8:00am - 10:00am, August 8, 2015, with INNS Big Data 2015
Slides: PowerPoint Show file.
This tutorial presents introductory material for understanding how a biological brain deals with big data --- how it attends and how it learns, especially the biological and computational underpinnings. The questions to be discussed include: Why and how is the brain learning incremental? How does a brain update itself from its autonomous activities in its physical environment? What is the computational architecture that enables a brain to learn and update incrementally? What are the working memory, short-term memory, and long-term memory in such architecture? What is attention? How does a brain learn directly from cluttered environment? What is intent? How does a brain have intents? What does it mean by a general-purpose learner? Is the framework of finite automata general-purpose and in what sense? What roles does the framework of automata theory play in understanding brain learning? Furthermore, brain modulation, such as serotonin, dopamine, acetylcholine, and norepinephrine systems, is discussed to explain how the brain learns motivation (including emotion). Some recent experimental results for vision and language acquisition will be presented as examples. Many machine-learning researchers are interested in learning how the brain learns.
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
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.
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 his BS degree from Fudan University in 1982, his MS and PhD degrees from University of Illinois at Urbana-Champaign, 1985 and 1989, respectively, all in Computer Science. From August 2006 to May 2007, he was also a visiting professor at the Department of Brain and Cognitive Science of MIT. He has also been a visiting professor at Fudan University since 2004. His research interests include computational biology, computational neuroscience, computational developmental psychology, biologically inspired systems, computer vision, audition, touch, behaviors, and intelligent robots. He is the author or coauthor of about three hundred research articles. He is an editor-in-chief of International Journal of Humanoid Robotics, the editor-in-chief of the Brain-Mind Magazine, and an associate editor of the new IEEE Transactions on Autonomous Mental Development. He was the founding Chairman of the Governing Board of the International Conferences on Development and Learning (ICDLs) (2005-2007, http://cogsci.ucsd.edu/~triesch/icdl/), founding chairman of the Autonomous Mental Development Technical Committee of the IEEE Computational Intelligence Society (2004-2005), an associate editor of IEEE Trans. on Pattern Recognition and Machine Intelligence, an associate editor of IEEE Trans. on Image Processing. He is a Fellow of IEEE.
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.
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, 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, “Developmental Robotics: Theory and Experiments,” International Journal of Humanoid Robotics, vol. 1, no. 2, pp. 199-235, 2004.
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 and M. Luciw, “Dually Optimal Neuronal Layers: Lobe Component Analysis,” IEEE Transactions on Autonomous Mental Development, vol. 1, no. 1, pp. 68-85, 2009.
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, "Why Have We Passed `Neural Networks Do not Abstract Well'?'', Natural Intelligence: the INNS Magazine, vol. 1, no.1, pp. 13-22, 2011. PDF file.
J. Weng, "Symbolic Models and Emergent Models: A Review," IEEE Transactions on Autonomous Mental Development, vol. 4, no. 1, pp. 29-53, 2012. PDF file.
J. Weng and M. Luciw, "Brain-Like Emergent Spatial Processing," IEEE transactions on Autonomous Mental Development, vol. 4, no. 2, pp. 161-185, 2012. PDF file.
J. Weng,"Brains as Naturally Emerging Turing Machines,"' in Proc. International Joint Conference on Neural Networks, Killarney, Ireland, pp. +1-8, July 12-17. 2015. PDF file.
Q. Guo, X. Wu, and J. Weng, "Cross-Domain and Within-Domain Synaptic Maintenance for Autonomous
Development of Visual Areas,'' in Proc. the Fifth Joint IEEE International Conference on Development and Learning and on EpiGenetic Robotics, Providence, RI, pp. +1-6, August 13-16, 2015. PDF file.
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