Teaching

Summer School in Computational Sensory-Motor Neuroscience (CoSMo 2020)

The 9th edition of this summer school will integrate modern ML approaches with traditional modeling techniques. Teaching materials will be hands-on Python tutorial-based and will be complemented by step-by-step how to model guidance taught during 2-week group projects.

Organizers: Drs. Gunnar Blohm, Paul Schrater, Megan Peters & Konrad Körding
Dates: August 2 - 16, 2020
Location: London, ON, Canada



NSCI 401: Introduction to Modelling in Neuroscience

This course provides an introduction to the main modelling approaches and theoretical concepts in Neuroscience. We discuss the computational anatomy of the brain and how it implements perception, learning, memory, decision making and motor control, among other topics.

Lectures and seminar. Offered in Fall Term.

Prerequisite: STAT 263* or equivalent and standing in the fourth year BSCH LISC degree; or permission of course director. ANAT 312*, or NSCI 323* or NSCI 324*, or PSYC 271*, or equivalent highly recommended



NSCI 800: Current Topics in Neuroscience

An advanced course that will focus on current research topics in selected areas of Neuroscience. Topics will include research in all fields of specialization within the Neuroscience graduate program (Cellular/Molecular Neuroscience, Systems Neuroscience, Cognitive/Behavioural Neuroscience, Neurological & Psychiatric Disorders) to introduce students to the breadth of research in Neuroscience. This course is required for all M.Sc. students in the Neuroscience graduate program.

Lecture and seminar. Offered in Fall Term.

Prerequisite: An introductory course in neuroscience (NSCI 323/324 or equivalent), or permission of the course supervisor. Enrolment is limited with priority given to Neuroscience graduate students.



NSCI 801: Quantitative Neuroscience


This is a tutorial-based introduction to quantitative methods for neuroscience research. The goal is to provide Matlab/Python-based hands-on skills in signal processing, basic and advanced statistics, data neuroscience (machine learning) and model fitting methods. This includes an introduction to scientific programming as well as causality-supporting methods and open science framework approaches.

Lectures and hands-on tutorials. Offered in Winter Term.

Prerequisite: none. No previous experience required



NSCI 850: Computational Approaches to Neuroscience

This course provides an overview and hands on experience of the most important computational approaches in Neuroscience. The main topics covered include single cell and neural network modelling, Bayesian approaches, State Space modelling and Optimal Control Theory. More specific modelling approaches are also discussed as well as some widely used computational data analysis methods.

Lectures and hands-on tutorials. Offered in Winter Term.

Prerequisite: permission of course coordinator.