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<font size="5"><font face="Arial">Dear Steve,<br>
<br>
On 4/6/14 4:30 PM, Stephen Grossberg wrote:<br>
> A non-technical but fairly comprehensive review article was
published in 2012 in Neural Networks<br>
> and can be found at <a
href="http://cns.bu.edu/%7Esteve/ART.pdf">http://cns.bu.edu/~steve/ART.pdf</a>.<br>
<br>
Please let us continue this conversation as it was very
difficult to have such in-depth<br>
academic conversations when we meet at a conference. I hope
that this conversation is not too boring<br>
for others on this list. I try to be concise and clear here.<br>
<br>
How do we better understand how the brain works? <br>
<br>
We know that there are many areas and subareas in each human
brain. <br>
<br>
Are these areas more like individual organs in the human body
(as Steve Pinker reasoned),<br>
or more like emergent statistical signal clusters (as I
proposed)?<br>
Probably both views are somewhat true in a sense,<br>
but what is a better "first-order" approximation through the
lifetime of each brain?<br>
<br>
Approach A: Static brain areas. First, draw a figure of brain
areas like your Fig. 4.<br>
Study and model the roles of these individual brain areas, like
your above article. <br>
The term "static" does not mean that each brain area does not
learn. <br>
In contrary, each brain area does learn and adapt. <br>
The more areas one's model contains, the more complete his brain
model is. <br>
In your Fig. 4, you have V2, V3, V4, ITa, ITb, SC, LIP, PFC, and
PPC. <br>
<br>
Approach B: Dynamic brain areas. The entire brain is a single,
but highly dynamic area Y.<br>
Regard brain areas as results of development from living
experience. This development is regulated<br>
by a set of somewhat general-purpose developmental mechanisms in
each brain cell (e.g.,<br>
the laminar architecture). <br>
All receptors in the body are denoted as a set X, which contains
a group of receptors,<br>
X1, X2, X3, ... , Xm where Xi, i=1, 2, ..., m is a sensory
organ. <br>
For example, X1 and X2 are left retina and right retina,
respectively.<br>
X3 and X4 contain all hair cells in the left cochlea and right
cochlea, respectively.<br>
Likewise, all muscles and glands in the body are denoted as a
set Z, which contains a group of<br>
effectors, Z1, Z2, Z3, ... , Zn where Zi, i=1, 2, ..., n is an
effector organ. <br>
The entire nervous system consists of many cells as a set Y. <br>
There is no statically modeled Brodmann areas inside Y, because<br>
Brodmann areas are only applicable for a normal human. <br>
For a congenitally blind, e.g., all visual areas are mostly
assigned to vision and touch. <br>
<br>
Our Cresceptron 1992, in a sense, followed Approach A. <br>
Developmental Networks (DN) since 2007, with its embodiments<br>
Where-What Networks (WWN-1 2008, through WWN-8 2013) followed
Approach B. <br>
<br>
That is probably why Asim Roy was looking for architecture
similarities between<br>
DN and other traditional neural networks. However, complex
architectures in Y autonomously emerge. <br>
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<br>
from Z to everywhere in the brain Y.<br>
<br>
Why? This is probably because the analysis and understanding
are both challenging. <br>
We reached a rigorous analysis of a general-purpose Y using the
finite automata theory. <br>
<br>
For example, the largely one-way connections among areas<br>
V2, V3, V4, ITa, ITb, SC, LIP, PFC, and PPC in your Fig. 4 are
inconsistent with<br>
ensemble knowledge in neural anatomy (e.g., Felleman and Van
Essen 1991 [1]). Their<br>
connection tables show that almost all connections between two
brain areas are two-way.<br>
You said to me that you have not included all the connections in
figures in Fig. 4. However,<br>
if you include all the missing connections, your explanation in
the papers does not hold any more.<br>
<br>
[1] D. J. Felleman and D. C. Van Essen, Distributed hierarchical
processing in the primate cerebral cortex, Cerebral Cortex,1,
1-47, 1991.<br>
<br>
-John<br>
<br>
</font></font>-- --
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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