BCSC 247: Syllabus
Fall 2024
Tue/Thu 12:30 PM - 1:45 PM / Location: Bausch & Lomb Room 270
Personnel
Instructor: Ralf Haefner
Office: Meliora Hall 313
Tue & Thu 1:45-2:30 pm
Teaching Assistants:
Shizhao Liu: Room 428 Meliora Hall; Office Hours: Thu 2:30 - 3:30 pm
Yelin Dong: Room TBD; Office Hours: Tue 2:30 - 3:30 pm
Linh Dinh: Room 3002 Wegmans Hall; Office Hours: Wed 1:30 - 2:30 pm
Recitation time: Friday, time TBD, Location: TBD
Prerequisites for the course: Basic programming skills (MATLAB, R, Python preferred), basic knowledge in linear algebra and analysis
Course Description
This course will cover a range of mathematical theories describing various aspects of brain function with a focus on visual processing and decision-making. We will address the questions of how stochastically spiking neurons can represent information about the outside world, infer knowledge about behaviorally relevant variables and make decisions based on them. The focus of the course will not be on the biological details of neuronal activity, but on mathematical and computational models of how this activity might support perception and decision-making.
Assignments
There will be 5 assignments, all involving programming. They will typically be implementations of models discussed in class in order to apply and deepen understanding. Assignments will include additional questions for graduate students.
Readings
There is no assigned textbook for this class.
Readings come from "Theoretical Neuroscience" by Dayan & Abbott, 2001 (pdf available online) as well as other sources which will be posted on Blackboard.
Readings on programming (both available as ebooks at library):
Matlab for Neuroscientists
Neural data science: a primer with Matlab and Python
Grading
Undergraduate students (4 credits):
Assignments (45%); regular short in-class tests (15%); Final exam (40%)
Graduate students (4 credits):
Assignments (45%); regular short in-class tests (15%); Final exam (40%)
This course follows the College credit hour policy for four-credit courses. This course meets twice weekly for three academic hours per week. The course also includes a recitation for one academic hour per week.
Class attendance is expected
Blackboard will be used for assignments, grading, and slides.
Slack will be used for general communication and discussion.
Collaboration
Collaboration in teams of 2 or 3 students is encouraged but not required. All assignments must be completed and submitted individually, with individual results, based on individual simulations.
Academic Honesty
Bottom line: don’t cheat, don’t plagiarize. Cases of suspected misconduct will not be evaluated directly by us, but will be referred to the College Board on Academic Honesty. I take this policy very seriously. If you are unsure of something, please ask me!
ChatGPT & other generative AI is permitted