Computational Cognitive Neuroscience -- Spring 2019 |
Instructor: Peggy Seriès
Lectures :on Mondays and Thursdays 11.10 am
Labs : time and place TBA
On wk1 students will be offered a non compulsory matlab tutorial covering basic matlab prerequisites.
TA & Tutor: Samuel Rupprechter and Frank Karvelis.
This is a 10 points course for MSc level students. There are no prerequisites, no prior knowledge in neuroscience is necessary but some background in statistics, calculus, linear algebra is required, as a well as some knowledge of programming. We will use matlab (or python).
Computational Cognitive Neuroscience is a growing research field. The aim of this course is to learn the tools and concepts that can be used to model the relation between the activity of the brain and cognitive processes. This course will appeal to students who are interested in the basic principles of computation in the human and animal brain, in particular how we can relate the activity of the brain to perception, behaviour or decision-making.
This course differs from / complements NC and NIP in focussing on 'higher level' processes and phenomena (e.g. decision making) and more conceptual models (even if we'll try to stay as close as possible to neurophysiology). It is a good complement to those courses if you are interested in a PhD in computational neuroscience.
The topics discussed in the course are mostly of interest for academic research and although they have inspired machine learning solutions, they are of little direct applicability. Tools from machine learning, though, are commonly used in this field.
Apart from learning about the brain, you will also learn about numeral modelling of differential equations, random processes, decoding techniques, dynamical systems, Bayesian modelling and more.
This year, the course material will be posted on Learn.
The information below relates to last year.
The labs will consist mainly of implementation in matlab of simple models of population codes, perception, learning and plasticity and decision making. Attendance and work on this material will help for the assignments.
ClassesThe course slides will be on Learn.
Here is the material from last year for reference, the course this year will be slightly different.