CS 591.02: Informational Aspects of Biology

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.

Reading List

• Brain

  • 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.

• Vision

  • 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, 1984.

  • 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.

• Evolution

  • Darwin, C. Origin of Species, Chapters 1-3.

  • Mitchell, M. An Introduction to Genetic Algorithms, MIT Press, 1997, selected readings.

• Immunology

  • (to be announced)