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Dear Danko,<br>
<br>
Much of your views are consistent to our DN model, an overarching
model for developing brains.<br>
<br>
> The system always adjusts--to everything(!)<br>
<br>
Yes, since the system does not know which is new and which is old.
However, the amount of adjustment is different. <br>
There is also a novelty system imbedded into the basic brain
circuits, realized by neurotransmitters such as ACh and NE.<br>
<br>
> The only simple input-output mappings that take place are the
sensory-motor loops that execute the actual behavior.<br>
<br>
Sorry, I do not quite agree. All sensory-motor loops that execute
the actual behavior are not simple input-output mappings.<br>
They affect all related brain representations, including perception,
cognition and motivation, as the DN system implies.<br>
<br>
> 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>
The brain does not seem to have an if-then-else circuit like your
above statement seems to suggest. Regardless new or old, <br>
the brain uses basically the same set of mechanisms. Only the
outcome is always different.<br>
<br>
> Importantly, practopoietic theory is not formulated in terms of
neurons (inhibition, excitation, connections, changes of synaptic
weights, etc.). <br>
<br>
Then, does it fall into the trap of symbolic representations? How
does the theory explain the development of various types of
invariance?<br>
DN suggests that various type of invariance arise from experience,
not in the human genes. Thus, convolution networks (including<br>
the Creceptron that my co-authors and I used before) for locational
invariance are GROSSLY wrong for the brain. <br>
<br>
-John<br>
<br>
<div class="moz-cite-prefix">On 4/14/14 6:51 AM, Danko Nikolic
wrote:<br>
</div>
<blockquote cite="mid:534BBDC2.4090406@gmail.com" type="cite">
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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 moz-do-not-send="true" 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
face="Arial" size="5">Dear
John,</font>
<div><font face="Arial"
size="5"><br>
</font></div>
<div><font face="Arial"
size="5">Thanks for your
questions. I reply
below.</font></div>
<div> <font face="Arial"
size="5"><br>
</font>
<div>
<div>
<div><font
face="Arial"
size="5">On Apr 5,
2014, at 10:51 AM,
Juyang Weng wrote:</font></div>
<font face="Arial"
size="5"><br>
</font>
<blockquote
type="cite">
<div
bgcolor="#FFFFFF"
text="#000000"><font
face="Arial"
size="5"> 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
face="Arial"
size="5"><br>
</font></div>
</div>
<font face="Arial"
size="5">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 face="Arial"
size="5"><br>
</font></div>
<div><font face="Arial"
size="5">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 face="Arial"
size="5"><br>
</font></div>
<div><font face="Arial"
size="5">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 face="Arial"
size="5"><br>
</font></div>
<div><font face="Arial"
size="5">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 face="Arial"
size="5"><br>
</font></div>
<div><font face="Arial"
size="5">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 face="Arial"
size="5"><br>
</font>
<blockquote
type="cite">
<div
bgcolor="#FFFFFF"
text="#000000"><font
face="Arial"
size="5"> The
currently active
neuron is the
one being
examined by the
top down process<br>
</font></div>
</blockquote>
<div><font
face="Arial"
size="5"><br>
</font></div>
</div>
<font face="Arial"
size="5">I'm not sure
what you mean by
"being examined", but
perhaps my comment
above may deal with
it.</font></div>
<div><font face="Arial"
size="5"><br>
</font></div>
<div><font face="Arial"
size="5">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 face="Arial"
size="5"><br>
</font></div>
<div><font face="Arial"
size="5">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 face="Arial"
size="5"><br>
</font></div>
<div><font face="Arial"
size="5">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 face="Arial"
size="5"><br>
</font></div>
<div><font face="Arial"
size="5">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 face="Arial"
size="5"><br>
</font></div>
<div><font face="Arial"
size="5">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 face="Arial"
size="5"><br>
</font></div>
<div><font face="Arial"
size="5">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
face="Arial"
size="5"><br>
</font></div>
<blockquote
type="cite">
<div
bgcolor="#FFFFFF"
text="#000000"><font
face="Arial"
size="5"> 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
face="Arial"
size="5"><br>
</font></div>
</div>
<font face="Arial"
size="5">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 face="Arial"
size="5"><br>
</font></div>
<div><font face="Arial"
size="5">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 face="Arial"
size="5"><br>
</font></div>
<div><font face="Arial"
size="5">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 face="Arial"
size="5"><br>
</font></div>
<div><font face="Arial"
size="5">I hope that
the above replies
provide some relevant
information, as well
as pointers for
finding more.</font></div>
<div><font face="Arial"
size="5"><br>
</font></div>
<div><font face="Arial"
size="5">Best,</font></div>
<div><font face="Arial"
size="5"><br>
</font></div>
<div><font face="Arial"
size="5">Steve</font></div>
<div><font face="Arial"
size="5"><br>
</font></div>
<div><font face="Arial"
size="5"><br>
</font></div>
<div><font face="Arial"
size="5"><br>
</font></div>
<div>
<blockquote type="cite">
<div bgcolor="#FFFFFF"
text="#000000">
<div> <font
face="Arial"
size="5"><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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</span></font></pre>
</div>
</div>
</blockquote>
</div>
<div><font face="Arial"
size="5"><br>
</font>
<div> <font
face="Arial"
size="5"><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">
<div
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">
<div
style="word-wrap:break-word">
<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>
</div>
</div>
</blockquote>
</div>
<br>
</div>
</blockquote>
<br>
</div>
</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 moz-do-not-send="true" 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 moz-do-not-send="true" class="moz-txt-link-abbreviated" href="mailto:danko.nikolic@gmail.com">danko.nikolic@gmail.com</a>
----------------------------</pre>
</blockquote>
<br>
<pre class="moz-signature" 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: 517-353-4388
Fax: 517-432-1061
Email: <a class="moz-txt-link-abbreviated" href="mailto:weng@cse.msu.edu">weng@cse.msu.edu</a>
URL: <a class="moz-txt-link-freetext" href="http://www.cse.msu.edu/~weng/">http://www.cse.msu.edu/~weng/</a>
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