Computational Cognitive Neuroscience

1. Computational Cognitive Neuroscience Shyh-Kang Jeng Department of Electrical Engineering/ Graduate Institute of Communication/ Graduate Institute of Networking and Multimedia

2. Artificial Intelligence http://www.research.ibm.com/deepblue/meet/html/d.1.shtml http://www.research.ibm.com/deepblue/press/html/g.6.6.shtml 羅仁權, 再造一個青年愛因斯坦, 台大科學創造新文明特展, 2011 http://www.takanishi.mech.waseda.ac.jp/top/research/music/flute/wf_4rv/index_j.htm

3. Jeff Hawkins’s Comments on Artificial Intelligence • AI defenders … a program that produces outputs resembling (or surpassing) human performance on a task in some narrow but useful way really is just as good as the way our brains do it • …this kind of ends-justify-the-means interpretation of functionalism leads AI researchers astray J. Hawkins, On Intelligence, Times Books, 2004

4. Artificial Neural Networks R. O. Duda, P. E. Harr, and D. G. Stork, Pattern Classification, 2nd ed., John Wiley & Sons, 2001

5. Jeff Hawkins’s Comments on Artificial Neural Networks • Connectionists intuitively felt the brain wasn’t a computer and that its secrets lie in how neurons behave when connected together • That was a good start, but the field barely moved on from its early successes • Research on cortically realistic networks was, and remains, rare

6. Jeff Hawkins’s Comments on Intelligence • Since intelligence is an internal property of a brain, we have to look inside the brain to understand what intelligence is • To succeed, we will need to crib heavily from nature’s engine of intelligence, the neocortex • No other roads will get us there

7. Cognitive Neuroscience • To understand how neural processes give rise to cognition • Perception, attention, language, memory, problem solving, planning, reasoning, coordination and execution of action • “Cognitive neuroscience – with its concern about perception, action, memory, language, and selective attention – will increasingly come to represent the central focus of all neurosciences in the twenty-first century.”

8. Experimental Methodologies • fMRI and other imaging modalities • Neural basis of cognition in human • Multi-electrode arrays • Record from many separate neurons at a time • Insight into representation of information http://www.csulb.edu/~cwallis/482/fmri/fmri.h2.gif http://paulbourke.net/oldstuff/eeg/eeg2.jpeg http://en.wikibooks.org/wiki/File:Sleep_EEG_Stage_1.jpg

9. Other Major Research Methods • Processes occurring in individuals with disorders • Helpful to understand the “normal” case • Animal models are also often used • Conscious experience • Subject to scientific scrutiny through observables • Including verbal reports or other readout methods • Brief interval of time or longer periods of time

10. Different Mechanistic Goals • Some focus on partitioning the brain into distinct modules with isolable functions • Some try to find detailed characterization of actual physical and chemical processes • Some look for something more general • Not the details themselves that matter • Principles that are embodied in these details are more important

11. Two-Route Model for Reading http://en.wikibooks.org/wiki/File:1_1_twoRouteModelInReading.JPG

12. Computational Cognitive Neuroscience • Understanding how the brain embodies the mind, using biologically based computational models made up of networks of neuron-like units • Intersection of many disciplines • Neuroscience • Cognitive psychology • Computation

13. Computational Model for Reading Randall C. O’Reilly and Yuko Munakata, Computational Explorations in Cognitive Neuroscience: Understanding the Mind by Simulating the Brain, MIT Press, 2000 Randall C. O’Reilly and Yuko Munakata, Computational Explorations in Cognitive Neuroscience: Understanding the Mind by Simulating the Brain, MIT Press, 2000 http://www.lps.uci.edu/~johnsonk/CLASSES/philpsych/brain.jpg

14. Usefulness of Models • Work through in detail of proposed modular mechanism • Lead to • explicit predictions that can be compared for an adequate account • exploration of what postulates imply about resulting behaviors

15. Course Outline • Introduction and Overview I. Basic Neural Computational Mechanisms • Individual Neurons • Networks of Neurons • Hebbian Model Learning • Error-Driven Task Learning • Combined Model and Task Learning

16. Course Outline II. Large-Scale Brain Area Organization and Cognitive Phenomena • Large-Scale Brain Area Functional Organization • Perception and Attention • Memory • Language • High-Level Cognition

17. Textbook and Website • Randall C. O’Reilly and Yuko Munakata, Computational Explorations in Cognitive Neuroscience: Understanding the Mind by Simulating the Brain, MIT Press, 2000. • http://cc.ee.ntu.edu.tw/~skjeng/CCN2011.htm

18. Software Emergent • For practicing examples in the textbook and doing homeworks as well as the term project • Enhanced from PDP++ • Downloadable from http://grey.colorado.edu/emergent/index.php/ Main_Page http://grey.colorado.edu/emergent/index.php/File:Screenshot_ax_tutorial.png

19. References • Thomas J. Anastasio, Tutorial on Neural Systems Modeling, Sinauer Associates Inc. Publishers, 2010 • Bernard J. Baars and Nicole M. Gage, Cognition, Brain, and Consciousness:Introduction to Cognitive Neuroscience, 2nd ed., Academic Press, 2010

20. References • Friedemann Pulvermuller, The Neuroscience of Language, Cambridge University Press, 2002 • Douglas Medin, Brian H. Ross, Arthur B. Markman, Cognitive Psychology, 4th ed,. Wiley, 2004

21. References • Patricia Churchland and Terrence J. Sejnowski, The Computational Brain (Computational Neuroscience), MIT Press, 1994 • Peter Dayan and L. F. Abbott, Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems, MIT Press, 2005

22. References • J. Hawkins, On Intelligence, Times Books, 2004