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Dear Colleagues:<br>
<br>
Thank those who have given inputs. Indeed, the Manifesto does not
need to be linked with any society. Therefore the last paragraph
about the new society has been removed. In addition, some
citations have been removed and some brain evidence added. The
removal of the last paragraph is a major change.<br>
<br>
----<br>
The Brain Principles Manifesto<br>
(Draft Version 4.0)<br>
<br>
Feb. 27, 2015<br>
<br>
<meta name="Title" content="">
<p class="MsoNormal">Historically, public acceptance of science was
slow.<span style="mso-spacerun:yes"> </span>For example, Charles
Darwin waited about 20
years (from the <span style="mso-fareast-font-family:"Times
New Roman"">1830s
to 1858)</span> to publish his theory of evolution for fear of
public
reaction.<span style="mso-spacerun:yes"> </span>About 20 years
later (by the
1870s) the scientific community and much of the general public had
accepted
evolution as a fact. <span style="mso-spacerun:yes"> </span>Of
course, the
debate on evolution still goes on today. <o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">Is the public acceptance of science<span
style="font-family:
华文黑体;mso-bidi-font-family:华文黑体;mso-fareast-language:ZH-CN"> </span>faster
in
modern days?<span style="mso-spacerun:yes"> </span>Not
necessarily so, even
though we have now better and faster means to communicate.<span
style="mso-spacerun:yes"> </span><span
style="mso-spacerun:yes"> </span>The primary
reason is still the same but much more severe<span class="st"><span
style="mso-fareast-font-family:"Times New Roman"">—</span></span>the
remaining
open scientific problems are more complex and the required
knowledge to convincingly
understand goes beyond a typical single person. <o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">For instance, network-like brain computation <span
class="st"><span style="mso-fareast-font-family:"Times New
Roman"">— </span></span>connectionist
computation <span class="st"><span
style="mso-fareast-font-family:"Times New Roman"">—
</span></span>has been long doubted and ignored by industry.<span
style="mso-spacerun:yes"> </span><i
style="mso-bidi-font-style:normal">Convolutional
deep</i> networks appeared by at least 1980.<span
style="mso-spacerun:yes">
</span><i style="mso-bidi-font-style:normal">Max-pooling</i> in
deep and fully
automatic learning networks was published by 1992.<span
style="mso-spacerun:yes"> </span>However, Apple, Baidu,
Google, Microsoft,
Samsung, and other major related companies did not show
considerable interest
till after 2012.<span style="mso-spacerun:yes"> </span>That is a
delay of
about 20 years.<span style="mso-spacerun:yes"> </span>The two
techniques above
are not very difficult to understand. <span
style="mso-spacerun:yes"> </span>However, these two suddenly hot
techniques have
already been proved obsolete by the discoveries of more
fundamental and
effective principles of the brain, six of which are intuitively
explained below.<span style="mso-spacerun:yes"> </span><span
style="mso-spacerun:yes"> </span><o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">Industrial and academic interests have been
keen on a
combination of two things <span class="st"><span
style="mso-fareast-font-family:
"Times New Roman"">—</span></span> easily
understandable tests and major
companies are involved.<span style="mso-spacerun:yes"> </span>However,
the newly
known brain principles have told us that the ways to conduct such
tests will give
only vanishing gains that do not lead to a human-like zero error
rate,
regardless how many more images can be added to the training sets
and how long
the Moore’s Law can continue. <span style="mso-spacerun:yes"> </span>Why?<span
style="mso-spacerun:yes"> </span>This is because all these
static training
sets prevent participants from considering autonomous object
segmentation.<span style="mso-spacerun:yes"> </span>The neurons
in human babies learn to segment
objects through baby’s interaction with the real physical world.<span
style="mso-spacerun:yes"> </span>Do our industry and public
need another 20
years?<span style="mso-spacerun:yes"> </span>Or more? <o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">October 2011<span
style="font-family:华文黑体;mso-bidi-font-family:
华文黑体;mso-fareast-language:ZH-CN"> </span>a highly respected
multi-<span style="font-family:Times">disciplinary</span>
professor kindly wrote: “I tell
these students that they can work on brains and do good science,
or work on
robots and do good engineering.<span style="mso-spacerun:yes"> </span>But
if
they try to do both at once, the result will be neither good
science nor good engineering.”<span style="mso-spacerun:yes"> </span>How
long does it take for the industry and
public to accept that the pessimistic<span
style="font-family:Times;mso-bidi-font-family:
华文黑体;mso-fareast-language:ZH-CN"> view</span> of the brain was
no longer true
even then?<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">The brain principles that have already <span
style="font-family:Times;mso-bidi-font-family:华文黑体;mso-fareast-
language:ZH-CN">been
</span>discovered could bring fundamental changes in the way
humans live, the
way countries and societies are organized, our industry, our
economy, and the
way humans treat one another.<span style="mso-spacerun:yes"> </span><span
style="font-family:华文黑体;mso-bidi-font-family:华文黑体;mso-fareast-
language:ZH-CN"><o:p></o:p></span></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">The known brain principles have told us that
the brain of
anybody, regardless of his education and experience, is
fundamentally shortsighted,
in both space and time.<span style="mso-spacerun:yes"> </span>Prof.
<span class="st"><span style="mso-fareast-font-family:"Times
New Roman"">Jonathan Haidt
documented well such shortsightedness in his book “</span></span><a
href="http://www.amazon.com/The-Righteous-Mind-Politics-Religion/dp/0307455777"><i
style="mso-bidi-font-style:normal"><span
style="mso-fareast-font-family:"Times New Roman"">The
Righteous
Mind: Why Good People Are Divided by Politics and Religion</span></i></a><span
class="st"><span style="mso-fareast-font-family:"Times New
Roman"">”, although not
in terms of brain computation. <span style="mso-spacerun:yes"> </span><o:p></o:p></span></span></p>
<p class="MsoNormal"><span class="st"><span
style="mso-fareast-font-family:"Times New Roman""><o:p> </o:p></span></span></p>
<p class="MsoNormal"><span class="st"><span
style="mso-fareast-font-family:"Times New Roman"">In
terms of brain computation, the circuits in your brain
self-wire </span></span><span
style="mso-fareast-font-family:"Times New Roman"">beautifully<span
class="st"> and
precisely according to your real-time experience (the genome
only regulates)
and their various invariance properties required for
abstraction also largely
depend on experience.<span style="mso-spacerun:yes"> </span>Serotonin
(caused
by, e.g., threats), dopamine (caused by e.g., praise), and
other neural
transmitters quickly change the way these circuits work so
that neurons for more
long-term thoughts lost in competition to fire.<span
style="mso-spacerun:yes">
</span>Therefore, you make long-term mistakes but you still
feel you are right
(e.g., indignant and patriotic). <span
style="mso-spacerun:yes"> </span>Everybody is like that,
including every
politician.<span style="mso-spacerun:yes"> </span>Depending
on experience, shortsightedness
varies in terms of subject matter. <span
style="mso-spacerun:yes"> </span><o:p></o:p></span></span></p>
<p class="MsoNormal"><span class="st"><span
style="mso-fareast-font-family:"Times New Roman""><o:p> </o:p></span></span></p>
<p class="MsoNormal"><span class="st"><span
style="mso-fareast-font-family:"Times New Roman"">Surprisingly,
to
understand how the brain works requires a sophisticated
automata theory in
computer science. See J. Weng, </span></span><a
href="http://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&ved=0CCAQFjAA&url=http%3A%2F%2Fwww.scirp.org%2Fjournal%2FPaperDownload.aspx%3FpaperID%3D53728&ei=21rnVP7fB4KZyAT-noH4Cg&usg=AFQjCNEzHz-YDhivI1esDtgKg84SEx4RuQ&bvm=bv.86475890,d.aWw&cad"><i
style="mso-bidi-font-style:normal"><span
style="mso-fareast-font-family:"Times New Roman"">Brain
</span></i><i style="mso-bidi-font-style:normal"><span
style="font-family:Times;
mso-bidi-font-family:华文黑体">as</span></i><i
style="mso-bidi-font-style:normal"><span
style="mso-fareast-font-family:"Times New Roman"">
an Emergent Finite
Automaton: A Theory and Three Theorems</span></i></a><span
class="st"><span style="mso-fareast-font-family:"Times New
Roman"">, <i style="mso-bidi-font-style:
normal">IJIS</i>, 2015, which proposed the following six
brain principles:<o:p></o:p></span></span></p>
<p class="MsoListParagraphCxSpFirst"
style="margin-left:42.0pt;mso-add-space:
auto;text-indent:-.25in;mso-list:l0 level1 lfo1"><!--[if !supportLists]--><span
class="st"><span style="mso-fareast-font-family:"Times New
Roman""><span style="mso-list:Ignore">1.<span
style="font:7.0pt "Times New Roman"">
</span></span></span></span><!--[endif]--><span class="st"><span
style="mso-fareast-font-family:
"Times New Roman"">The developmental program
(genome-like, task-nonspecific)
regulates the development (i.e., lifetime learning) of a
task-nonspecific
“brain” network (DN).<span style="mso-spacerun:yes"> </span>The
DN is of
general-purpose—can learn any body-capable tasks, in
principle. <o:p></o:p></span></span></p>
<p class="MsoListParagraphCxSpMiddle"
style="margin-left:42.0pt;mso-add-space:
auto;text-indent:-.25in;mso-list:l0 level1 lfo1"><!--[if !supportLists]--><span
class="st"><span style="mso-fareast-font-family:"Times New
Roman""><span style="mso-list:Ignore">2.<span
style="font:7.0pt "Times New Roman"">
</span></span></span></span><!--[endif]--><span class="st"><span
style="mso-fareast-font-family:
"Times New Roman"">The brain’s images are naturally
sensed images of cluttered
scenes where many objects mix.<span style="mso-spacerun:yes">
</span>As a
child learns to see, teachers do not draw a bounding box
around each learned object
in her retinal image — DN self-learns segmentation.<o:p></o:p></span></span></p>
<p class="MsoListParagraphCxSpMiddle"
style="margin-left:42.0pt;mso-add-space:
auto;text-indent:-.25in;mso-list:l0 level1 lfo1"><!--[if !supportLists]--><span
class="st"><span style="mso-fareast-font-family:"Times New
Roman""><span style="mso-list:Ignore">3.<span
style="font:7.0pt "Times New Roman"">
</span></span></span></span><!--[endif]--><span class="st"><span
style="mso-fareast-font-family:
"Times New Roman"">The brain’s muscles have multiple
subareas where each
subarea represents either declarative knowledge (e.g.,
abstract concepts such
as location, type, scale, etc.) or non-declarative knowledge
(e.g., driving a
car or riding a bicycle). <o:p></o:p></span></span></p>
<p class="MsoListParagraphCxSpMiddle"
style="margin-left:42.0pt;mso-add-space:
auto;text-indent:-.25in;mso-list:l0 level1 lfo1"><!--[if !supportLists]--><span
class="st"><span style="mso-fareast-font-family:"Times New
Roman""><span style="mso-list:Ignore">4.<span
style="font:7.0pt "Times New Roman"">
</span></span></span></span><!--[endif]--><span class="st"><span
style="mso-fareast-font-family:
"Times New Roman"">Every area in the “brain” DN
emerges (does not statically exist,
see M. Sur et al. <i style="mso-bidi-font-style:normal">Nature</i>
2000 and P.
Voss, <i style="mso-bidi-font-style:normal">Frontiers in
Psychology</i> 2013) using
a unified area function whose feature development is nonlinear
but free of
local minima, contrary to engineering intuition.<o:p></o:p></span></span></p>
<p class="MsoListParagraphCxSpMiddle"
style="margin-left:42.0pt;mso-add-space:
auto;text-indent:-.25in;mso-list:l0 level1 lfo1"><!--[if !supportLists]--><span
class="st"><span style="mso-fareast-font-family:"Times New
Roman""><span style="mso-list:Ignore">5.<span
style="font:7.0pt "Times New Roman"">
</span></span></span></span><!--[endif]--><span class="st"><span
style="mso-fareast-font-family:
"Times New Roman"">The “brain” DN learns
incrementally—taking one-pair of
sensory pattern and motor pattern at a time to update the
“brain” and
discarding the pair immediately after. <span
style="mso-spacerun:yes"> </span>Namely,
a real brain has only one pair of stereoscopic retinas. <o:p></o:p></span></span></p>
<p class="MsoListParagraphCxSpLast"
style="margin-left:42.0pt;mso-add-space:auto;
text-indent:-.25in;mso-list:l0 level1 lfo1"><!--[if !supportLists]--><span
class="st"><span style="mso-fareast-font-family:"Times New
Roman""><span style="mso-list:Ignore">6.<span
style="font:7.0pt "Times New Roman"">
</span></span></span></span><!--[endif]--><span class="st"><span
style="mso-fareast-font-family:
"Times New Roman"">The “brain” DN is always
optimal—Each DN update in real time
computes the maximum likelihood estimate of the “brain”,
conditioned on the
limited computational resources and the limited learning
experience in its
“life” so far.<o:p></o:p></span></span></p>
<p class="MsoNormal" style="margin-left:24.0pt"><span class="st"><span
style="mso-fareast-font-family:"Times New Roman""><o:p> </o:p></span></span></p>
<p class="MsoNormal"><span class="st"><span
style="mso-fareast-font-family:"Times New Roman"">The
logic
completeness of a brain is (partially, not all) understood by
a universal
Turing Machine in DN which is like our modern-day computer, in
principle.<span style="mso-spacerun:yes"> </span>This
automaton brain model proposes that each
brain is an automaton, but also very different from all
traditional symbolic
automata because it programs itself—emergent.<span
style="mso-spacerun:yes">
</span>No traditional automata can program themselves in the
sense of Turing
Machine but a brain automaton does. <o:p></o:p></span></span></p>
<p class="MsoNormal"><span class="st"><span
style="mso-fareast-font-family:"Times New Roman""><o:p> </o:p></span></span></p>
<p class="MsoNormal"><span class="st"><span
style="mso-fareast-font-family:"Times New Roman"">The
automaton
brain model has predicted that neural circuits dynamically and
precisely
record the statistics of experience, roughly consistent with
neural anatomy
(e.g., Felleman & Van Essen, <i
style="mso-bidi-font-style:normal">Cerebral
Cortex</i>, 1991). <span style="mso-spacerun:yes"> </span>In
particular, the
model predicted that “shifting attention between `humans’ and
`vehicles’
dramatically changes brain representation of all categories”
(J. Gallant et al.
<i style="mso-bidi-font-style:normal">Nature Neuroscience</i>,
2013) and that human
attention “can regulate the activity of their neurons in the
medial temporal
lobe” (C. Koch et al. <i style="mso-bidi-font-style:normal">Nature</i>,
2010). <span style="mso-spacerun:yes"> </span>The “place”
cells work of the 2014 Nobel Prize
in Physiology or Medicine implies that neurons encode
exclusively bottom-up
information (place). The automaton brain model challenges such
a view: Neurons
represent a combination of both bottom-up (e.g., place) and
top-down context (e.g.,
goal) as reported by Koch et al. and Gallant et al. <o:p></o:p></span></span></p>
<p class="MsoNormal"><span class="st"><span
style="mso-fareast-font-family:"Times New Roman""><o:p> </o:p></span></span></p>
<p class="MsoNormal"><span class="st"><span
style="mso-fareast-font-family:"Times New Roman"">Unfortunately,
the
automaton brain model implies that all neuroscientists and
neural network
researchers are unable to understand the brain of their
studies without a rigorous
training in automata theory. <span style="mso-spacerun:yes"> </span>For
example,
traditional models for nervous systems and neural networks
focus on
pattern recognition and do not have the capabilities of a
grounded symbol
system (e.g., “rulefully combining and recombining,” Stevan
Harnad, <i style="mso-bidi-font-style:normal">Physica D,</i>
1990).<span style="mso-spacerun:yes"> </span>The automata
theory deals with such
capabilities. <span style="mso-spacerun:yes"> </span>Does
this new knowledge
stun our students and researchers or guide them so their time
is better spent?<o:p></o:p></span></span></p>
<p class="MsoNormal"><span class="st"><span
style="mso-fareast-font-family:"Times New Roman""><o:p> </o:p></span></span></p>
<p class="MsoNormal"><span class="st"><span
style="mso-fareast-font-family:"Times New Roman"">Understanding
brain
automata would enable us to see answers to a wide variety of
important
questions, some of which are raised below. <span
style="mso-spacerun:yes"> </span>We do not provide yes/no
answers here, only
raise questions.<span style="mso-spacerun:yes"> </span>The
automaton brain
model predicts that there is no absolute right or wrong in any
brain but its
environmental experiences wire and rewire the brain. <o:p></o:p></span></span></p>
<p class="MsoNormal"><span class="st"><span
style="mso-fareast-font-family:"Times New Roman""><o:p> </o:p></span></span></p>
<p class="MsoNormal"><span class="st"><span
style="mso-fareast-font-family:"Times New Roman"">In
each question of social science, we may have two sides, Side A
with more
knowledge due to the relatively more open political and
ideological environment
and Side B with less knowledge due to relatively less open
political and ideological
environment, but all having normal human brains that deserve
respect.<span style="mso-spacerun:yes"> </span>Is it more
scientifically productive for
Side A to make friends with Side B so that Side B is not
threatened and resist?<span style="mso-spacerun:yes"> </span>Side
A wants the brains in Side B to rewire
for good instead of for bad, doesn’t it?<o:p></o:p></span></span></p>
<p class="MsoNormal"><span class="st"><span
style="mso-fareast-font-family:"Times New Roman""><o:p> </o:p></span></span></p>
<p class="MsoNormal"><span class="st"><span
style="mso-fareast-font-family:"Times New Roman"">How
can
our industry and public understand that the door for
understanding brains has
opened for them?<span style="mso-spacerun:yes"> </span>How
can they see the
economical outlooks that this opportunity leads them to?<o:p></o:p></span></span></p>
<p class="MsoNormal"><span class="st"><span
style="mso-fareast-font-family:"Times New Roman""><o:p> </o:p></span></span></p>
<p class="MsoNormal"><span class="st"><span
style="mso-fareast-font-family:"Times New Roman"">How
should
our educational system reform to prepare our many bright minds
for the
new brain age?<span style="mso-spacerun:yes"> </span>Has
our government been
prompt to properly respond to this modern call from the
nature?</span></span><span class="st"><span
style="font-family:华文黑体;mso-bidi-font-family:华文黑体;mso-fareast-language:
ZH-CN"><o:p></o:p></span></span></p>
<p class="MsoNormal"><span class="st"><span
style="mso-fareast-font-family:"Times New Roman""><o:p> </o:p></span></span></p>
<p class="MsoNormal"><span class="st"><span
style="mso-fareast-font-family:"Times New Roman"">How
should
our young generation</span></span><span class="st"><span
style="font-family:
华文黑体;mso-bidi-font-family:华文黑体;mso-fareast-language:ZH-CN"> </span></span><span
class="st"><span style="mso-fareast-font-family:"Times New
Roman"">act for the
new opportunity that is unfolding before their eyes?<span
style="mso-spacerun:yes"> </span>Is a currently narrowly
defined academic degree
sufficient for their career?<o:p></o:p></span></span></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">How can everybody take advantage of the new
knowledge about
his own brain so that he is more successful in his career?<span
style="mso-spacerun:yes"> </span><o:p></o:p></p>
---<br>
Please continue to send your comments. <br>
<br>
-John
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<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>
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