Dept. of Cognitive and Neural Systems
Boston University
Sept. 8, 2010 $\rightarrow$ Dec. 8, 2010
Weds 1-4 PM
B03, 677 Beacon Street

Course Syllabus and Required Readings

Professor: Eric Schwartz

Office: 677 Beacon Street, 310
Office hours: By appointment
Phone: 353-6179

Teaching Fellow: Nan Jia
Office: Room 107-677 Beacon Street
Phone: 617 299 6268
Office Hours: Thurs. 1:30 -> 5:30 Room 107

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 lecture. The final grade will be weighted equally between the homework, quizes, the mid-term, and the final.

E-MAIL: An alias CN580 will be set up and used to broadcast information to students enrolled in CN580.

A CLASS WEB SITE (a "wiki") is located at
Point any browser at the previous URL to reach this swiki. Located here are a variety of useful links, the homework assignments, an open class discussion page, and other important aspects of this class. The password for the swiki will be given out in class, along with a brief introduction to its use.


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.

ANNOUNCEMENTS: Class announcements will be made on the CN580 swiki:

USEFUL LINKS: The CN580 swiki will contain this syllabus, and other announcements and material pertinent to the class:

Course overview

copyright Prof. Eric L. Schwartz
Dept. of Cognitive and Neural Systems