OVERVIEW: Computational Neuroscience refers to the application of
methods and techniques from computer science to research in
neuroscience, and to the view of the nervous system as a joint
computational-biological entity. Research in this area presumes a
solid grounding in multiple disciplines. CN580 will focus on building
a firm background in neuroscience, which will be provided by a
"standard" background supplied by a text like Kandel and Schwartz, but
supplemented by more sophisticated readings from the literature of
biophysics and neuroscience. Computational approaches, including
derivation of equations, numerical simulation, and modeling will be
stressed. The course will focus on three spatial scales in the nervous
system: membrane, neuronal, and supra-neuronal. Membrane biophysics
will be covered at the level of the Nernst, Goldman, Hodgkin-Huxley
and Fitzhugh-Nagumo equations, and simplifications such as the
integrate and fire model. Simulators, such as SPICE, NEURON, GENESIS,
or the equivalent will be studied. Basic passive(RC) and active
(transistor, op amp, vco) circuit analysis, and modern VLSI metaphors
for neuronal activity, will be reviewed. Map and column architectures
of visual cortex will be surveyed as an example of supra-neuronal
approaches to neural modeling, and as an introduction to CN780
(Research Topics in Computational Neuroscience).
REQUIREMENTS: Students will be assigned selected readings. Some background readings from Principles of Neural Science (Fourth Edition) by Eric. R. Kandel, James H. Schwartz and Thomas M. Jessell (Elsevier) will be assigned, along with readings in a variety of other texts and primary sources, which will be available as reprints in the CNS library. The use of simulators will be explored in class, including one or more of the choice from Neuron, Genesis, Spice, and Matlab. Manuals and descriptive information for these will be available in the CNS library under the course designation CN580.
There will be a mid-term exam (in class) and a final (in class) exam.
Problem sets, or brief literature reviews, will be assigned during
class. The reading for a given lecture is due on the date of that
The final grade will be weighted equally
between the homework, quizes, the mid-term, and the final.
Homework will be submitted directly to the T.A., or placed in his mailbox at or before the time it is due.
No late homework will be accepted, except by prior agreement with the instructor.
Homework done as a group is not acceptable. This is particularly true
if computer code is involved. One of the major responibilities of
students in this department is to develop skills in modeling, and
computer coding is, to a very large extent, the language and medium of
modeling. Struggling with the development of coding skills is an
important part of the educational process in this department.
Evidence of substantial similarity between the code produced by different students will be viewed with displeasure!
This, of course, does not mean that you can't study together-just do your own work when the time comes.