Title: Developmental Model for Computational Brain-Minds

Speaker: Juyang Weng, Professor

Date & Time: December 9^th (Mon), 2:30pm

Venue: C505

Abstract: In the disciplines of neuroscience, cognitive science, artificial intelligence and robotics, existing models fall into two major schools, symbolic and emergent (connectionist). The representations used by the emergent school are numeric patterns that emerge automatically but such models do not abstract well. In this talk, I present a brain-scale model DN (Developmental Network) that takes the best of the two schools: Its emergent representations abstract at least as well as the framework of Finite Automata (FA) (and Markov models). It models how a brain network Y optimally serves as a two-way multi-exchange bridge for two types of islands, the sensory islands in X (e.g., images) and the effector islands in Z (e.g., muscles). The effector islands are the open states of an FA. Next, I explain why the framework of FA is general-purpose, going behind the Turing Machine framework, because it not only copes with abstract rules (e.g., syntax) but also meanings (e.g., semantics). Furthermore, neural modulation, such as serotonin, dopamine, acetylcholine, and norepinephrine systems,is critical for motivation (including emotion) integrated into DN in a mathematically well-defined way. I will also present some recent experimental results.

Short bio: 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. 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 over two hundred fifty research articles. He is the author of a new book: Natural and Artificial Intelligence: Introduction to Computational Brain-Mind, available from amazon.com. He is an editor-in-chief of International Journal of Humanoid Robotics and the Brain-Mind Magazine, and an associate editor of the IEEE Transactions on Autonomous Mental Development. He has chaired and co-chaired some conferences, including the NSF/DARPA funded Workshop on Development and Learning 2000 (1st ICDL), 2nd ICDL (2002), 7th ICDL (2008), 8th ICDL (2009), and INNS NNN 2008. He was the chairman of the Governing Board of the International Conferences on Development and Learning (ICDLs) (2005-2007, http://cogsci.ucsd.edu/~triesch/icdl/), 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.