Basic Theories for Neuroinformatics and ... - Semantic Scholar
Basic Theories for Neuroinformatics and Neurocomputing Yingxu Wang President, International Institute of Cognitive Informatics and Cognitive Computing (ICIC) Director, Laboratory for Cognitive Informatics and Cognitive Computing Dept. of Electrical and Computer Engineering, Schulich School of Engineering The University of Calgary 2500 University Drive, NW, Calgary, Alberta, Canada T2N 1N4 Email: [email protected] ABSTRACT
The logical model of t he brain and the abstract intelligence theory of natural intelligence will en able the development of cognitive computers that perceive, thi nk and learn. The functional and theoretical difference between cognitive computers and classic co mputers are t hat the latter are data processors based on Boolean algebra and its logical counterparts; while the former are knowledge processors based on contemporary denotational mathematics. A wi de range of applications of co gnitive computers have been developing in ICIC [http://www.ucalgary.ca/icic/] and my laboratory such as, inter alia, cognitive robots, cognitive learning engines, cognitive Internet, cognitive agents, cognitive search engines, cognitive translators, cognitive control systems, and cognitive automobiles.
A fundamental challenge for almost all scientific disciplines is to explain how natural intelligence is g enerated by physiological organs and what the logical model of the brain is beyond its neu ral architectures. According to cognitive informatics and abstract in telligence, the exploration of the brain is a complicated recursive problem where contemporary denotational mathematics is needed to efficiently deal with it. Cognitive psychology and medical science were u sed to explain that t he brain works in a ce rtain way based on empirical observations on related activ ities in usually overlapped brain areas. However, the lack of precise models and rigorous causality in brain studies has dissatisfied the formal expectations of researchers in computational intelligence and mathematics, because a computer, the logical counterpart of the brain, might not be explained in such a vague and empirical approach without the support of formal models and rigorous means. In order to formally explain the archi tectures and functions of the b rain, as well as their intricate relations and interactions, systematic models of t he brain are s ought for revealing the principles and mechanisms of the brain at the neural, physiological, cognitive, and logical (abstract) levels. Cognitive and brain informatics investigate into the brain via not only inductive syntheses through these four cognitive levels from the bottom up in order to form theories based on empirical observations, but also deductive analyses from the top down in order to explain various functional and behavioral instances according to the abstract intelligence theory. This keynote lecture presents systematic models of th e brain from the facets of cognitive informatics, abstract intelligence, brain Informatics, neuroinformatics, and cognitive psychology. A logical model of t he brain is introduced that maps the cognitive functions of the brain onto its neural and physiological architectures. This work leads to a coherent abstract intelligence theory based on both denotational mathematical models and cognitive psychology observations, which rigorously explains the underpinning principles and mechanisms of the brain. On the basis of t he abstract intelligence theories and the logical models of the brain, a comprehensive set of cognitive behaviors as identified in the Layered Reference Model of the Brain (LRMB) such as perception, inference and learning can be rigorously explained and simulated. Proc. 12th IEEE Int. Conf. on Cognitive Informatics & Cognitive Computing (ICCI*CC’13) D.F. Hsu, Y. Wang, A.R. Rao, D. Zhang, W. Kinsner, W. Pedrycz, R.C. Berwick & L.A. Zadeh (Eds.) 978-1-4799-0783-0/13/$31.00 ©2013 IEEE
Keywords: Cognitive informatics, cognitive computing, abstract intelligence, denotational mathematics, cognitive computers, knowledge processors, inference engines, learning engines, perception engines, computational intelligence, applications ABOUT THE KEYNOTE SPEAKER Yingxu Wang is pr ofessor of cognitive informatics an d software science, President of International Institute of Cognitive Informatics and C ognitive C omputing (ICIC, www.ucalgary.ca/icic/), Director of Laboratory for Cognitive Informatics and Cognitive Computing, and of Laboratory for Denotational Mathematics and Software Science at the University of Calgary. He is a Fellow of WIF (UK), a Fellow of ICIC, a P.Eng of Canada, and a Senior Member of IEEE and ACM. He receive d a PhD in Software Engineering from the Nottingham Trent University, UK, and a BSc in Electrical Engineering from Shanghai Tiedao University. He has industrial experi ence since 1972 and has been a full professor since 1994. He was a visiting professor on sabbatical leaves at Oxford University (1995), Stanford University (2008), University of California, Berkeley (2008), and M IT (2012), respectively. He is the
3
founder and steering committee chair of the annual IE EE International Conference on Cognitive Informatics and Cognitive Computing (ICCI*CC). He is fo unding Editor-inChief of International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), founding Editor-in-Chief of International Journal of Software Science and Computational Intelligence (IJSSCI), Associate Editor of IEEE Trans on System, Man, and Cybernetics - Systems, and Editor-in-Chief of Journal of Advanced Mathematics and Applications. Dr. Wang is the initiator of a few cutting-edge research fields such as cognitive informatics (CI, the t heoretical framework of CI, neuroinformatics, the logical model of the brain (LMB), the lay ered reference model of the brain (LRMB), the cognitive model of brain informatics (CMBI), the mathematical model of consciousness, and the cognitive learning engine (CLE)); abstract intelligence; cognitive computing (such as co gnitive computers, cognitive robots, cognitive agents, and the cognitive Internet); denotational mathematics (i.e., concept algebra, inference algebra, semantic algebra, behavioral process algebra, system algebra, granular algebra, and visual semantic algebra); software science (on unified mathematical models and laws of software, cognitive complexity of software, automatic code generators, the coordinative work organization t heory, and built-in tests (BITs)); basic studies in cognitive linguistics (such as the cognitive linguistic framework of languages, semantic algenra, formal semantics of languages, deductive grammar of English, and the cognitive complexity of text comprehension). He has published over 130 peer reviewed journal papers, 220+ peer reviewed conference papers, and 18 books in cognitive informatics, cognitive computing, software science, denotational mathematics, and computational intelligence. He is the recipie nt of dozens international awards on academic leadership, outstanding contributions, research achievement, best papers, and teaching in the last three decades.
[8]
[9] [10] [11] [12]
[13] [14] [15]
[16]
[17]
[18] REFERENCES [1] [2] [3] [4] [5] [6] [7]
[19]
Anderson, J.R. (1983), The Architecture of Cognition, Harvard Univ. Press, Cambridge, MA. Bender, E.A. (1996), Mathematical Methods in Artificial Intelligence, IEEE CS Press, Los Alamitos, CA. Boole, G. (1854), The Laws of Thought, Prometheus Books, NY., Reprint in 2003. Leahey, T.H. (1997), A History of Psychology: Main Currents in Psychological Thought, 4th ed., PrenticeHall, Upper Saddle River, NJ. Newton, I. (1687), The Mathematical Principles of Natural Philosophy, 1st ed. in Latin, 1687, English ed., 1729. Russel, B. (19 03). The Principles of Mathematics, George Allen & Unwin, London. Tian, Y., Y. Wang, M.L. Gavrilova, and G. Ruhe (2011), A Formal Knowledge Representation System (FKRS) for the Intelligent Knowledge Base o f a Cognitive Learning Engine, Int’l Journal of Software Science and Computational Intelligence, 3(4), 1-17.
[20]
[21]
[22]
4
Wang, Y. (2002), Keynote: On Cognitive Informatics, Proc. 1st IEEE International Conference on Cognitive Informatics (ICCI’02), Calgary, Canada, IEEE CS Press, August, pp. 34-42. Wang, Y. (2 003), On Cognitive Informatics, Brain and Mind: A Transdisciplinary Journal of Neuroscience and Neurophilosophy, USA, August, 4(3), 151-167. Wang, Y. ( 2007), The T heoretical Framework of Cognitive Informatics, International Journal of Cognitive Informatics and Natural Intelligence, 1(1), 1-27. Wang, Y. ( 2008), On Contemporary Denotational Mathematics for Computational Intelligence, Transactions of Computational Science, 2, 6-29. Wang, Y. (2009), On Abstract Intelligence: Toward a Unified Theory of Natural, Artificial, Machinable, and Computational Intelligence, Int’l Journal of Software Science and Computational Intelligence, 1(1), 1-18. Wang, Y. (2009), On Cognitive Computing, International Journal of Software Science and Computational Intelligence, 1(3), 1-15. Wang, Y. (2010), Cognitive Robots: A Reference Model towards Intelligent Authentication, IEEE Robotics and Automation, 17(4), 54-62. Wang, Y. (2011), Inference Algebra (IA): A Denotational Mathematics for Cognitive Computing and Machine Reasoning (I), Int’l Journal of Cognitive Informatics and Natural Intelligence, 5(4), 62-83. Wang, Y. (2012), Keynote: Towards the Next Generation of Co gnitive Computers: Knowledge vs. Data Computers, Proceedings of 12th International Conference on Computational Science and Applications (ICCSA'12), Salvador, Brazil, Springer, June 18-21. Wang, Y., Y. Wang, S. Patel, and D. Patel (2006), A Layered Reference Model of the Brain (LRMB), IEEE Trans. on Systems, Man, and Cybernetics (Part C), 36(2), 124-133. Wang, Y. and Y. Wang: (2006), Cognitive Informatics Models of the Brain, IEEE Trans. on Systems, Man, and Cybernetics (Part C), 36(2), 203-207. Wang, Y., W. Kinsner, and D. Zhang (2009), Contemporary Cybernetics and its Faces of Cognitive Informatics and Computational Intelligence, IEEE Trans. on System, Man, and Cybernetics (Part B), 39(4), 1-11. Wang, Y., L.A. Zadeh, and Y. Yao (2009), On the System Algebra Foundations for Granular Computing, International Journal of Software Science and Computational Intelligence, 1(1), 1-17. Wang, Y., W. Kinsner, J.A. Anderson, D. Zhang, Y. Yao, P. Sheu, J. Tsai, W. Pedrycz, J.-C. Latombe, L.A. Zadeh, D. Pat el, and C . Chan (2009), A Doctrine of Cognitive Informatics, Fundamenta Informatica, 90(3), 203-228. Wang, Y., Y. Tian and K. Hu (2011), Semantic Manipulations and F ormal Ontology for Machine Learning based on Concept Algebra, International Journal of Cognitive Informatics and Natural Intelligence, 5(3), 1-29.
The logical model of t he brain and the abstract intelligence theory of natural intelligence will en able the development of cognitive computers that perceive, thi nk and learn. The functional and theoretical difference between cognitive computers and classic co mputers are t hat the latter are data processors based on Boolean algebra and its logical counterparts; while the former are knowledge processors based on contemporary denotational mathematics. A wi de range of applications of co gnitive computers have been developing in ICIC [http://www.ucalgary.ca/icic/] and my laboratory such as, inter alia, cognitive robots, cognitive learning engines, cognitive Internet, cognitive agents, cognitive search engines, cognitive translators, cognitive control systems, and cognitive automobiles.
A fundamental challenge for almost all scientific disciplines is to explain how natural intelligence is g enerated by physiological organs and what the logical model of the brain is beyond its neu ral architectures. According to cognitive informatics and abstract in telligence, the exploration of the brain is a complicated recursive problem where contemporary denotational mathematics is needed to efficiently deal with it. Cognitive psychology and medical science were u sed to explain that t he brain works in a ce rtain way based on empirical observations on related activ ities in usually overlapped brain areas. However, the lack of precise models and rigorous causality in brain studies has dissatisfied the formal expectations of researchers in computational intelligence and mathematics, because a computer, the logical counterpart of the brain, might not be explained in such a vague and empirical approach without the support of formal models and rigorous means. In order to formally explain the archi tectures and functions of the b rain, as well as their intricate relations and interactions, systematic models of t he brain are s ought for revealing the principles and mechanisms of the brain at the neural, physiological, cognitive, and logical (abstract) levels. Cognitive and brain informatics investigate into the brain via not only inductive syntheses through these four cognitive levels from the bottom up in order to form theories based on empirical observations, but also deductive analyses from the top down in order to explain various functional and behavioral instances according to the abstract intelligence theory. This keynote lecture presents systematic models of th e brain from the facets of cognitive informatics, abstract intelligence, brain Informatics, neuroinformatics, and cognitive psychology. A logical model of t he brain is introduced that maps the cognitive functions of the brain onto its neural and physiological architectures. This work leads to a coherent abstract intelligence theory based on both denotational mathematical models and cognitive psychology observations, which rigorously explains the underpinning principles and mechanisms of the brain. On the basis of t he abstract intelligence theories and the logical models of the brain, a comprehensive set of cognitive behaviors as identified in the Layered Reference Model of the Brain (LRMB) such as perception, inference and learning can be rigorously explained and simulated. Proc. 12th IEEE Int. Conf. on Cognitive Informatics & Cognitive Computing (ICCI*CC’13) D.F. Hsu, Y. Wang, A.R. Rao, D. Zhang, W. Kinsner, W. Pedrycz, R.C. Berwick & L.A. Zadeh (Eds.) 978-1-4799-0783-0/13/$31.00 ©2013 IEEE
Keywords: Cognitive informatics, cognitive computing, abstract intelligence, denotational mathematics, cognitive computers, knowledge processors, inference engines, learning engines, perception engines, computational intelligence, applications ABOUT THE KEYNOTE SPEAKER Yingxu Wang is pr ofessor of cognitive informatics an d software science, President of International Institute of Cognitive Informatics and C ognitive C omputing (ICIC, www.ucalgary.ca/icic/), Director of Laboratory for Cognitive Informatics and Cognitive Computing, and of Laboratory for Denotational Mathematics and Software Science at the University of Calgary. He is a Fellow of WIF (UK), a Fellow of ICIC, a P.Eng of Canada, and a Senior Member of IEEE and ACM. He receive d a PhD in Software Engineering from the Nottingham Trent University, UK, and a BSc in Electrical Engineering from Shanghai Tiedao University. He has industrial experi ence since 1972 and has been a full professor since 1994. He was a visiting professor on sabbatical leaves at Oxford University (1995), Stanford University (2008), University of California, Berkeley (2008), and M IT (2012), respectively. He is the
3
founder and steering committee chair of the annual IE EE International Conference on Cognitive Informatics and Cognitive Computing (ICCI*CC). He is fo unding Editor-inChief of International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), founding Editor-in-Chief of International Journal of Software Science and Computational Intelligence (IJSSCI), Associate Editor of IEEE Trans on System, Man, and Cybernetics - Systems, and Editor-in-Chief of Journal of Advanced Mathematics and Applications. Dr. Wang is the initiator of a few cutting-edge research fields such as cognitive informatics (CI, the t heoretical framework of CI, neuroinformatics, the logical model of the brain (LMB), the lay ered reference model of the brain (LRMB), the cognitive model of brain informatics (CMBI), the mathematical model of consciousness, and the cognitive learning engine (CLE)); abstract intelligence; cognitive computing (such as co gnitive computers, cognitive robots, cognitive agents, and the cognitive Internet); denotational mathematics (i.e., concept algebra, inference algebra, semantic algebra, behavioral process algebra, system algebra, granular algebra, and visual semantic algebra); software science (on unified mathematical models and laws of software, cognitive complexity of software, automatic code generators, the coordinative work organization t heory, and built-in tests (BITs)); basic studies in cognitive linguistics (such as the cognitive linguistic framework of languages, semantic algenra, formal semantics of languages, deductive grammar of English, and the cognitive complexity of text comprehension). He has published over 130 peer reviewed journal papers, 220+ peer reviewed conference papers, and 18 books in cognitive informatics, cognitive computing, software science, denotational mathematics, and computational intelligence. He is the recipie nt of dozens international awards on academic leadership, outstanding contributions, research achievement, best papers, and teaching in the last three decades.
[8]
[9] [10] [11] [12]
[13] [14] [15]
[16]
[17]
[18] REFERENCES [1] [2] [3] [4] [5] [6] [7]
[19]
Anderson, J.R. (1983), The Architecture of Cognition, Harvard Univ. Press, Cambridge, MA. Bender, E.A. (1996), Mathematical Methods in Artificial Intelligence, IEEE CS Press, Los Alamitos, CA. Boole, G. (1854), The Laws of Thought, Prometheus Books, NY., Reprint in 2003. Leahey, T.H. (1997), A History of Psychology: Main Currents in Psychological Thought, 4th ed., PrenticeHall, Upper Saddle River, NJ. Newton, I. (1687), The Mathematical Principles of Natural Philosophy, 1st ed. in Latin, 1687, English ed., 1729. Russel, B. (19 03). The Principles of Mathematics, George Allen & Unwin, London. Tian, Y., Y. Wang, M.L. Gavrilova, and G. Ruhe (2011), A Formal Knowledge Representation System (FKRS) for the Intelligent Knowledge Base o f a Cognitive Learning Engine, Int’l Journal of Software Science and Computational Intelligence, 3(4), 1-17.
[20]
[21]
[22]
4
Wang, Y. (2002), Keynote: On Cognitive Informatics, Proc. 1st IEEE International Conference on Cognitive Informatics (ICCI’02), Calgary, Canada, IEEE CS Press, August, pp. 34-42. Wang, Y. (2 003), On Cognitive Informatics, Brain and Mind: A Transdisciplinary Journal of Neuroscience and Neurophilosophy, USA, August, 4(3), 151-167. Wang, Y. ( 2007), The T heoretical Framework of Cognitive Informatics, International Journal of Cognitive Informatics and Natural Intelligence, 1(1), 1-27. Wang, Y. ( 2008), On Contemporary Denotational Mathematics for Computational Intelligence, Transactions of Computational Science, 2, 6-29. Wang, Y. (2009), On Abstract Intelligence: Toward a Unified Theory of Natural, Artificial, Machinable, and Computational Intelligence, Int’l Journal of Software Science and Computational Intelligence, 1(1), 1-18. Wang, Y. (2009), On Cognitive Computing, International Journal of Software Science and Computational Intelligence, 1(3), 1-15. Wang, Y. (2010), Cognitive Robots: A Reference Model towards Intelligent Authentication, IEEE Robotics and Automation, 17(4), 54-62. Wang, Y. (2011), Inference Algebra (IA): A Denotational Mathematics for Cognitive Computing and Machine Reasoning (I), Int’l Journal of Cognitive Informatics and Natural Intelligence, 5(4), 62-83. Wang, Y. (2012), Keynote: Towards the Next Generation of Co gnitive Computers: Knowledge vs. Data Computers, Proceedings of 12th International Conference on Computational Science and Applications (ICCSA'12), Salvador, Brazil, Springer, June 18-21. Wang, Y., Y. Wang, S. Patel, and D. Patel (2006), A Layered Reference Model of the Brain (LRMB), IEEE Trans. on Systems, Man, and Cybernetics (Part C), 36(2), 124-133. Wang, Y. and Y. Wang: (2006), Cognitive Informatics Models of the Brain, IEEE Trans. on Systems, Man, and Cybernetics (Part C), 36(2), 203-207. Wang, Y., W. Kinsner, and D. Zhang (2009), Contemporary Cybernetics and its Faces of Cognitive Informatics and Computational Intelligence, IEEE Trans. on System, Man, and Cybernetics (Part B), 39(4), 1-11. Wang, Y., L.A. Zadeh, and Y. Yao (2009), On the System Algebra Foundations for Granular Computing, International Journal of Software Science and Computational Intelligence, 1(1), 1-17. Wang, Y., W. Kinsner, J.A. Anderson, D. Zhang, Y. Yao, P. Sheu, J. Tsai, W. Pedrycz, J.-C. Latombe, L.A. Zadeh, D. Pat el, and C . Chan (2009), A Doctrine of Cognitive Informatics, Fundamenta Informatica, 90(3), 203-228. Wang, Y., Y. Tian and K. Hu (2011), Semantic Manipulations and F ormal Ontology for Machine Learning based on Concept Algebra, International Journal of Cognitive Informatics and Natural Intelligence, 5(3), 1-29.
