Computational Cognitive Neuroscience

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Computational Cognitive Neuroscience

• Shyh-Kang Jeng
• Department of Electrical Engineering/
• Graduate Institute of Communication/
• Graduate Institute of Networking and Multimedia

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Artificial Intelligence
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http//www.takanishi.mech.waseda.ac.jp/top/researc
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Jeff Hawkinss 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
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Artificial Neural Networks
R. O. Duda, P. E. Harr, and D. G. Stork, Pattern
Classification, 2nd ed., John Wiley Sons, 2001
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Jeff Hawkinss Comments on Artificial Neural
Networks

• Connectionists intuitively felt the brain wasnt
  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

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Jeff Hawkinss 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
  natures engine of intelligence, the neocortex
• No other roads will get us there

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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.

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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/FileSleep_EEG_Stage_
1.jpg
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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

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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

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Two-Route Model for Reading
http//en.wikibooks.org/wiki/File1_1_twoRouteMode
lInReading.JPG
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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

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Computational Model for Reading
Randall C. OReilly and Yuko Munakata,
Computational Explorations in Cognitive
Neuroscience Understanding the Mind by
Simulating the Brain, MIT Press, 2000
Randall C. OReilly 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
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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

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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

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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

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Textbook and Website

• Randall C. OReilly 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

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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/FileS
creenshot_ax_tutorial.png
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References

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

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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

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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

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References

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