<html>
<head>
<meta content="text/html; charset=ISO-8859-1"
http-equiv="Content-Type">
</head>
<body bgcolor="#FFFFFF" text="#000000">
> Thus, long-term memories are not stored primarily in the
weights of neuron connections (as widely presumed) but in the rules
by which the system changes its network in response to sensory
inputs.<br>
<br>
I still wish that you can explain it in terms of neurons and their
connections, as I believe that any model of a brain should at least
<br>
be explained in the
<meta http-equiv="content-type" content="text/html;
charset=ISO-8859-1">
grainularity of neurons, their connections and neural transmitters.
<br>
<br>
-John<br>
<br>
<div class="moz-cite-prefix">On 4/14/14 2:10 PM, Danko Nikolic
wrote:<br>
</div>
<blockquote cite="mid:534C247B.6070809@gmail.com" type="cite">
<meta content="text/html; charset=ISO-8859-1"
http-equiv="Content-Type">
<div class="moz-cite-prefix">Dear Andras,<br>
<br>
I see why it may seem on the first look that practopoiesis is
somehow related to reinforcement learning. However, any closer
look to the theory will reveal that this is not the case.
Practopoiesis is related to just a little bit to reinforcement
learning, and it is as much related to any other brain theory or
algorithm. At the end, in fact, it is quite a novel and exciting
way of looking at brain function.<br>
<br>
Maybe the contribution of practopoiesis can be appreciated best
if described in the language of learning. Imagine a system that
does not have one or two general learning rules that are applied
to all of the units of the system. Instead, imagine that the
number of different learning rules equals the number of units in
the system; a billion neurons means billion different learning
rules, whereby each learning rule maximizes a different
function. <br>
<br>
Moreover, imagine that, in this system, all its long-term
memories (explicit and implicit) are stored in those learning
rules. Thus, long-term memories are not stored primarily in the
weights of neuron connections (as widely presumed) but in the
rules by which the system changes its network in response to
sensory inputs. Then, when we retrieve a piece of information,
or recognize a stimulus, or make a decision, we use these
learning mechanisms by quickly applying them to the network
(e.g., every second or even faster) as a function of the
incoming sensory inputs (or the sensory-motor contingencies). As
a result, the network continuously changes its architecture
with very high rate, and can quickly come back to its previous
architecture if it is presented with a sequences of stimuli that
has been presented already previously. One of the key point is
that this process of changing the network is how we think,
perceive, recall, categorize, etc.<br>
<br>
This process requires one more set of learning mechanisms that
lay behind those mentioned rules containing our long-term
memory. This latter set is responsible for acquiring our
long-term memories i.e., for determining for each unit which
learning rules it needs to use. Thus, there is a process of
learning how to learn.<br>
<br>
Practopoietic theory explain how this is possible, why it works,
how such systems can be described in generalized cybernetics
terms, and why this approach is sufficiently adaptive to produce
intelligence on par with that of humans. In practopoietic
theory, the learning rules that store our long-term memories are
referred to, not as "learning", but as "reconstruction of
knowledge" or in Greek "anapoiesis". The paper also reviews
behavioral evidence indicating that our cognition in fact is
anapoietic by its nature.<br>
<br>
I hope that this helps understand that practopoiesis is
something totally new and cannot simply be described with the
existing machine-leaning approaches and brain theories.<br>
<br>
With best regards,<br>
<br>
Danko<br>
<br>
<br>
On 4/14/14 3:05 PM, Andras Lorincz wrote:<br>
</div>
<blockquote
cite="mid:c90c18121a2a437a9a5201342cba0c5a@DB4PR06MB173.eurprd06.prod.outlook.com"
type="cite">
<meta http-equiv="Content-Type" content="text/html;
charset=ISO-8859-1">
<style type="text/css" style="display:none"><!--P{margin-top:0;margin-bottom:0;} .ms-cui-menu {background-color:#ffffff;border:1px rgb(171, 171, 171) solid;font-family:'Segoe UI WPC', 'Segoe UI', Tahoma, 'Microsoft Sans Serif', Verdana, sans-serif;font-size:11pt;color:rgb(51, 51, 51);} .ms-cui-menusection-title {display:none;} .ms-cui-ctl {vertical-align:text-top;text-decoration:none;color:rgb(51, 51, 51);} .ms-cui-ctl-on {background-color:rgb(223, 237, 250);opacity: 0.8;} .ms-cui-img-cont-float {display:inline-block;margin-top:2px} .ms-cui-smenu-inner {padding-top:0px;} .ms-owa-paste-option-icon {margin: 2px 4px 0px 4px;vertical-align:sub;padding-bottom: 2px;display:inline-block;} .ms-rtePasteFlyout-option:hover {background-color:rgb(223, 237, 250) !important;opacity:1 !important;} .ms-rtePasteFlyout-option {padding:8px 4px 8px 4px;outline:none;} .ms-cui-menusection {float:left; width:85px;height:24px;overflow:hidden}.wf {speak:none; font-weight:normal; font-variant:
n!
ormal; tex
t-transform:none; -webkit-font-smoothing:antialiased; vertical-align:middle; display:inline-block;}.wf-family-owa {font-family:'o365Icons'}@font-face { font-family:'o365IconsIE8'; src:url('https://r4.res.outlook.com/owa/prem/15.0.918.10/resources/styles/office365icons.ie8.eot?#iefix') format('embedded-opentype'), url('https://r4.res.outlook.com/owa/prem/15.0.918.10/resources/styles/office365icons.ie8.woff') format('woff'), url('https://r4.res.outlook.com/owa/prem/15.0.918.10/resources/styles/office365icons.ie8.ttf') format('truetype'); font-weight:normal; font-style:normal;}@font-face { font-family:'o365IconsMouse'; src:url('https://r4.res.outlook.com/owa/prem/15.0.918.10/resources/styles/office365icons.mouse.eot?#iefix') format('embedded-opentype'), url('https://r4.res.outlook.com/owa/prem/15.0.918.10/resources/styles/office365icons.mouse.woff') format('woff'), url('https://r4.res.outlook.com/owa/prem/15.0.918.10/resources/styles/office!
365icons.m
ouse.ttf') format('truetype'); font-weight:normal; font-style:normal;}.wf-family-owa {font-family:'o365IconsMouse'}.ie8 .wf-family-owa {font-family:'o365IconsIE8'}.ie8 .wf-owa-play-large:before {content:'\e254';}.notIE8 .wf-owa-play-large:before {content:'\e054';}.ie8 .wf-owa-play-large {color:#FFFFFF/*$WFWhiteColor*/;}.notIE8 .wf-owa-play-large {border-color:#FFFFFF/*$WFWhiteColor*/; width:1.4em; height:1.4em; border-width:.1em; border-style:solid; border-radius:.8em; text-align:center; box-sizing:border-box; -moz-box-sizing:border-box; padding:0.1em; color:#FFFFFF/*$WFWhiteColor*/;}.ie8 .wf-size-play-large {width:40px; height:40px; font-size:30px}.notIE8 .wf-size-play-large {width:40px; height:40px; font-size:30px}--></style>
<div id="OWAFontStyleDivID"
style="font-size:12pt;color:#000000;background-color:#FFFFFF;font-family:Calibri,Arial,Helvetica,sans-serif;">
<p>You are heading reinforcement learning and the
so-called temporal difference learning in it. This is a good
direction, since many little details can be mapped to the
corico-basal ganglia-thalamocortical loops. Nonetheless,
this can't explain everything. The separation of outliers
from the generalizable subspace(s) is relevant, since the
latter enables one to fill in missing information, whereas
the former does not. This was the take-home message of the
Netflix competition and the subsequent developments on exact
matrix completion.<br>
</p>
<p>Andras<br>
</p>
<div>
<p><br>
</p>
<div name="divtagdefaultwrapper"
style="font-family:Calibri,Arial,Helvetica,sans-serif;
font-size:; margin:0">
<div>
<div>
<div>
<div><font>
<p class="MsoNormal" style="font-size:13px;
font-family:Calibri,Arial,Helvetica,sans-serif;
margin:0cm 0cm 0pt"> _________________________<br>
<font size="2">Andras Lorincz<br>
ECCAI Fellow</font></p>
<p class="MsoNormal" style="font-size:13px;
font-family:Calibri,Arial,Helvetica,sans-serif;
margin:0cm 0cm 0pt"> <font size="2">email: <a
moz-do-not-send="true"
class="moz-txt-link-abbreviated"
href="mailto:lorincz@inf.elte.hu">lorincz@inf.elte.hu</a> <br>
home: </font><a moz-do-not-send="true"
href="http://people.inf.elte.hu/lorincz"><font
size="2">http://people.inf.elte.hu/lorincz</font></a><font
size="2"> </font></p>
</font></div>
</div>
</div>
</div>
</div>
</div>
<div style="color: rgb(40, 40, 40);">
<hr tabindex="-1" style="display:inline-block; width:98%">
<div id="divRplyFwdMsg" dir="ltr"><font
style="font-size:11pt" color="#000000" face="Calibri,
sans-serif"><b>From:</b> Connectionists <a
moz-do-not-send="true" class="moz-txt-link-rfc2396E"
href="mailto:connectionists-bounces@mailman.srv.cs.cmu.edu"><connectionists-bounces@mailman.srv.cs.cmu.edu></a>
on behalf of Danko Nikolic <a moz-do-not-send="true"
class="moz-txt-link-rfc2396E"
href="mailto:danko.nikolic@googlemail.com"><danko.nikolic@googlemail.com></a><br>
<b>Sent:</b> Monday, April 14, 2014 12:51 PM<br>
<b>To:</b> <a moz-do-not-send="true"
class="moz-txt-link-abbreviated"
href="mailto:connectionists@mailman.srv.cs.cmu.edu">connectionists@mailman.srv.cs.cmu.edu</a><br>
<b>Subject:</b> Re: Connectionists: how the brain works?
(UNCLASSIFIED)</font>
<div> </div>
</div>
<div>
<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 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">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">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"><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"><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"><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">
<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>
----------------------------------------------
</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;
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;
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;
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>
</div>
</div>
</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>
----------------------------------------------
</pre>
</body>
</html>