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. (course credit: NSCI 855)
Organizers: Drs. Gunnar Blohm, Paul Schrater, Megan Peters &
Konrad Körding
Dates: August 2 - 16, 2020
Location: London, ON, Canada
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
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.
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
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.
This course is based on the annual Summer School in
Computational Sensory-Motor Neuroscience (CoSMo), and
alternatively the online equivalent Neuromatch Academy
Computational Neuroscience (NMA-CN) course. Through lectures,
tutorials and a problem-based project, students will gain
advanced knowledge and experience in the application of
computational methodologies to modelling in neuroscience.
This course is based on the annual Neuromatch Academy Deep
Learning Summer School, which is a online 3-week (15 days)
intensive course. Through lectures, hands-on tutorials and a
problem-based project, students will gain advanced knowledge and
experience in how modern deep learning methods can help advance
(neuro-)science.
