(cross-listed as CIS558)
Computer Analysis and Modeling of Biological Signals and Systems
Spring term, 2005
|Instructor:||Prof. Mark Liberman
Lecture: Tuesday 9:00-11:00, IRCS (3401 Walnut St., 4th floor)
The lab is optional, and will normally function as an informal session to work on homework problems, side topics and term projects.
|Syllabus:||A schedule with links to lecture notes can be found here.|
|Structure:||There will be a series of about six homework exercises using the computer language MATLAB, and a term project, which can be a summary of existing methods and results in some area of interest to you, or can be new work of your own. Grading will be based on the homework (40%), the term project (50%), and class participation (10%).|
|Software:||See this link for information about software used in the course.|
|Prerequisites:||Digital signal processing is mainly applied linear algebra. There are also basic connections to calculus and probability, and the physics of signals will also come up. The course will review the needed mathematical concepts, but if they are all entirely new to you, you will have to work hard to learn both the basic mathematics and its application. However, a genuine interest in understanding, modeling, or mimicking biological systems will go a long way. In the past, participants who have found the course worthwhile had backgrounds ranging from an MS in math to "nothing past high school algebra."|
|Texts and readings:||
Extensive on-line lecture notes will be provided (linked to the syllabus). In addition, some useful articles and book chapters will be made available on line as the semester unfolds. The following texts may be useful for reference:
Since all of these are expensive, one is out of print, and only a small amount of material will be drawn for the course from each, they are not treated as course texts. Copies of these texts will be made available in the phonetics lab (Williams 623) for your reference, and relevant sections will be reproduced for course participants. However, all are well worth owning if you plan to continue in related fields.
|Course Philosophy:||Digital Signal Processing is basically a simple topic, whose
fundamentals are easier for most people to understand than first-year college
calculus is. It provides essential conceptual and practical tools for research
in areas such as computer vision, phonetics and speech processing, neuroscience,
computer music, and any other discipline that is concerned with the production,
perception or interpretation of physical signals by living creatures.
However, DSP is usually taught to electrical engineers after three or four semesters of prerequisites; and then an EE DSP course usually includes some things that are not crucial for a biologically-oriented audience, while leaving out some other other things that are. A properly designed one-semester lab course can give interested students the foundation needed to understand and use DSP concepts and techniques in biological applications. This can be done without requiring a mathematical background much beyond basic algebra.
This course was developed in the mid 1990's by Mark Liberman and Eero Simoncelli. Eero now teaches a modified version, called Mathematical Tools for Neural Science, as a required course in the Neuroscience program at NYU.