Chinese Version

The syllabus spans the history of the brain theory. Brain theory is the endeavor to understand mind (thinking, intellect) in terms of its design (how it is built, how it works). Subjects include: neurobiological modeling [1~8] (Hebbian synapse and Hebbian learning; NMDA/LTE), perception, associative memory, computational mental process (Longuet-Higgins, H.C.[38])...

　

Syllabus(2012)

Syllabus for Brain Theory 2012.

Syllabus(2009)

1. Hebbian synapse and Hebbian learning; NMDA/LTE (2 Lectures) [3][4][33]

2. Elman network (2 Lectures) wiki [9~14]

3. Gestalt theory (2 Lecture) Hopfield model Hairy neural network [15~16]

4. Reinforcement learning (2 Lectures, 6 hours total) Q Learning [19~23]

5. The manifold way of perception (1 Lecture) [24~26]

6. Barlow’s cognitive map (2 Lectures) [27~28]

7. Psychological complexity (1 Lecture) [29]

8. Mental model theory (1 Lecture) [30]

9. More

References：

Neurobiological modeling

[1] .The Neurophysiology of Remembering, Karl H. Pribram, Scientific American, 1969, 220(1):73-86

[2] From Bird Song to Neurogenesis, Fernando Nottebohm,     Scientific American, Feb 1989, 74-79

[3] Building a Brainier Mouse, Joe Z. Tsien, Scientific American, April 2000, 43~48

[4] Cell Assemblies

[5] A possible organization of animal memory and learning, L. N. Cooper, Proceedings of the Nobel Symposium on Collective Properties of Physical Systems, B. Lundquist and S. Lundquist (Eds.), New York: Academic Press, 252-264, 1973

[6] The neurobiology of cognition, M. James Nichols and William T. Newsome, Nature, vol.402, Supp, 1999, C35~C38.

[7] Genetics and general cognitive ability, Robert Plomin, Nature, vol. 402, Supp,1999, C25~C29

[8] The Emergence of Intelligence, by William H. Calvin, Scientific American, vol. 9(4), 44~51, 1998

Elman network

[9] Finding Structure in Time, J.L. Elman, Cognitive Science, vol. 14, 179-211 (1990)

[10] J.L. Elman, Generalization, simple recurrent networks, and the emergence of structure, Proceedings of the 20th Annual Conf. of the Cognitive Science Society, Mahway, NJ, (1998).

[11] J.L. Elman, E.A. Bates, and M.H. Johnson, A. Karmiloff-Smith, D. Parisi, K. Plunkett, Rethink innateness, The MIT Press, Cambridge, Massachusetts, (1996).

[12] Representational Issues: Commentary on The Algebraic Mind, by Gary Marcus, MIT Press, Jeff Elman

[13] Response to Jeff Elman's Commentary on The Algebraic Mind (MIT Press), Gary F. Marcus,

January 2, 1999

[14] Literal works

Hopfield &Gestalt

[15] Hairy neural network

[16] Irvin Rock and Stephen Palmer, The legacy of Gestalt psychology, Scientific American, pages 84~90, December 1990

[17] Holonomic brain theory

[18] Theories and measures of consciousness: An extended framework, Anil K. Seth,* Eugene Izhikevich,* George N. Reeke,*† and Gerald M. Edelman, Proc Natl Acad Sci U S A. 2006 July 11; 103(28): 10799–10804.

Reinforcement learning

[19] Reinforcement learning: An introduction, by R.S. Sutton and A.G. Barto, 1998, MIT Press.

[20] Q Learning

[21] Barto, A.G., Sutton, R.S., & Anderson, C. (1983). Neuron-like adaptive elements that can solve difficult learning control problems, IEEE Transactions on Systems, Man, and Cybernetics, SMC-13: 834-846.

[22] Learning to Predict by the Methods of Temporal Differences, Machine Learning, 1988

[23] Practical Issues in Temporal Difference Learning, Machine Learning, 8, pp.257–277 (1992).

Manifold

[24] A global geometric framework for nonlinear dimensionality reduction Joshua B. Tenenbaum, Vin de Silva, John C. Langford, Science, vol. 290, 22 December 2000, 2319-2323

[25] Nonlinear Dimensionality reduction by locally linear embedding, Sam T. Roweis and Lawrence K. Saul, Science, vol. 290. 22 December 2000, 2323-2326

[26] The manifold ways of perception, H. Sebastian Seung and Daniel D. Lee,
Science, vol 290, 22 December, 2268-2269

Barlow’s theory

[27] Unsupervised Learning, H.B. Barlow, Neural Computation 1, 295-311 (1989)

[28] Finding Minimum Entropy Codes, H.B. Barlow, T.P. Kaushal, G.J. Mitchison, Neural Computation 1, 412-423 (1989)

Complexity

[29] Minimization of Boolean complexity in human concept learning, Jacob Feldman, Nature, vol. 407, 5 October 2000, 630-632

[30] Illusions in Reasoning About Consistency
P. N. Johnson-Laird, Paolo Legrenzi, Vittorio Girotto, Maria S. Legrenzi, SCIENCE, VOL 288 21 APRIL 2000 p.531

Books and articles

[31] Mind design II, edited by John Haugeland, 1997

[32] Concepts for Neural Networks. A Survey edited by L.J. Landau and J.G. Taylor.

[33] Neurocomputing: Foundations of Research, edited by James A. Anderson and Edward Rosenfeld, The MIT Press, 1988

[34] Neural networks, a comprehensive foundation, second edition, by Simon Haykin, Prentice-Hall, Inc., 1999

[35] Cognitive Science

[36] Conscious and Cognition

[37] AI Lecture Tokyo: Cognition as Computation: Why Did it Fail?

[38] A program which learns to count, by R.J.D. Power and H.C. Longuet-Higgins, Lecture note in chinese.

Syllabus(2008)

1. Reinforcement learning (2 Lectures, 6 hours total) Q Learning [9]

2. Elman network (2 Lectures) wiki [13][14][15]

3. Hebbian synapse and Hebbian learning; NMDA/LTE (2 Lectures) [11][20]

4. Gestal theory (2 Lecture) Hopfield model Hairy Model [16][17][18][19]

5. Cognitive map (2 Lectures) [1][2]

6. The manifold way of perception (1 Lecture) [3][4][5]

7. Psychological complexity (1 Lecture) [6]

8. Mental model theory (1 Lecture) [12]

9. More

References：

[1] Unsupervised Learning, H.B. Barlow, Neural Computation 1, 295-311 (1989)

[2] Finding Minimum Entropy Codes, H.B. Barlow, T.P. Kaushal, G.J. Mitchison, Neural Computation 1, 412-423(1989)

[3] A global geometric framework for nonlinear dimensionality reduction Joshua B. Tenenbaum, Vin de Silva, John C. Langford, Science, vol. 290, 22 December 2000, 2319-2323

[4] Nonlinear Dimensionality reduction by locally linear embedding, Sam T. Roweis and Lawrence K. Saul, Science, vol. 290. 22 December 2000, 2323-2326

[5] The manifold ways of perception, H. Sebastian Seung and Daniel D. Lee,
Science, vol 290, 22 December, 2268-2269

[6] Minimization of Boolean complexity in human concept learning
Jacob Feldman, Nature, vol 407, 5 October 2000, 630-632

[7] Mind design II, edited by John Haugeland, 1997

[8] Concepts for Neural Networks. A Survey edited by L.J. Landau and J.G. Taylor.

[9] Reinforcement learning: An introduction, by R.S. Sutton and A.G. Barto, 1998, MIT Press.

[10] Neurocomputing: Foundations of Research, edited by James A. Anderson and Edward Rosenfeld, The MIT Press, 1988

[11] Neural networks, a comprehensive foundation, second edition, by Simon Haykin, Prentice-Hall, Inc., 1999

[12] Illusions in Reasoning About Consistency
P. N. Johnson-Laird, Paolo Legrenzi, Vittorio Girotto, Maria S. Legrenzi
SCIENCE, VOL 288 21 APRIL 2000 p.531

[13] J.L. Elman, Generalization, simple recurrent networks, and the emergence of structure, Proceedings of the 20th Annual Conf. of the Cognitive Science Society, Mahway, NJ, (1998).

[14] J.L. Elman, E.A. Bates, and M.H. Johnson, A. Karmiloff-Smith, D. Parisi, K. Plunkett, Rethink innateness, The MIT Press, Cambridge, Massachusetts, (1996).

[15] Conscious and Cognition

[16] Cognitive Science

[17] Logic, gestalt theory, and neural computation in research on auditory perceptual organization, Randolph Eichert1, Luder Schmidt1 and Uwe Seifert1, LNCS Volume 1317/1997, pages 70~88

[18] Irvin Rock and Stephen Palmer, The legacy of Gestalt psychology, Scientific American, pages 84~90, December 1990

[19] Holonomic brain theory

[20] Cell Assemblies

Syllabus(2007)

Announce

• The reference page for illusion: http://dogfeathers.com/java/necker.html

Lecture 1:

The manifold ways of perception
Dimensionality reduction
Isomap, LLE, GTM
Coherent structure

Reference: [6], [7], [8]

Lecture 2:

Grand illusion(1992)
Outside memory
Change blindness, inattentional blindness
Coherence theory

Reference: [10], [11]

Lecture 3:

Illusion in reasoning
Formal rule theory
Mental model theory (Lecture note in chinese)

Reference: [2]

Lecture 4:

Clausius's entropy
Boltzmann's entropy
Shannon's entropy
Fisher information
Contention between two hemisphere

Reference: [1]

Lecture 5:

Psychological complexity
Subjective complexity
Logical complexity
Kolmogorov complexity
Boolean complexity

Reference: [9]

Lecture 6 & 9:

Cognitive map
Working model
Redundancy, Knowledge, Regularity
Negative filter
Redundancy reduction, Minimum entropy code

Reference: [3], [4], [5]

Lecture 7 & 8:

Volume transmission, pleasure, mood, wakeness
Volume transmitters, nitric oxide, carbon monoxide, CSF Holistic forms

Lecture 10:

Gestalt theory, Hologram
Structuralism
Behaviorism

Lecture 11:

Ceteris paribus condition
Formal representation
Knowledge representation

Reference: [14]

Lecture 12:

Godel's third remark, mental procedures
A philosophical error in Turing's work
A philosophical error in Penrose's work

Reference: [15]

Reference

[1]

Side Splitting
News & Analysis
SCIENTIFIC AMERICAN January 2001 p.18

[2]

Illusions in Reasoning About Consistency
P. N. Johnson-Laird, Paolo Legrenzi, Vittorio Girotto, Maria S. Legrenzi
SCIENCE, VOL 288 21 APRIL 2000 p.531

[3]

Unsupervised Learning
H.B. Barlow
Neural Computation 1, 295-311 (1989)

[4]

Adaptation and Decorrelation in the Cortex
edited by Richard Durbin, Christopher Miall, Graeme Mitchison.
The Computing neuron, p.54-72

[5]

Finding Minimum Entropy Codes
H.B. Barlow, T.P. Kaushal, G.J. Mitchison
Neural Computation 1, 412-423(1989)

[6]

A global geometric framework for nonlinear dimensionality reduction
Joshua B. Tenenbaum, Vin de Silva, John C. Langford
Science, vol. 290, 22 December 2000, 2319-2323

[7]

Nonlinear Dimensionality reduction by locally linear embedding
Sam T. Roweis and Lawrence K. Saul
Science, vol. 290. 22 December 2000, 2323-2326

[8]

The manifold ways of perception
H. Sebastian Seung and Daniel D. Lee
Science, vol 290, 22 December, 2268-2269

[9]

Minimization of Boolean complexity in human concept learning
Jacob Feldman
Nature, vol 407, 5 October 2000, 630-632

[10]

Beyond the grand illusion: what change blindness really teaches us about vision
Alva Noe, Kevin O’Regan,...,
Visual Cognition, vol. 7, page 93, 2000
http://nivea.psycho.univ-paris5.fr/ASSChtml/ASSC.html

[11]

Solving the‘real’mysteries of visual perception: the world as an outside memory
J.K. O’Regan,
Canadian Journal of Psychology, vol. 46, page 461, 1992

[12]

The emergence of the volume transmission concept
Michele Zoli and others
Brain Research Reviews, vol. 26, pate 136, 1998

[13]

Signals that go with the flow
Charles Nicholson,
Trends in Neurosciences, vol. 22, page 143, 1999

[14]

Chapter 6, Mind design II
edited by John Haugeland,
1997

[15]

Chapter 6, Turing's Philosophical Error?
in Concepts for Neural Networks.
A Survey edited by L.J. Landau and J.G. Taylor.

[16]

Q Learning

[17]

A program which learns to count, by R.J.D. Power and H.C. Longuet-Higgins, Lecture note in chinese.

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