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Complex Systems Related Courses -- Fall 2009
The following are courses that are relevant to the study of complex
systems. These courses are not taught by CSCS, so inquiries should be
directed to the departments or instructors offering the courses.
This list is by no means complete. It just reflects the courses about
which we have been told. If you know of other relevant courses, please
let us know and we will announce them here. You can also find
information about other courses and research activities at UM by
following the various UM-related links at
our websites page.
Note well:
Some of these courses may be used to fullfill requirements for the
CSCS Graduate Certificate,
but only by permission of the CSCS Director. Please see the
CSCS Certificate Requirements for detailed
descriptions. For further information about using these courses to
fulfill CSCS requirements, please contact the CSCS office
( cscs at umich edu).
Complexity and Emergence
Cross-listed as Honors 493, CSE 594, and Psych 404/808
Instructor: John Holland
Time: Tu Th 9-11:00AM
EECS 587: Parallel Computing
Instructor: Quentin F. Stout
Time: TuTh 12:00-1:30
Location: 1008 EECS
It is almost impossible to buy a computer that isn't parallel. New CPUs are on chips with multiple processors (multicore systems):
that's what "core duo" refers to in the ads for laptops and desktops. The Sony Playstation 3 uses a chip with several cores (the IBM
Cell processor, which is also being used in some supercomputers), and some graphics processing chips have 128 specialized compute
cores. The number of cores/chip will continually increase, and hence parallel computing is needed to make use of whatever system you
buy. This is especially true for compute-intensive tasks such as simulations or analyzing large amounts of data. For more information, see
www.eecs.umich.edu/~qstout/587.
MATH 559 (or BIOINF 800-001): Computational and Mathematical Neuroscience
Instructor: Victoria Booth
Time: MWF 2-3pm
Computational neuroscience investigates the brain at many different
levels, from single cell activity, to small local network computation,
to the dynamics of large neuronal populations. As such, this course
introduces students to modeling and quantitative techniques used to
investigate neural activity at these different levels.
Topics to be covered include:
Passive membrane properties, the Nernst potential, derivation of the
Hodgkin-Huxley model, action potential generation, action potential
propagation in cable and multi-compartmental models, probabilistic
models for ion channel gating, reductions of the Hodgkin-Huxley model,
phase plane analysis, linear stability of equilibria, bifurcation
analysis, synaptic currents, excitatory and inhibitory network dynamics,
firing rate models, neural coding.
No required textbook. Readings and homework problems will be selected
from a number of different texts including: 1. Foundations of
Cellular Neurophysiology by D. Johnston and S.M.
Wu (MIT Press, 1999). 2.Theoretical Neuroscience: Computational and Mathematical
Modeling of Neural Systems by P. Dayan and L. Abbott (MIT Press, 2005).
3. Biophysics of Computation by C. Koch (Oxford University Press,
1999).
Numerical implementation and analysis of the models presented in the
lectures will be an integral part of the course. MATLAB experience
helpful but not required. Course requirements will include homework
assignments containing a combination of analytical and numerical-based
problems, a longer-term modeling project and an oral presentation of the
project to the class at the end of the semester.
Prerequisites: Math 216, 217 (required) and 463 (recommended), or
permission of instructor.
Questions? Contact Victoria Booth, Departments of Mathematics and
Anesthesiology, 4075 East Hall,vbooth@umich.edu
MATH 658: Nonlinear Dynamics, Geometric Mechanics and Control
Instructor: Anthony M. Bloch
Time: TuTh 10:00-11:30
This course will discuss aspects of the modern theory of nonlinear dynamics and
ordinary differential equations as applied to problems in geometric mechanics, Hamiltonian
and nonholonomic systems (systems with nonintegrable constraints), nonlinear stability
theory and nonlinear control theory. The role of symmetry and reduction will be discussed
as well as topics such as the least action principle, integrability, symplectic and Poisson
geometry, and controllability and accessibility on manifolds.
Text: A. Bloch, Nonholonomic Mechanics and Control, Springer Verlag. Other books
will be referenced as well as the primary mathematical literature.
Prerequisite: a course in differential equations.
Updated April 2009
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