CSC 2546 -- Winter 2017
Class: Wednesday 2-4
This graduate course offers an introduction to current topics and methods in computational neuroscience. Theoretical analysis and computational methods are tools for characterizing what nervous systems do and determining how and why they do it. Neuroscience encompasses approaches ranging from molecular studies to human psychophysics. Computational neuroscience encourages cross-talk between the many levels of this broad field by constructing compact descriptions of what has been learned at various levels, building bridges between these descriptions, and identifying potential unifying concepts and principles. This course will cover the basic methods used for these purposes and discuss examples in which computational approaches have yielded insight into brain function.
Among the questions addressed are how neurons encode information in spike trains and how these can be decoded, how individual neurons integrate their inputs and generate spikes, what kind of computation can be performed when these neurons are organized into feed-forward or recurrent networks, and what the consequences of different synaptic modification rules can be in learning and development.
Who should take this course? Any student interested in current computational approaches to understanding how the brain works. The course is meant to be accessible to any CS graduate student. It will help if you have a background in basic biology, but it is not necessary: we will spend some time talking about neurons but most of the work will abstract away many biological details. I will suggest some sources to read up on basic terms and concepts in neurobiology. The material involves a fair amount of math, in particular linear algebra and probability/statistics, but again I will provide pointers to texts for catch-up or review.
Readings: The background material and initial reading in this course will come from the following book, which will be available in the bookstore: Dayan, P. & Abbott, L. F. (2001). Theoretical Neuroscience. Cambridge, MA: MIT Press. Other readings will be announced as the course progresses.
Course requirements and grading: The first few class meetings will be lecture-based, covering material in the book. There are assigned readings for these lectures that are intended to prepare you to participate in the class discussion for that day. For the remainder of the term, students will jointly present research papers during class. All students will be expected to do readings each week, and come to class prepared to participate in discussion. Each student will be responsible for the readings, a class presentation, and a course project. The presentation and class participation will be worth 40% of the grade, and the project 60%. There will be no exams in this course.