Connectionists: Brain-like computing fanfare and big data fanfare

[Juyang Weng]

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.

Big data and brain-like computing will fail if such a project is without 
the guidance of a brain-scale model.

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.

For example, the Spaun model claimed to be " 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:

(1) computer vision,
(2) artificial intelligence,
(3) automata theory and computational complexity,
(4) electrical engineering, such as signals and systems, control, 
conventional neural networks,
(5) biology,
(6) neuroscience,
(7) cognitive science, such as learning and memory, human vision 
systems, and developmental psychology,
(8) mathematics, such as linear algebra, probability, statistics, and 
optimization theory.
If you do not have some of the above, take such courses as soon as 
possible.   BMI summer courses 2014 will offer some.

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).

Why?  I just give the first mechanism that every brain must have and 
thus every brain model must have:
learning and recognizing unknown objects FROM unknown cluttered 
backgrounds and producing desired behaviors

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.

None of the tasks that Spaun did includes cluttered background, let 
along learning directly from cluttered scenes.

Attention is the first basic mechanism of the brain learned from the 
baby time, not recognizing a pattern in a clean background.

Autonomously learning attention is the single most important mechanism 
for Big Data and Brain-Like Computing!
How?  Read How the Brain-Mind Works: A Two-Page Introduction to a Theory 
<http://www.brain-mind-magazine.org/read.php?file=BMM-V2-N2-a1-HowBrainMind-a.pdf> 
banner






-John


On 1/24/14 9:03 PM, Thomas Trappenberg wrote:
> 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.
> 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).
>
> Cheers, Thomas Trappenberg
>
>
> 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?
>
>
>
> On Fri, Jan 24, 2014 at 9:02 PM, Ivan Raikov <ivan.g.raikov at gmail.com 
> <mailto:ivan.g.raikov at gmail.com>> wrote:
>
>
>     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?
>
>       -Ivan Raikov
>
>     On Sat, Jan 25, 2014 at 8:31 AM, james bower <bower at uthscsa.edu
>     <mailto:bower at uthscsa.edu>> wrote:
>
>         [snip]
>
>         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.
>
>         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.
>
>         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.
>
>

-- 
--
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: weng at cse.msu.edu
URL: http://www.cse.msu.edu/~weng/
----------------------------------------------

-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mailman.srv.cs.cmu.edu/pipermail/connectionists/attachments/20140126/987e9567/attachment-0001.html>
-------------- next part --------------
A non-text attachment was scrubbed...
Name: BMM-V2-N2-a1-HowBrainMind-i.jpg
Type: image/jpeg
Size: 696158 bytes
Desc: not available
URL: <http://mailman.srv.cs.cmu.edu/pipermail/connectionists/attachments/20140126/987e9567/attachment-0001.jpg>