Credits: 3 credits (A/F only)

Prerequisites: NSci 3101/3102W recommended

Time:  MW, 1:30-3:30pm

Place: 2-137 Jackson

Course Description: 

This course will introduce the concepts of computational and theoretical neuroscience. It will cover (1) general principles of theoretical neuroscience, including distributed representations and information theory, (2) methods for single-cell modeling, including compartmental and integrate-and-fire models, (3) learning rules, including supervised, unsupervised, and reinforcement learning models, and (4) specific systems models, taken from the current theoretical neuroscience literature. Class time will be divided between lecture and discussion. Readings each week will be taken from the current scientific literature. The class is designed for graduate students and for advanced undergraduates. Grading for undergraduates will be based on weekly homework and two essay exams. Grading for graduate and undergraduate students will be handled separately. Prerequisites: NSci 3101/3102W are recommended.

Course Format:

66% lecture

33% discussion

Course Workload:

10-20 pages of reading per week

1-2 homework problems per week

Grading: 

25% - Midsemester Exam                                     

25% - Final exam                                                         

15% - Written Reports or Papers

10% - Class Participation

Students for whom intended:

The class is designed for graduate students and for advanced undergraduates