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

Computational Cognitive Neuroscience

Shyh-Kang JengDepartment of Electrical Engineering/

Graduate Institute of Communication/

Graduate Institute of Networking and Multimedia

Artificial Intelligence

http://www.takanishi.mech.waseda.ac.jp/top/research/music/flute/wf_4rv/index_j.htm

http://www.research.ibm.com/deepblue/meet/html/d.1.shtmlhttp://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

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

Artificial Neural Networks

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

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

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

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.”

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://paulbourke.net/oldstuff/eeg/eeg2.jpeg http://en.wikibooks.org/wiki/File:Sleep_EEG_Stage_1.jpg

http://www.csulb.edu/~cwallis/482/fmri/fmri.h2.gif

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

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

Two-Route Model for Reading

http://en.wikibooks.org/wiki/File:1_1_twoRouteModelInReading.JPG

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

Computational Model for Reading

http://www.lps.uci.edu/~johnsonk/CLASSES/philpsych/brain.jpgRandall 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

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

Course Outline1. Introduction and Overview

I. Basic Neural Computational Mechanisms

2. Individual Neurons

3. Networks of Neurons

4. Hebbian Model Learning

5. Error-Driven Task Learning

6. Combined Model and Task Learning

Course OutlineII. Large-Scale Brain Area Organization and

Cognitive Phenomena7. Large-Scale Brain Area Functional

Organization8. Perception and Attention9. Memory10. Language11. High-Level Cognition

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

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

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

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

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

References J. Hawkins, On Intelligence, Times Books,

2004