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[ Feature ] 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 www.asiabiotech.com 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 [ Feature ] 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 1. 2. 3. 4. 5. 6. 7. 8. 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. www.asiabiotech.com