The articles collected in the syllabus spans the history of the brain theory since its inception. Brain theory is the endeavor to understand mind (thinking, intellect) in terms of its design (how it is built, how it works). Subjects include
• Computational mental process, (Longuet-Higgins, H.C.),
• Winograd's SHRDLU program,
• Mental process, formal system, language,
• Neurobiological modeling,
• Perception and associative memory.
Textbook
Samples from references such as, Neurocomputing Foundations of Research, edited by James A. Anderson and Edward Rosenfeld, The MIT Press, 1988
Neural networks, a comprehensive foundation, second edition, by Simon Haykin, Prentice-Hall, Inc., 1999
課程大綱(Syllabus)
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
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]
參考資料(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