Connectionists: how the brain works?

[Juyang Weng]

Dear Steve,

On 4/6/14 4:30 PM, Stephen Grossberg wrote:
 > A non-technical but fairly comprehensive review article was published 
in 2012 in Neural Networks
 > and can be found at http://cns.bu.edu/~steve/ART.pdf 
<http://cns.bu.edu/%7Esteve/ART.pdf>.

Please let us continue this conversation as it was very difficult to 
have such in-depth
academic conversations when we meet at a conference.  I hope that this 
conversation is not too boring
for others on this list.   I try to be concise and clear here.

How do we better understand how the brain works?

We know that there are many areas and subareas in each human brain.

Are these areas more like individual organs in the human body (as Steve 
Pinker reasoned),
or more like emergent statistical signal clusters (as I proposed)?
Probably both views are somewhat true in a sense,
but what is a better "first-order" approximation through the lifetime of 
each brain?

Approach A: Static brain areas.   First, draw a figure of brain areas 
like your Fig. 4.
Study and model the roles of these individual brain areas, like your 
above article.
The term "static" does not mean that each brain area does not learn.
In contrary, each brain area does learn and adapt.
The more areas one's model contains, the more complete his brain model is.
In your Fig. 4, you have V2, V3, V4, ITa, ITb, SC, LIP, PFC, and PPC.

Approach B: Dynamic brain areas.  The entire brain is a single, but 
highly dynamic area Y.
Regard brain areas as results of development from living experience.  
This development is regulated
by a set of somewhat general-purpose developmental mechanisms in each 
brain cell (e.g.,
the laminar architecture).
All receptors in the body are denoted as a set X, which contains a group 
of receptors,
X1, X2, X3, ... , Xm where Xi, i=1, 2, ..., m is a sensory organ.
For example, X1 and X2 are left retina and right retina, respectively.
X3 and X4 contain all hair cells in the left cochlea and right cochlea, 
respectively.
Likewise, all muscles and glands in the body are denoted as a set Z, 
which contains a group of
effectors, Z1, Z2, Z3, ... , Zn where Zi, i=1, 2, ..., n is an effector 
organ.
The entire nervous system consists of many cells as a set Y.
There is no statically modeled Brodmann areas inside Y, because
Brodmann areas are only applicable for a normal human.
For a congenitally blind, e.g., all visual areas are mostly assigned to 
vision and touch.

Our Cresceptron 1992, in a sense, followed Approach A.
Developmental Networks (DN) since 2007, with its embodiments
Where-What Networks (WWN-1 2008, through WWN-8 2013) followed Approach B.

That  is probably why Asim Roy was looking for architecture similarities 
between
DN and other traditional neural networks.  However, complex 
architectures in Y autonomously emerge.
Hopefully, with all human receptors and effectors and human experience, 
Y would emerge a human-like brain.    As far as I know, there were no 
prior published networks that allow signal projections
from Z to everywhere in the brain Y.

Why?  This is probably because the analysis and understanding are both 
challenging.
We reached a rigorous analysis of a general-purpose Y using the finite 
automata theory.

For example, the largely one-way connections among areas
V2, V3, V4, ITa, ITb, SC, LIP, PFC, and PPC in your Fig. 4 are 
inconsistent with
ensemble knowledge in neural anatomy (e.g., Felleman and Van Essen 1991 
[1]).  Their
connection tables show that almost all connections between two brain 
areas are two-way.
You said to me that you have not included all the connections in figures 
in Fig. 4.  However,
if you include all the missing connections, your explanation in the 
papers does not hold any more.

[1] D. J. Felleman and D. C. Van Essen, Distributed hierarchical 
processing in the primate cerebral cortex, Cerebral Cortex,1, 1-47, 1991.

-John

-- -- 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/ 
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