[Bmi] Brain Principles Manifesto (4.0): the society paragraph has been removed

[Juyang Weng]

Dear Colleagues:

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.

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The Brain Principles Manifesto
(Draft Version 4.0)

Feb. 27, 2015

Historically, public acceptance of science was slow.For example, Charles 
Darwin waited about 20 years (from the 1830s to 1858) to publish his 
theory of evolution for fear of public reaction.About 20 years later (by 
the 1870s) the scientific community and much of the general public had 
accepted evolution as a fact. Of course, the debate on evolution still 
goes on today.

Is the public acceptance of sciencefaster in modern days?Not necessarily 
so, even though we have now better and faster means to communicate.The 
primary reason is still the same but much more severe—the remaining open 
scientific problems are more complex and the required knowledge to 
convincingly understand goes beyond a typical single person.

For instance, network-like brain computation — connectionist computation 
— has been long doubted and ignored by industry./Convolutional deep/ 
networks appeared by at least 1980./Max-pooling/ in deep and fully 
automatic learning networks was published by 1992.However, Apple, Baidu, 
Google, Microsoft, Samsung, and other major related companies did not 
show considerable interest till after 2012.That is a delay of about 20 
years.The two techniques above are not very difficult to understand. 
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.

Industrial and academic interests have been keen on a combination of two 
things — easily understandable tests and major companies are 
involved.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. Why?This is because all these static training sets prevent 
participants from considering autonomous object segmentation.The neurons 
in human babies learn to segment objects through baby’s interaction with 
the real physical world.Do our industry and public need another 20 
years?Or more?

October 2011a highly respected multi-disciplinary 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.But if they try to do 
both at once, the result will be neither good science nor good 
engineering.”How long does it take for the industry and public to accept 
that the pessimisticview of the brain was no longer true even then?

The brain principles that have already been 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.

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.Prof. Jonathan Haidt documented 
well such shortsightedness in his book “/The Righteous Mind: Why Good 
People Are Divided by Politics and Religion/ 
<http://www.amazon.com/The-Righteous-Mind-Politics-Religion/dp/0307455777>”, 
although not in terms of brain computation.

In terms of brain computation, the circuits in your brain self-wire 
beautifullyand precisely according to your real-time experience (the 
genome only regulates) and their various invariance properties required 
for abstraction also largely depend on experience.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.Therefore, you 
make long-term mistakes but you still feel you are right (e.g., 
indignant and patriotic). Everybody is like that, including every 
politician.Depending on experience, shortsightedness varies in terms of 
subject matter.

Surprisingly, to understand how the brain works requires a sophisticated 
automata theory in computer science. See J. Weng, /Brain //as//an 
Emergent Finite Automaton: A Theory and Three Theorems/ 
<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>, 
/IJIS/, 2015, which proposed the following six brain principles:

1.The developmental program (genome-like, task-nonspecific) regulates 
the development (i.e., lifetime learning) of a task-nonspecific “brain” 
network (DN).The DN is of general-purpose—can learn any body-capable 
tasks, in principle.

2.The brain’s images are naturally sensed images of cluttered scenes 
where many objects mix.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.

3.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).

4.Every area in the “brain” DN emerges (does not statically exist, see 
M. Sur et al. /Nature/ 2000 and P. Voss, /Frontiers in Psychology/ 2013) 
using a unified area function whose feature development is nonlinear but 
free of local minima, contrary to engineering intuition.

5.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. Namely, a real brain has only one pair of 
stereoscopic retinas.

6.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.

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.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.No traditional automata can 
program themselves in the sense of Turing Machine but a brain automaton 
does.

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, /Cerebral Cortex/, 
1991). In particular, the model predicted that “shifting attention 
between `humans’ and `vehicles’ dramatically changes brain 
representation of all categories” (J. Gallant et al. /Nature 
Neuroscience/, 2013) and that human attention “can regulate the activity 
of their neurons in the medial temporal lobe” (C. Koch et al. /Nature/, 
2010). 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.

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. 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, /Physica D,/ 1990).The automata theory 
deals with such capabilities. Does this new knowledge stun our students 
and researchers or guide them so their time is better spent?

Understanding brain automata would enable us to see answers to a wide 
variety of important questions, some of which are raised below. We do 
not provide yes/no answers here, only raise questions.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.

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.Is it more scientifically productive for 
Side A to make friends with Side B so that Side B is not threatened and 
resist?Side A wants the brains in Side B to rewire for good instead of 
for bad, doesn’t it?

How can our industry and public understand that the door for 
understanding brains has opened for them?How can they see the economical 
outlooks that this opportunity leads them to?

How should our educational system reform to prepare our many bright 
minds for the new brain age?Has our government been prompt to properly 
respond to this modern call from the nature?

How should our young generationact for the new opportunity that is 
unfolding before their eyes?Is a currently narrowly defined academic 
degree sufficient for their career?

How can everybody take advantage of the new knowledge about his own 
brain so that he is more successful in his career?

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Please continue to send your comments.

-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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