz-txt-link-freetext" href="http://www.danko-nikolic.com/practopoiesis/">http://www.danko-nikolic.com/practopoiesis/</a><br>
<br>
<br>
<br>
On 4/11/14 2:42 AM, Tsvi Achler wrote:<br>
</div>
<blockquote
cite="mid:CANdH7hkv2agxn4sb2iu5U4knmosAwfyTKftR-9-Q5sOnoTYf+A@mail.gmail.com"
type="cite">
<div dir="ltr">I can't comment on most of this, but I am not sure
if all models of sparsity and sparse coding fall into the
connectionist realm either because some make statistical
assumptions.
<div>-Tsvi</div>
</div>
<div class="gmail_extra">
<br>
<br>
<div class="gmail_quote">On Tue, Apr 8, 2014 at 9:19 PM, Juyang
Weng <span dir="ltr"><<a moz-do-not-send="true"
href="mailto:weng@cse.msu.edu" target="_blank">weng@cse.msu.edu</a>></span>
wrote:<br>
<blockquote class="gmail_quote" style="margin:0 0 0
.8ex;border-left:1px #ccc solid;padding-left:1ex">
<div bgcolor="#FFFFFF" text="#000000"> Tavi:<br>
<br>
Let me explain a little more detail:<br>
<br>
There are two large categories of biological neurons,
excitatory and inhibitory. Both are developed through
mainly signal statistics, <br>
not specified primarily by the genomes. Not all people
agree with my this point, but please tolerate my this view
for now. <br>
I gave a more detailed discussion on this view in my NAI
book. <br>
<br>
The main effect of inhibitory connections is to reduce the
number of firing neurons (David Field called it sparse
coding), with the help of <br>
excitatory connections. This sparse coding is important
because those do not fire are long term memory of the area
at this point of time.<br>
My this view is different from David Field. He wrote that
sparse coding is for the current representations. I think
sparse coding is <br>
necessary for long-term memory. Not all people agree with
my this point, but please tolerate my this view for now.
<br>
<br>
However, this reduction requires very fast parallel
neuronal updates to avoid uncontrollable large-magnitude
oscillations. <br>
Even with the fast biological parallel neuronal updates,
we still see slow but small-magnitude oscillations such as
the <br>
well-known theta waves and alpha waves. My view is that
such slow but small-magnitude oscillations are side
effects of <br>
excitatory and inhibitory connections that form many
loops, not something really desirable for the brain
operation (sorry, <br>
Paul Werbos). Not all people agree with my this point,
but please tolerate my this view for now. <br>
<br>
Therefore, as far as I understand, all computer
simulations for spiking neurons are not showing major
brain functions<br>
because they have to deal with the slow oscillations that
are very different from the brain's, e.g., as Dr. Henry
Markram reported<br>
(40Hz?). <br>
<br>
The above discussion again shows the power and necessity
of an overarching brain theory like that in my NAI book.
<br>
Those who only simulate biological neurons using
superficial biological phenomena are not going to
demonstrate <br>
any major brain functions. They can talk about signal
statistics from their simulations, but signal statistics
are far from brain functions. <br>
<br>
-John
<div>
<div class="h5"><br>
<br>
<div>On 4/8/14 1:30 AM, Tsvi Achler wrote:<br>
</div>
<blockquote type="cite">
<div dir="ltr">Hi John,
<div>ART evaluates distance between the contending
representation and the current input through
vigilance. If they are too far apart, a poor
vigilance signal will be triggered.</div>
<div>The best resonance will be achieved when they
have the least amount of distance.</div>
<div>If in your model, K-nearest neighbors is used
without a neural equivalent, then your model is
not quite in the spirit of a connectionist
model.</div>
<div>For example, Bayesian networks do a great job
emulating brain behavior, modeling the
integration of priors. and has been invaluable
to model cognitive studies. However they assume
a statistical configuration of connections and
distributions which is not quite known how to
emulate with neurons. Thus pure Bayesian models
are also questionable in terms of connectionist
modeling. But some connectionist models can
emulate some statistical models for example see
section 2.4 in Thomas & McClelland's
chapter in Sun's 2008 book (<a
moz-do-not-send="true"
href="http://www.psyc.bbk.ac.uk/people/academic/thomas_m/TM_Cambridge_sub.pdf"
target="_blank">http://www.psyc.bbk.ac.uk/people/academic/thomas_m/TM_Cambridge_sub.pdf</a>).</div>
<div>I am not suggesting <span
style="font-size:13.333333969116211px;font-family:arial,sans-serif">Hodgkin-Huxley</span> level
detailed neuron models, however connectionist
models should have their connections explicitly
defined. </div>
<div>Sincerely,</div>
<div>-Tsvi</div>
<div><br>
</div>
</div>
<div class="gmail_extra"><br>
<br>
<div class="gmail_quote">On Mon, Apr 7, 2014 at
10:58 AM, Juyang Weng <span dir="ltr"><<a
moz-do-not-send="true"
href="mailto:weng@cse.msu.edu"
target="_blank">weng@cse.msu.edu</a>></span>
wrote:<br>
<blockquote class="gmail_quote" style="margin:0
0 0 .8ex;border-left:1px #ccc
solid;padding-left:1ex">
<div bgcolor="#FFFFFF" text="#000000"> Tsvi,<br>
<br>
Note that ART uses a vigilance value to pick
up the first "acceptable" match in its
sequential bottom-up and top-down search.<br>
I believe that was Steve meant when he
mentioned vigilance. <br>
<br>
Why do you think "ART as a neural way to
implement a K-nearest neighbor algorithm"?
<br>
If not all the neighbors have sequentially
participated,<br>
how can ART find the nearest neighbor, let
alone K-nearest neighbor?<br>
<br>
Our DN uses an explicit k-nearest mechanism
to find the k-nearest neighbors in every
network update, <br>
to avoid the problems of slow resonance in
existing models of spiking neuronal
networks. <br>
The explicit k-nearest mechanism itself is
not meant to be biologically plausible, <br>
but it gives a computational advantage for
software simulation of large networks <br>
at a speed slower than 1000 network updates
per second.<br>
<br>
I guess that more detailed molecular
simulations of individual neuronal spikes
(such as using the Hodgkin-Huxley model of<br>
a neuron, using the <a
moz-do-not-send="true"
href="http://www.neuron.yale.edu/neuron/"
target="_blank">NEURON software,</a> or
like <a moz-do-not-send="true"
href="http://bluebrain.epfl.ch/"
target="_blank">the Blue Brain project</a>
directed by respected Dr. Henry Markram) <br>
are very useful for showing some detailed
molecular, synaptic, and neuronal
properties.<br>
However, they miss necessary
brain-system-level mechanisms so much that
it is difficult for them <br>
to show major brain-scale functions <br>
(such as learning to recognize objects and
detection of natural objects directly from
natural cluttered scenes). <br>
<br>
According to my understanding, if one uses a
detailed neuronal model for each of a
variety of neuronal types and<br>
connects those simulated neurons of
different types according to a diagram of
Brodmann areas, <br>
his simulation is NOT going to lead to any
major brain function. <br>
He still needs brain-system-level knowledge
such as that taught in the BMI 871 course. <br>
<br>
-John <br>
<div>
<div> <br>
<div>On 4/7/14 8:07 AM, Tsvi Achler
wrote:<br>
</div>
<blockquote type="cite">
<div dir="ltr">
<div>Dear Steve, John</div>
I think such discussions are great
to spark interests in feedback
(output back to input) such models
which I feel should be given much
more attention.
<div>In this vein it may be better
to discuss more of the details
here than to suggest to read a
reference.</div>
<div><br>
</div>
<div>Basically I see ART as a neural
way to implement a K-nearest
neighbor algorithm. Clearly the
way ART overcomes the neural
hurdles is immense especially in
figuring out how to coordinate
neurons. However it is also
important to summarize such
methods in algorithmic terms
which I attempt to do here (and
please comment/correct).</div>
<div>Instar learning is used to find
the best weights for quick
feedforward recognition without
too much resonance (otherwise more
resonance will be needed).
Outstar learning is used to find
the expectation of the patterns.
The resonance mechanism evaluates
distances between the "neighbors"
evaluating how close differing
outputs are to the input pattern
(using the expectation). By
choosing one winner the network is
equivalent to a 1-nearest neighbor
model. If you open it up to more
winners (eg k winners) as you
suggest then it becomes a
k-nearest neighbor mechanism.</div>
<div><br>
</div>
<div>Clearly I focused here on the
main ART modules and did not
discuss other additions. But I
want to just focus on the main
idea at this point.</div>
<div>Sincerely,</div>
<div>-Tsvi</div>
</div>
<div class="gmail_extra"> <br>
<br>
<div class="gmail_quote">On Sun, Apr
6, 2014 at 1:30 PM, Stephen
Grossberg <span dir="ltr"><<a
moz-do-not-send="true"
href="mailto:steve@cns.bu.edu"
target="_blank">steve@cns.bu.edu</a>></span>
wrote:<br>
<blockquote class="gmail_quote"
style="margin:0 0 0
.8ex;border-left:1px #ccc
solid;padding-left:1ex">
<div
style="word-wrap:break-word"><font
size="5" face="Arial">Dear
John,</font>
<div><font size="5"
face="Arial"><br>
</font></div>
<div><font size="5"
face="Arial">Thanks for
your questions. I reply
below.</font></div>
<div> <font size="5"
face="Arial"><br>
</font>
<div>
<div>
<div><font size="5"
face="Arial">On Apr
5, 2014, at 10:51
AM, Juyang Weng
wrote:</font></div>
<font size="5"
face="Arial"><br>
</font>
<blockquote type="cite">
<div bgcolor="#FFFFFF"
text="#000000"><font
size="5"
face="Arial"> Dear
Steve,<br>
<br>
This is one of my
long-time
questions that I
did not have a
chance to ask you
when I met you
many times before.
<br>
But they may be
useful for some
people on this
list. <br>
Please accept my
apology of my
question implies
any false
impression that I
did not intend.<br>
<br>
(1) Your statement
below seems to
have confirmed my
understanding: <br>
Your top-down
process in ART in
the late 1990's is
basically for
finding an
acceptable match <br>
between the input
feature vector and
the stored feature
vectors
represented by
neurons (not meant
for the nearest
match). <br>
</font></div>
</blockquote>
<div><font size="5"
face="Arial"><br>
</font></div>
</div>
<font size="5"
face="Arial">ART has
developed a lot since
the 1990s. A
non-technical but fairly
comprehensive review
article was published in
2012 in <i>Neural
Networks</i> and can
be found at <a
moz-do-not-send="true"
href="http://cns.bu.edu/%7Esteve/ART.pdf" target="_blank">http://cns.bu.edu/~steve/ART.pdf</a>.</font></div>
<div><font size="5"
face="Arial"><br>
</font></div>
<div><font size="5"
face="Arial">I do not
think about the top-down
process in ART in quite
the way that you state
above. My reason for
this is summarized by
the acronym CLEARS for
the processes of
Consciousness, Learning,
Expectation, Attention,
Resonance, and
Synchrony. </font><span
style="font-family:Arial;font-size:x-large">All the CLEARS processes
come into this story,
and </span><span
style="font-family:Arial;font-size:x-large">ART
top-down mechanisms
contribute to all of
them. For me, the most
fundamental issues
concern how ART
dynamically
self-stabilizes the
memories that are
learned within the
model's bottom-up
adaptive filters and
top-down expectations. </span></div>
<div><font size="5"
face="Arial"><br>
</font></div>
<div><font size="5"
face="Arial">In
particular, during
learning, a big enough
mismatch can lead to
hypothesis testing and
search for a new, or
previously learned,
category that leads to
an acceptable match. The
criterion for what is
"big enough mismatch" or
"acceptable match" is
regulated by a vigilance
parameter that can
itself vary in a
state-dependent way.</font></div>
<div><font size="5"
face="Arial"><br>
</font></div>
<div><font size="5"
face="Arial">After
learning occurs, a
bottom-up input pattern
typically directly
selects the
best-matching category,
without any hypothesis
testing or search. And
even if there is a reset
due to a large initial
mismatch with a
previously active
category, a single reset
event may lead directly
to a matching category
that can directly
resonate with the data. </font></div>
<div><font size="5"
face="Arial"><br>
</font></div>
<div><font size="5"
face="Arial">I should
note that all of the
foundational predictions
of ART now have
substantial bodies of
psychological and
neurobiological data to
support them. See the
review article if you
would like to read about
them.</font></div>
<div>
<div><font size="5"
face="Arial"><br>
</font>
<blockquote type="cite">
<div bgcolor="#FFFFFF"
text="#000000"><font
size="5"
face="Arial"> The
currently active
neuron is the one
being examined by
the top down
process<br>
</font></div>
</blockquote>
<div><font size="5"
face="Arial"><br>
</font></div>
</div>
<font size="5"
face="Arial">I'm not
sure what you mean by
"being examined", but
perhaps my comment above
may deal with it.</font></div>
<div><font size="5"
face="Arial"><br>
</font></div>
<div><font size="5"
face="Arial">I should
comment, though, about
your use of the word
"currently active
neuron". I assume that
you mean at the category
level. </font></div>
<div><font size="5"
face="Arial"><br>
</font></div>
<div><font size="5"
face="Arial">In this
regard, there are two
ART's. The first aspect
of ART is as a cognitive
and neural theory whose
scope, which includes
perceptual, cognitive,
and adaptively timed
cognitive-emotional
dynamics, among other
processes, is
illustrated by the above
referenced 2012 review
article in <i>Neural
Networks</i>. In the
biological theory, there
is no general commitment
to just one "currently
active neuron". One
always considers the
neuronal population, or
populations, that
represent a learned
category. Sometimes, but
not always, a
winner-take-all category
is chosen. </font></div>
<div><font size="5"
face="Arial"><br>
</font></div>
<div><font size="5"
face="Arial">The 2012
review article
illustrates some of the
large data bases of
psychological and
neurobiological data
that have been explained
in a principled way,
quantitatively
simulated, and
successfully predicted
by ART over a period of
decades. ART-like
processing is, however,
certainly not the only
kind of computation that
may be needed to
understand how the brain
works. The paradigm
called Complementary
Computing that I
introduced awhile ago
makes precise the sense
in which ART may be just
one kind of dynamics
supported by advanced
brains. This is also
summarized in the review
article.<br>
</font>
<div><font size="5"
face="Arial"><br>
</font></div>
<div><font size="5"
face="Arial">The
second aspect of ART
is as a series of
algorithms that
mathematically
characterize key ART
design principles and
mechanisms in a
focused setting, and
provide algorithms for
large-scale
applications in
engineering and
technology. ARTMAP,
fuzzy ARTMAP, and
distributed ARTMAP are
among these, all of
them developed with
Gail Carpenter. Some
of these algorithms
use winner-take-all
categories to enable
the proof of
mathematical theorems
that characterize how
underlying design
principles work. In
contrast, the
distributed ARTMAP
family of algorithms,
developed by Gail
Carpenter and her
colleagues, allows for
distributed category
representations
without losing the
benefits of fast,
incremental,
self-stabilizing
learning and
prediction in response
to a large
non-stationary
databases that can
include many
unexpected events. </font></div>
<div><font size="5"
face="Arial"><br>
</font></div>
<div><font size="5"
face="Arial">See,
e.g., <a
moz-do-not-send="true"
href="http://techlab.bu.edu/members/gail/articles/115_dART_NN_1997_.pdf"
target="_blank">http://techlab.bu.edu/members/gail/articles/115_dART_NN_1997_.pdf</a>
and <a
moz-do-not-send="true"
href="http://techlab.bu.edu/members/gail/articles/155_Fusion2008_CarpenterRavindran.pdf"
target="_blank">http://techlab.bu.edu/members/gail/articles/155_Fusion2008_CarpenterRavindran.pdf</a>.</font></div>
<div><font size="5"
face="Arial"><br>
</font></div>
<div><font size="5"
face="Arial">I should
note that FAST
learning is a
technical concept: it
means that each
adaptive weight can
converge to its new
equilibrium value on
EACH learning trial.
That is why ART
algorithms can often
successfully carry out
one-trial incremental
learning of a data
base. This is not true
of many other
algorithms, such as
back propagation,
simulated annealing,
and the like, which
all experience
catastrophic
forgetting if they try
to do fast learning.
Almost all other
learning algorithms
need to be run using
slow learning, that
allows only a small
increment in the
values of adaptive
weights on each
learning trial, to
avoid massive memory
instabilities, and
work best in response
to stationary data.
Such algorithms often
fail to detect
important rare cases,
among other
limitations. ART can
provably learn in
either the fast or
slow mode in response
to non-stationary
data.</font></div>
<div>
<div><font size="5"
face="Arial"><br>
</font></div>
<blockquote type="cite">
<div bgcolor="#FFFFFF"
text="#000000"><font
size="5"
face="Arial"> in a
sequential
fashion: one
neuron after
another, until an
acceptable neuron
is found.<br>
<br>
(2) The input to
the ART in the
late 1990's is for
a single feature
vector as a
monolithic input.
<br>
By monolithic, I
mean that all
neurons take the
entire input
feature vector as
input. <br>
I raise this point
here because
neuron in ART in
the late 1990's
does not have an
explicit local
sensory receptive
field (SRF), <br>
i.e., are fully
connected from all
components of the
input vector. A
local SRF means
that each neuron
is only connected
to a small region
<br>
in an input image.
<br>
</font></div>
</blockquote>
<div><font size="5"
face="Arial"><br>
</font></div>
</div>
<font size="5"
face="Arial">Various ART
algorithms for
technology do use fully
connected networks. They
represent a
single-channel case,
which is often
sufficient in
applications and which
simplifies mathematical
proofs. However, the
single-channel case is,
as its name suggests,
not a necessary
constraint on ART
design. </font></div>
<div><font size="5"
face="Arial"><br>
</font></div>
<div><font size="5"
face="Arial">In
addition, many ART
biological models do not
restrict themselves to
the single-channel case,
and do have receptive
fields. These include
the LAMINART family of
models that predict
functional roles for
many identified cell
types in the laminar
circuits of cerebral
cortex. These models
illustrate how
variations of a shared
laminar circuit design
can carry out very
different intelligent
functions, such as 3D
vision (e.g., 3D
LAMINART), speech and
language (e.g.,
cARTWORD), and cognitive
information processing
(e.g., LIST PARSE). They
are all summarized in
the 2012 review article,
with the archival
articles themselves on
my web page <a
moz-do-not-send="true"
href="http://cns.bu.edu/%7Esteve" target="_blank">http://cns.bu.edu/~steve</a>. </font></div>
<div><font size="5"
face="Arial"><br>
</font></div>
<div><font size="5"
face="Arial">The
existence of these
laminar
variations-on-a-theme
provides an existence
proof for the exciting
goal of designing a
family of chips whose
specializations can
realize all aspects of
higher intelligence, and
which can be
consistently connected
because they all share a
similar underlying
design. Work on
achieving this goal can
productively occupy lots
of creative modelers and
technologists for many
years to come.</font></div>
<div><font size="5"
face="Arial"><br>
</font></div>
<div><font size="5"
face="Arial">I hope that
the above replies
provide some relevant
information, as well as
pointers for finding
more.</font></div>
<div><font size="5"
face="Arial"><br>
</font></div>
<div><font size="5"
face="Arial">Best,</font></div>
<div><font size="5"
face="Arial"><br>
</font></div>
<div><font size="5"
face="Arial">Steve</font></div>
<div><font size="5"
face="Arial"><br>
</font></div>
<div><font size="5"
face="Arial"><br>
</font></div>
<div><font size="5"
face="Arial"><br>
</font></div>
<div>
<blockquote type="cite">
<div bgcolor="#FFFFFF"
text="#000000">
<div> <font size="5"
face="Arial"><br>
My apology again
if my
understanding
above has errors
although I have
examined the above
two points
carefully <br>
through multiple
your papers.<br>
<br>
Best regards,<br>
<br>
-John<br>
<br>
</font></div>
<div>
<pre cols="72"><font face="Arial"><span style="font-size:18px">Juyang (John) Weng, Professor
Department of Computer Science and Engineering
MSU Cognitive Science Program and MSU Neuroscience Program
428 S Shaw Ln Rm 3115
Michigan State University
East Lansing, MI 48824 USA
Tel: <a moz-do-not-send="true" href="tel:517-353-4388" value="+15173534388" target="_blank">517-353-4388</a>
Fax: <a moz-do-not-send="true" href="tel:517-432-1061" value="+15174321061" target="_blank">517-432-1061</a>
Email: <a moz-do-not-send="true" href="mailto:weng@cse.msu.edu" target="_blank">weng@cse.msu.edu</a>
URL: <a moz-do-not-send="true" href="http://www.cse.msu.edu/%7Eweng/" target="_blank">http://www.cse.msu.edu/~weng/</a>
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<div><font size="5"
face="Arial"><br>
</font>
<div> <font size="5"
face="Arial"><span
style="line-height:normal;text-indent:0px;border-collapse:separate;letter-spacing:normal;text-align:-webkit-auto;font-variant:normal;text-transform:none;font-style:normal;white-space:normal;font-weight:normal;word-spacing:0px"><span
style="line-height:normal;text-indent:0px;border-collapse:separate;letter-spacing:normal;text-align:-webkit-auto;font-variant:normal;text-transform:none;font-style:normal;white-space:normal;font-weight:normal;word-spacing:0px">
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style="word-wrap:break-word">
<span
style="line-height:normal;text-indent:0px;border-collapse:separate;letter-spacing:normal;text-align:-webkit-auto;font-variant:normal;text-transform:none;font-style:normal;white-space:normal;font-weight:normal;word-spacing:0px">
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<div>
<div>
<div>
<div>Stephen
Grossberg</div>
<div>Wang
Professor of
Cognitive and
Neural Systems</div>
<div>Professor
of
Mathematics,
Psychology,
and Biomedical
Engineering</div>
<div>
<div>Director,
Center for
Adaptive
Systems <a
moz-do-not-send="true"
href="http://www.cns.bu.edu/about/cas.html" target="_blank">http://www.cns.bu.edu/about/cas.html</a></div>
</div>
<div><a
moz-do-not-send="true"
href="http://cns.bu.edu/%7Esteve" target="_blank">http://cns.bu.edu/~steve</a></div>
<div><a
moz-do-not-send="true"
href="mailto:steve@bu.edu" target="_blank">steve@bu.edu</a></div>
</div>
</div>
</div>
<div><br>
</div>
</div>
</span></div>
</span><br>
</span><br>
</font></div>
<br>
</div>
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</blockquote>
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<br>
</div>
</blockquote>
<br>
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</div>
<pre cols="72"><span><font color="#888888">--
--
Juyang (John) Weng, Professor
Department of Computer Science and Engineering
MSU Cognitive Science Program and MSU Neuroscience Program
428 S Shaw Ln Rm 3115
Michigan State University
East Lansing, MI 48824 USA
Tel: <a moz-do-not-send="true" href="tel:517-353-4388" value="+15173534388" target="_blank">517-353-4388</a></font></span><div>
Fax: <a moz-do-not-send="true" href="tel:517-432-1061" value="+15174321061" target="_blank">517-432-1061</a>
Email: <a moz-do-not-send="true" href="mailto:weng@cse.msu.edu" target="_blank">weng@cse.msu.edu</a>
URL: <a moz-do-not-send="true" href="http://www.cse.msu.edu/%7Eweng/" target="_blank">http://www.cse.msu.edu/~weng/</a>
----------------------------------------------
</div></pre>
</div>
</blockquote>
</div>
<br>
</div>
</blockquote>
<br>
<pre cols="72">--
--
Juyang (John) Weng, Professor
Department of Computer Science and Engineering
MSU Cognitive Science Program and MSU Neuroscience Program
428 S Shaw Ln Rm 3115
Michigan State University
East Lansing, MI 48824 USA
Tel: <a moz-do-not-send="true" href="tel:517-353-4388" value="+15173534388" target="_blank">517-353-4388</a>
Fax: <a moz-do-not-send="true" href="tel:517-432-1061" value="+15174321061" target="_blank">517-432-1061</a>
Email: <a moz-do-not-send="true" href="mailto:weng@cse.msu.edu" target="_blank">weng@cse.msu.edu</a>
URL: <a moz-do-not-send="true" href="http://www.cse.msu.edu/%7Eweng/" target="_blank">http://www.cse.msu.edu/~weng/</a>
----------------------------------------------
</pre>
</div>
</div>
</div>
</blockquote>
</div>
<br>
</div>
</blockquote>
<br>
<br>
<pre class="moz-signature" cols="72">--
Prof. Dr. Danko Nikolić
Web:
<a class="moz-txt-link-freetext" href="http://www.danko-nikolic.com">http://www.danko-nikolic.com</a>
Mail address 1:
Department of Neurophysiology
Max Planck Institut for Brain Research
Deutschordenstr. 46
60528 Frankfurt am Main
GERMANY
Mail address 2:
Frankfurt Institute for Advanced Studies
Wolfgang Goethe University
Ruth-Moufang-Str. 1
60433 Frankfurt am Main
GERMANY
----------------------------
Office: (..49-69) 96769-736
Lab: (..49-69) 96769-209
Fax: (..49-69) 96769-327
<a class="moz-txt-link-abbreviated" href="mailto:danko.nikolic@gmail.com">danko.nikolic@gmail.com</a>
----------------------------</pre>
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