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Scientific Model Building

CCNM17-CM-108:2

lecture

Master

Cognitive Science MSc

Computational and Cognitive Neuroscience MSc

2

Semester 3

Autumn semester

Objectives

The aim of this course is to introduce students to the field of scientific modelling, model synthesis and analysis. As such, less emphasis will be on a "top-down" approach than in standard lecture courses. Students will have an opportunity to decide which topics will shape the direction of the course.

Contents

Topics to be covered:

• History of Complex Systems (Turing, von Neumann, Ulam, Conway, Wolfram)
• Introduction to Cellular Automata (1D/2D CA, rule codes,phenomenological studies, behaviour classes)
• CA Models of Fluid Dynamics (Lattice Gas Automata)
• Self-Organized Criticality (Sandpile Model, Forest fire model)
• Complex systems and emergence in Complex Systems
• Complex Networks, Small World Networks

One or two other topics will also be covered depending on the interests of students. This could cover topics including (but not restricted to): CA based Neural Networks, Dynamical Systems, Self-reproducing, Generalized CA models (probabilistic, reversible, quantum, structurally dynamic, etc.), Applications of CA.

Evaluation of outcomes
Learning requirements, mode of evaluation, criteria of evaluation:
requirements

• attendance

mode of evaluation: examination

• Boccara, N. (2004). Modeling complex systems. New York: Springer. ( )
• Newman, M. E. J. (2010). Networks: an introduction. Oxford ; New York: Oxford University Press. ( )
• Background material: Ilachinski, A. (2001). Cellular Automata:A Discrete Universe. Singapore: World Scientific Publishing Co. Pte. Ltd.