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Brain Wiring Enables a Turing Machine to Emerge inside the Network

Juyang Weng

Computer Science, Cognitive Science, and Neuroscience
Michigan State University
East Lansing, Michigan USA


At what levels should we understand and model brains? Wiesel and Hubel 1965 demonstrated that cells in the V1 area in a normal kitten selectively respond to the left eye, the right eye, and both eyes; but they respond only to one eye if the other eye is closed from birth. Sur and coworkers 2000 experimentally showed that a pathway amputation early in life of ferrets caused the auditory areas to connect indirectly with retina that the auditory cortex emerged visual representations and the ferrets demonstrated visual capabilities using rewired auditory cortex. Voss 2013 reviewed that the human visual cortex is reassigned to audition and touch in the born blind. These results seem to indicate that we must understand and model how the brain dynamically wire itself through signal activities and a fixed cascade architecture of neural networks is nether correct nor sufficient.

However, how can we understand this process of dynamic rewiring in terms of abstraction, logic, reasoning and emotion? I computationally explain how an emergent and modulated Turing Machine grows and adapt inside a huge brain network through lifetime. Therefore, not only brain experimentalists but also all neural network modelers and developers seem to be able to benefit greatly from such a new theoretical insight.

Short Bio

Juyang (John) Weng is a professor at the Dept. 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, including a book Natural and Artificial Intelligence: Introduction to Computational Brain-Mind. He is an editor-in-chief of International Journal of Humanoid Robotics and an associate editor of the IEEE Trans. on Autonomous Mental Development, and the editor-in-chief of the Brain-Mind Magazine. He is instrumental in the establishment and operation of the Brain-Mind Institute, a nonprofit for cross-disciplinary education and research.  He was an associate editor of IEEE Trans. on Pattern Recognition and Machine Intelligence (2001-2004), an associate editor of IEEE Trans. on Image Processing (1994-1997).  He is a Fellow of IEEE.