Course Description
The scale and complexity of biological data are fast expanding as biotechnology develops. How are we going to process and interpret large-scale data? Using example-based approaches in the context of behavioural neuroscience (e.g., sleep, movement, learning, decision-making), this course aims to bridge biology and computational analysis techniques. Through lectures and hands-on sessions, students will learn about modern methodology in neuroscience (e.g., electrophysiology, optogenetics, calcium imaging) and how to analyze neural data sets. Students will be introduced to programming in Matlab/Python tailored for neuroscience, signal processing, image processing, statistical analysis, and machine learning techniques. By developing practical skills through various neural data types (e.g., EEG, EMG, Ca2+ Imaging), this course equips students with the skills to handle neural data, basic computational approaches in neuroscience, and a quantitative understanding of brain functionality.
Prerequisite
(BIO270H1, BIO271H1/ PSL300H1, PSL301H1), MAT136H1
Exclusion
N/A
Lecturer(s)
Prof Jimmy Fraigne
jimmy.fraigne@utoronto.ca
Prof. Qian Lin
neuroqian.lin@utoronto.ca
Contact Hours
24L/12T
Recommended Preparation
Students should familiarize themselves with linear algebra. A strong interest in neuroscience, data analysis, or computational biology is recommended. Recommended course: CJH332
Evaluation (Subject to change)
Weekly Quizzes (10%)
Assignment 1 (10%)
Term Test (30%)
Assignment 2 (10%)
Assignment 3 (10%)
Final Assessment (30%)
Last updated on June 12th, 2025