Tutorial for IJCNN 2013
Sunday, August 4, 2013
8:00am - 9:45am
The mind is what the brain does. This tutorial presents introductory material for mapping a mind model to the corresponding brain, assisting understand the biological human brain and the mind arising from it, especially the computational underpinnings. That is why the words “Brain-Mind” are hyphenated in the title of this tutorial.
This material strives to unify natural intelligence with artificial intelligence. It approaches intelligence through not only what intelligence is but also how intelligence arises.
Examples of disciplinary questions related to the material to be presented:
Biology: How does each autonomous cell interact with the environment to give rise to animal behaviors, and what cellular roles is the genome likely to play?
Neuroscience: From an overarching perspective, how does a brain self-wire, perform top-down attention, and develop its functions?
Psychology: How does an integrated brain architecture accomplish multiple psychological learning models and develop brain’s external behaviors?
Computer Science: How does a brain-like network compute, adapt, reason, and generalize, and how is the automaton theory related to the brain-like network?
Electrical Engineering: How does a brain-like network perform general-purpose, nonlinear, feedback sensing-and-control, beyond traditional nonlinear control?
Mathematics: How does a brain-like network perform general-purpose, nonlinear optimization, and how does a brain realize emergent functionals?
Physics: How do meanings arise from physics, and how does a brain-like network treat space and time in a unified way, reminiscent of relativity?
Social sciences: How do computational principles of human brains provide insight into possible solutions to a variety of social and political problems?
The theory, mechanisms and algorithms are presented with related experimental results.
Juyang (John) Weng, Professor
Embodied Intelligence Laboratory
Department of Computer Science and Engineering,
Cognitive Science Program, and Neuroscience Program
Michigan State University
East Lansing, MI 48824 USA
Tel: 517-353-4388, 617-253-8024
Length: 1.75 hours
Prerequisites: General ideas about computer programs, basic knowledge about vectors.
Audience:researchers and governmental scientific officials in biology, neuroscience, psychology (including cognitive science), signal processing, image processing, computer vision, pattern recognition, speech recognition, autonomous control, language processing, autonomous robots, human-machine interface, and artificial intelligence.
Handout: the IJCNN 2013 organizing committee will provide tutorial material.
Juyang (John) Weng is a professor at the Department of Computer Science and Engineering, the Cognitive Science Program, and the Neuroscience Program, Michigan State University, East Lansing, Michigan, USA. He received the BS degree in computer science from Fudan University, Shanghai, China, in 1982, and M. Sc. and PhD degrees in computer science from the University of Illinois at Urbana-Champaign, in 1985 and 1989, respectively. Since the work of Cresceptron (ICCV 1993), he expanded his research interests in biologically inspired systems, especially the autonomous development of a variety of mental capabilities by robots and animals, including perception, cognition, behaviors, motivation, and abstract reasoning skills. He has published over 250 research articles on related subjects, including task muddiness, intelligence metrics, mental architectures, vision, audition, touch, attention, recognition, autonomous navigation, natural language understanding, and other emergent behaviors. He coauthored with T. S. Huang and N. Ahuja a research monograph titled Motion and Structure from Image Sequences and authored a book titled Natural and Artificial Intelligence: Computational Introduction to Computational Brain-Mind.
Dr. Weng is an Editor-in-Chief of the International Journal of Humanoid Robotics, the Editor-in-Chief of the Brain-Mind Magazine, and an associate editor of the IEEE Transactions on Autonomous Mental Development. He was a Program Chairman of the NSF/DARPA funded Workshop on Development and Learning 2000 (1st ICDL), a Program Chairman of the 2nd ICDL (2002), the chairman of the Autonomous Mental Development Technical Committee of the IEEE Computational Intelligence Society (2004-2005), the Chairman of the Governing Board of the International Conferences on Development and Learning (ICDLs) (2005-2007), a General Chairman of 7th ICDL (2008), the General Chairman of 8th ICDL (2009), an associate editor of the IEEE Transactions on Pattern Recognition and Machine Intelligence, and an associate editor of the IEEE Transactions on Image Processing.
J. Weng, Natural and Artificial Intelligence: Introduction to Computational Brain-Mind, BMI Press, 2012.
J. Weng and S. Paslaski and J. Daly and C. VanDam and J. Brown, "Modulation for Emergent Networks: Serotonin and Dopamine,'' Neural Networks, vol. 41, pp. 225-239, 2013.
J. Weng, “Symbolic Models and Emergent Models: A Review,'' IEEE Transactions on Autonomous Mental Development,vol. 4., no. 1, pp. 29-53, 2012.
J. Weng, “Three Theorems: Brain-Like Networks Logically Reason and Optimally Generalize,” in 2011 Int'l Joint Conference on Neural Networks, San Jose, California, pp. 2983-2990, July 31 - August 5, 2011.
J. Weng, “A 5-Chunk Developmental Brain-Mind Network Model for Multiple Events in Complex Backgrounds,'' July 18-23, Barcelona, Spain, pp. 1-8, 2010.
Y. Zhang and J. Weng, “Spatiotemporal Multimodal Developmental Learning,” IEEE Transactions on Autonomous Mental Development, vol. 2, no. 3, pp. 149-166, 2010.
M. Luciw and J. Weng, “Where What Network 3: Developmental Top-Down Attention with Multiple Meaningful Foregrounds,” in Proc. International Joint Conference on Neural Networks, Barcelona, Spain, pp. 4233-4240, July 18-23, 2010.
K. Miyan and J. Weng, “WWN-Text: Cortex-Like Language Acquisition with ’What’ and ’Where’,” in Proc. IEEE 9th International Conference on Development and Learning,'' Ann Arbor, pp. 280-285, August 18-21, 2010.
J. Weng, “Task Muddiness, Performance Metrics and the Necessity of Autonomous Mental Development,” Minds and Machines, vol. 19, pp. 93-115, 2009.
J. Weng and M. Luciw, “Dually Optimal Neuronal Layers: Lobe Component Analysis,” IEEE Transactions on Autonomous Mental Development, vol. 1, no. 1, pp. 68-85, 2009.
J. Weng, T. Luwang, H. Lu, X. Xue, “Multilayer In-place Learning Networks for Modeling Functional Layers in the Laminar Cortex,” Neural Networks, vol. 21, no.2-3, pp. 150-159, 2008.
Z. Ji, J. Weng and D. Prokhorov, “Where-What Network 1: ‘Where’ and ‘What’ Assist Each Other Through Top-down Connections,” IEEE International Conference on Development and Learning, Monterey, CA, pp. 61-66, Aug. 9-12, 2008.
J. Weng, “On Developmental Mental Architectures,” Neurocomputing, vol. 70, no. 13-15, pp. 2303-2323, 2007.
Y. Zhang and J. Weng, “Task Transfer by a Developmental Robot,” IEEE Transactions on Evolutionary Computation, vol. 11, no. 2, pp. 226-248, 2007.
X. Huang and J.Weng, “Inherent Value Systems for Autonomous Mental Development,” International Journal of Humanoid Robotics, vol. 4, no. 2, pp. 407-433, 2007.
J. Weng and W. Hwang, “From Neural Networks to the Brain: Autonomous Mental Development,” IEEE Computational Intelligence Magazine, vol. 1, no. 3, pp. 15-31, 2006.
J. Weng, “Developmental Robotics: Theory and Experiments,” International Journal of Humanoid Robotics, vol. 1, no. 2, pp. 199-235, 2004.
J. Weng, Y. Zhang and W. Hwang, “Candid Covariance-free Incremental Principal Component Analysis,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 25, no. 8, pp. 1034-1040, 2003.
J. Weng and I. Stockman, “Autonomous Mental Development: Workshop on Development and Learning,” AI Magazine, vol. 23, no. 2, pp. 95-98, 2002.
J. Weng, J. McClelland, A. Pentland, O. Sporns, I. Stockman, M. Sur and E. Thelen, “Autonomous Mental Development by Robots and Animals,” Science, vol. 291, no. 5504, pp. 599-600, Jan. 26, 2001.
W. S. Hwang and J. Weng, “Hierarchical Discriminant Regression,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 11, pp. 1277 - 1293, Nov. 2000.
D. L. Swets and J. Weng, “Hierarchical discriminant analysis for image retrieval,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 21, no. 5, pp. 386 - 401, May 1999.
J. Weng, N. Ahuja and T. S. Huang, “Learning recognition and segmentation using the Cresceptron,” International Journal of Computer Vision, vol. 25, no. 2, pp. 105-139, Nov. 1997.
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