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Intelligence Science
for Creating a Brain
By Professor Zhongzhi Shi
Life’s activity is the most advanced
movement in the nature. The human
brain is the most complicated material
in the world, and is the physiological
foundation of man’s intelligence and
advanced spiritual activities. The
brain is an organ recognizing the
world, so it is essential to understand
the physiological mechanism of the
brain, and its highly complicated and
orderly material for studying human
cognitive process and intelligent
mechanism. Brain science and neural
science promote enormously the
development of intelligent science
through studying natural intelligent
mechanisms at molecular, cellular
and behavioral levels, setting up
brain models and revealing human
brain’s nature. Neurophysiology and
neuroanatomy form the bedrock of
neural science. The former introduces
functions of the nervous system while
the latter introduces its structures.
Intelligence science is an
interdisciplinary subject dedicated
to joint research on basic theory and
technology of intelligence by brain
science, cognitive science, artificial
intelligence and others.1 Brain science
explores the essence of brain research
on the principle and model of natural
intelligence at the molecular, cell and
behavior level. Cognitive science
studies human mental activity, such
as perception, learning, memory,
thinking, consciousness etc. In order
to implement machine intelligence,
artificial intelligence attempts
simulation, extension and expansion
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of human intelligence using artificial
methodology and technology.
Research scientists coming from the
above three disciplines work together
to explore new concepts, theories,
and methodologies.
In order to create the brain,
more research has to be done on
intelligence science, especially the
neocortical column, mind model and
others. The neocortical column is a
group of neurons in the brain cortex
which can be successively penetrated
to the cortical surface, and which
have nearly identical receptive fields.
Since 1957 when V.B. Mountcastle
discovered the column structure,
there have been many research results
showing that in the visual, auditory,
somatosensory, and motor cortices,
as well as other co-existing cortices
of different species (rat, cat, rabbit,
monkey and human, etc.),2 there is
a functional column structure. These
results suggest that the functional
column is a common structure, and
the basic unit of physiology structure.
The activities of these columns form
a basis for the activities of the entire
cerebral cortex.
In order to deeply understand
the biological significance of columns
and their roles in information
processing, the researchers established
a number of mathematical modeling
studies. The Wilson-Cowan equations
are the most common method to
describe the functional column
in model studies. H.G. Shuster
and others simulated synchronous
oscillation found in the visual
cortex.3 B.H. Jansen et al. proposed
a coupling function column model
that produced EEG type waveforms
and evoked potential. 4 T. Fukai
designed a functional column network
model to simulate the access of visual
design etc. 5 Some other feature
column models describing functional
oscillation activities of the column
include phase column models.
Only a small number of models
are based on the single neuron. E.
Fransen et al. replaced the singlecell in the traditional network with
multi-cellular functional columns
to build an attractor network, and
simulate the working memory.
D. Hansel et al. built a super column
model under the structure of the
direction column of visual cortex
column, studied synchronization and
chaotic characteristics, and explained
the mechanism of the function
column with the direction selection.
The Blue Brain project was launched
in 2005 and aimed to reverse engineer
the mammalian brain from laboratory
data.6 The project now has a software
model of “tens of thousands” of
neurons – each one of which is
different – which has allowed them
to digitally construct an artificial
neocortical column. Henry Markram
who is Director of the Center for
Neuroscience & Technology and
co-Director of Ecole Polytechnique
Fédérale de Lausanne(EPFL)’s Brain
Mind Institute aims to unravel
the blueprint of the neocortical
Volume 13 > Number 9 > 2009
■ 15
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column, chemical imaging and
gene expression. Neurons within a
minicolumn encode similar features,
whereas a hypercolumn denotes a
unit containing a full set of values
for any given set of receptive field
parameters.
Recently IBM received a $4.9
million grant from the Defense
A d va n c e d R e s e a r c h P r o j e c t s
A g e n c y ( DA R PA ) t o l e a d a n
ambitious, cross-disciplinary research
project to create a new computing
platform: electronic circuits that
operate like a brain. Along with
IBM Almaden Research Center and
IBM T. J. Watson Research Center,
Stanford University, University
of Wisconsin-Madison, Cornell
University, Columbia University
Medical Center, and University of
California-Merced are participating
in the project.7
The mind refers to the aspects
of intellect and consciousness
manifested as combinations of
thought, perception, memory,
emotion, will and imagination
including all of the brain’s conscious
and unconscious cognitive processes.
The mind problem is a very
complicated non-linear problem.
We need to study the mind’s world
through the modern scientific
method. The mind model studies
the process of mentality and the
process of mind. But it is not the
traditional science of mentality and
it must seek the scientific proofs of
neurobiology and brain science to
supply the factual basis for mind
problems. The mind model is the
software for an artificial brain.
Intelligence science which facilitates
the cross-fertilization of research
coming from brain science, cognitive
science and artificial intelligence,
is the unique way to create a
brain.8 ■
16 ■ Volume 13 > Number 9 > 2009
Biography
Professor Shi, at the Institute of Computing Technology, Chinese
Academy of Sciences, graduated in computer science from the Graduate
School of University of Science and Technology of China in 1968,
and graduated in computer science from the University of Science
and Technology of China in 1964. From 1968 till 1980 he was with
the Department of Information Storage and Database Systems at the
Institute of Computing Technology, Chinese Academy of Sciences, first
as a research group leader then as vice director. He has spent several
years as a visiting scholar in prestigious institutions such as the Ohio
State University, University of Maryland, Erasmus University Rotterdam,
National University of Singapore and many others.
Professor Shi’s research and teaching interests are in the areas of
intelligence science, distributed intelligence, machine learning, neural
computing and data mining. He has published 11 monographs, 12 books
and more than 400 research papers in journals and conferences. In 1992
he published his monograph Principles of Machine Learning in English.
His most recent monographs are Intelligence Science and Knowledge
Discovery, written in Chinese. He has won a 2nd-Grade National Award
at Science and Technology Progress of China in 2002, and two 2nd-Grade
Awards at Science and Technology Progress of the Chinese Academy of
Sciences in 1998 and 2001, respectively.
Professor Shi is also active in professional activities. He is a senior
member of IEEE, ACM and AAAI member, a Chair for the WG 12.2 of
IFIP. He serves as a Vice President for Chinese Association of Artificial
Intelligence, Executive president of Chinese Neural Network Council.
In 2006 he was selected as Chair or Co-Chairs of Program Committee
for ICCI2006, ICAI2006, PRIMA2006, ASWC2006, ICIIP2006.
References
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Zhongzhi Shi. Intelligence Science. To be published by World Scientific Publishing Co.
Mountcastle, V. B. (1957). Modality and topographic properties of single neurons of cat’s
somatic sensory cortex. J Neurophysiol, 20: 408-434.
Schuster, H G, Wagner P. (1990). 1: A model for neuronal oscillations in the visual cortex.
2: Phase description of the feature dependent synchronization. Biol Cybern, 64: 77~85.
Jansen, B. H., Zouridakis, G., Brandt, M. E. (1993). A neurophysiologically-based
mathematical model of flash visual evoked potentials. Biol Cybern, 68: 275~283.
Fukai T. (1994). A model of cortical memory processing based on columnar organization.
Biol Cybern, 70: 427-434.
Henry Markram. (2006). The Blue Brain Project. Nature Reviews Neuroscience 7, 153-160
(February 2006).
Jennifer LeClaire. (2008). IBM, Partners Aim To Build Brain-Like Computer Systems. Newsfactor.
com, November 21, 2008.
Zhongzhi Shi. On Intelligence Science. To be published in International Journal on Advanced
Intelligence.
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