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I enjoyed many views of you, including those of Jim Bower, Richard
Loosemore, Ali Minai, and Thomas Trappenberg. Let me give a humble
suggestion, when you hear a detailed view that looks "polarizing",
do not get discouraged as your emotion is mistreating you. Find out
whether the detail fits many brain functions. <br>
<br>
Big data and brain-like computing will fail if such a project is
without the guidance of a brain-scale model. <br>
<br>
Note: vague statements are not very useful here as everybody can
give many. A brain-scale model must be very detailed
computationally. The more detailed, the more brain-scale functions
it covers, and the fewer mechanisms it uses, the better. <br>
<br>
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For example, the Spaun model claimed to be "
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the world's largest functional brain model". With great respect, I
congratulate its appearance in Science 2012. But, unfortunately,
Science editors are not in a position to judge how close a brain
model is. I understand that no model of the brain is perfect and
every model is an approximation of the nature. From my brain-scale
model, I think that a minimal requirement for a reviewer on a brain
model must have a formal training (e.g., a 3-credit course and pass
all its exams) in all the following: <br>
<br>
(1) computer vision,<br>
(2) artificial intelligence,<br>
(3) automata theory and computational complexity, <br>
(4) electrical engineering, such as signals and systems, control,
conventional neural networks,<br>
(5) biology,<br>
(6) neuroscience,<br>
(7) cognitive science, such as learning and memory, human vision
systems, and developmental psychology,<br>
(8) mathematics, such as linear algebra, probability, statistics,
and optimization theory.<br>
If you do not have some of the above, take such courses as soon as
possible. BMI summer courses 2014 will offer some. <br>
<br>
If you have taken all the above courses, you will know that the
Spaun model is grossly wrong (and, with respect, the deep learning
net of Geoffery Hinton for the same reason). <br>
<br>
Why? I just give the first mechanism that every brain must have and
thus every brain model must have:<br>
learning and recognizing unknown objects FROM unknown cluttered
backgrounds and producing desired behaviors<br>
<br>
Note: not just recognizing but learning; not a single object in a
clean background that Spaun demonstrated but also simultaneous
multiple objects in a cluttered backgrounds. No objects can be
pre-segmented from the cluttered background during learning. That
is how a baby learns. <br>
<br>
None of the tasks that Spaun did includes cluttered background, let
along learning directly from cluttered scenes. <br>
<br>
Attention is the first basic mechanism of the brain learned from the
baby time, not recognizing a pattern in a clean background. <br>
<br>
Autonomously learning attention is the single most important
mechanism for Big Data and Brain-Like Computing! <br>
How? Read
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<a
href="http://www.brain-mind-magazine.org/read.php?file=BMM-V2-N2-a1-HowBrainMind-a.pdf">How
the Brain-Mind Works: A Two-Page Introduction to a Theory</a>
<img src="cid:part2.07050806.03090906@cse.msu.edu" alt="banner"
style="float:left;margin:0 5px 0 0;" height="100" width="100"> <br>
<br>
<br>
<br>
<br>
<br>
<br>
-John<br>
<br>
<br>
<div class="moz-cite-prefix">On 1/24/14 9:03 PM, Thomas Trappenberg
wrote:<br>
</div>
<blockquote
cite="mid:CAJf2TrdD+068bJZV7HO-fT5BPRqR1wXZAu2zqUhDZVmHhdnC=w@mail.gmail.com"
type="cite">
<div dir="ltr">
<div>
<div>
<div>Thanks John for starting a discussion ... I think we
need some. What I liked most about your original post was
asking about "What are the underlying principles?" Let's
make a list.<br>
</div>
Of course, there are so many levels of organizations and
mechanisms in the brain, that we might speak about different
things; but getting different views would be fun and I think
very useful (without the need to offer the only and
ultimate).<br>
<br>
</div>
Cheers, Thomas Trappenberg<br>
<br>
<br>
</div>
PS: John, I thought you started a good discussion before, but I
got discouraged by your polarizing views. I think a lot of us
can relate to you, but lhow about letting others come forward
now?<br>
<br>
</div>
<div class="gmail_extra"><br>
<br>
<div class="gmail_quote">On Fri, Jan 24, 2014 at 9:02 PM, Ivan
Raikov <span dir="ltr"><<a moz-do-not-send="true"
href="mailto:ivan.g.raikov@gmail.com" target="_blank">ivan.g.raikov@gmail.com</a>></span>
wrote:<br>
<blockquote class="gmail_quote" style="margin:0 0 0
.8ex;border-left:1px #ccc solid;padding-left:1ex">
<div dir="ltr"><br>
<div class="gmail_extra">I think perhaps the objection to
the Big Data approach is that it is applied to the
exclusion of all other modelling approaches. While it is
true that complete and detailed understanding of
neurophysiology and anatomy is at the heart of
neuroscience, a lot can be learned about signal
propagation in excitable branching structures using
statistical physics, and a lot can be learned about
information representation and transmission in the brain
using mathematical theories about distributed
communicating processes. As these modelling approaches
have been successfully used in various areas of science,
wouldn't you agree that they can also be used to
understand at least some of the fundamental properties
of brain structures and processes? <br>
<br>
</div>
<div class="gmail_extra"> -Ivan Raikov<br>
</div>
<div class="gmail_extra"><br>
<div class="gmail_quote">On Sat, Jan 25, 2014 at 8:31
AM, james bower <span dir="ltr"><<a
moz-do-not-send="true"
href="mailto:bower@uthscsa.edu" target="_blank">bower@uthscsa.edu</a>></span>
wrote:<br>
<blockquote class="gmail_quote" style="margin:0px 0px
0px 0.8ex;border-left:1px solid
rgb(204,204,204);padding-left:1ex">
<div>[snip] <br>
</div>
</blockquote>
<div class="im">
<blockquote class="gmail_quote" style="margin:0px
0px 0px 0.8ex;border-left:1px solid
rgb(204,204,204);padding-left:1ex">
<div style="word-wrap:break-word">
<div>An enormous amount of engineering and
neuroscience continues to think that the
feedforward pathway is from the sensors to the
inside - rather than seeing this as the actual
feedback loop. Might to some sound like a
semantic quibble, but I assure you it is not.</div>
<div><br>
</div>
<div>If you believe as I do, that the brain
solves very hard problems, in very
sophisticated ways, that involve, in some
sense the construction of complex models about
the world and how it operates in the world,
and that those models are manifest in the
complex architecture of the brain - then
simplified solutions are missing the point.</div>
<div><br>
</div>
<div>What that means inevitably, in my view, is
that the only way we will ever understand what
brain-like is, is to pay tremendous attention
experimentally and in our models to the actual
detailed anatomy and physiology of the brains
circuits and cells.</div>
<br>
</div>
</blockquote>
</div>
</div>
</div>
</div>
</blockquote>
</div>
<br>
</div>
</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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