CS 591.02: Informational Aspects of Biology
Instructors: Stephanie Forrest
Time: TuTh 2:00 - 3:15 PM
Location: ME 208
Many natural systems process information, but the methods they use
are radically different from those typically found in electronic
computers. Understanding these alternative forms of computation is
important, both for the purpose of building better computers and as a
scientific enterprise in which the computational perspective helps
explain the observed behavior of the natural system. CS 591.02 will
concentrate on computation in biological systems, covering both
computational modeling of natural phenomena and computer algorithms
based on methods observed in biology.
CS 591.02 will be taught as an introductory graduate-level class with
a significant programming component. However, we will try to
accommodate students from other disciplines who lack a strong computer
science background. The material will be divided into four general
topics: The brain (neural modeling and neural networks), vision
(computational theory, psychophysics, neuroscience), evolution
(genetics, population biology, and genetic algorithms), and immunology
(overview of the immune system and computer immune systems). For each
topic, we will present an overview of the relevant biology, current
modeling techniques, and computer algorithms based on the biology.
- Hebb, from The Organization of Behavior (Neurocomputing Ch. 4)
- Minsky and Papert, from Perceptrons (Neurocomputing Ch. 13)
- McClelland and Rumelhart, Psychological Review (Neurocomputing Ch. 25)
- Neocognitron (Neurocomputing Ch. 31)
- Boltzmann Machine (Neurocomputing Ch. 38)
- Backpropagation (Neurocomputing Ch. 42)
- Sutton, Temporal Difference Method (Machine Learning)
- Dayan and Hutton, Helmholtz Machine (Neural Networks)
- Frey, B., Dayan, P., and G. Hinton, A
Simple Algorithm that Discovers Efficient Perceptual Codes,
Computational and Psychophysical Mechanisms of Visual Coding,
Jenkin and Harris (eds.), Cambridge Univ. Press, New York, NY, 1997.
- Adelson, E.H., Perceptual Organization and the Judgement of Brightness,
Science 262, pp. 2042-2044, 1993.
- Crick, F., The Astonishing Hypothesis, Chapters 10-11.
- He, Z.J., and T.L. Ooi, Illusory Contour Formation Affected by
Luminance Contrast Polarity, Perception 27, pp. 313-335, 1998.
- Marr, D. Vision, Chapter 1.
- Marr, D. and T. Poggio, Cooperative Computation of Stereo
Disparity, Science 194, pp. 283-287, 1976.
- von der Heydt, R., Peterhans, E., and G. Baumgartner, Illusory
Contours and Cortical Neuron Responses, Science 224, pp. 1260-1262,
- Williams, L.R., and D.W. Jacobs, Stochastic Completion Fields: A
Neural Model of Illusory Contour Shape and Salience, Neural
Computation 9, pp. 837-858, 1997.
- Darwin, C. Origin of Species, Chapters 1-3.
- Mitchell, M. An Introduction to Genetic Algorithms, MIT
Press, 1997, selected readings.