Course catalogue doctoral education - VT24
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| Title | Computational modelling for cognitive neuroscience and psychiatry research |
|---|---|
| Course number | 3144 |
| Programme | Neuroscience |
| Language | English |
| Credits | 1.5 |
| Date | 2019-04-04 -- 2019-05-16 | Responsible KI department | Department of Neurobiology, Care Sciences and Society |
| Specific entry requirements | Background in medicine, biomedicine, biology, psychology, cognitive science, medical imaging, computational biology or similar. Previous experience with statistical analysis (regression, general linear model) and the R software. |
| Purpose of the course | The purpose of the course is to introduce doctoral students to computational techniques for modelling and analysing behavioural data for cognitive neuroscience and psychiatry research, providing them with practical experience applying these techniques. |
| Intended learning outcomes | After successful course completion, the students will be acquainted with several key computational models and have enough understanding to enable them to 1) critically interpret the results of the studies in the field and 2) adapt the models to new experimental paradigms for their own research. The students will be able to implement and estimate the models with the R package rstan. |
| Contents of the course | Bayesian modelling; introduction to reinforcement learning; classical models for decision-making tasks (drift diffusion model, intertemporal choice, two-armed bandit). Applications: psychosis, addiction, depression, anxiety. |
| Teaching and learning activities | Lectures. Hands-on sessions with practical exercises. |
| Compulsory elements | Attending the lectures and hands-on sessions is mandatory. Absence from a lecture may be compensated by writing an essay on the corresponding topic. The final examination is compulsory (both report and presentation). |
| Examination | Examination consists of a practical assignment where students will define a problem in cognitive neuroscience or psychiatry and describe how to study it with the approaches explained in the course (theoretical framework, experiments, modelling and analysis, expected outcomes). The assignments will be presented in front of the other students in the last session. |
| Literature and other teaching material | Sutton & Barto, Reinforcement Learning: An Introduction. McElreath, Statistical Rethinking Wagenmakers & Lee, Bayesian Cognitive Modeling http://mc-stan.org |
| Number of students | 8 - 16 |
| Selection of students | Selection will be based on 1) the relevance of the course syllabus for the applicant's doctoral project (according to written motivation), 2) date for registration as a doctoral student (priority given to earlier registration date) |
| More information | Course dates and times: April 4, 11, 25 and May 2 (9:00-16:00); May 9 and 16 (9:00-12:00).
This course is new but builds on a previous course with number 3045, the course evaluation of which can be seen below. |
| Additional course leader | |
| Latest course evaluation | Course evaluation report |
| Course responsible |
Benjamin Garzon Department of Neurobiology, Care Sciences and Society benjamin.garzon@ki.se |
| Contact person |
Benjamin Garzon Institutionen för neurobiologi, vårdvetenskap och samhälle benjamin.garzon@ki.se Rita Almeida Institutionen för klinisk neurovetenskap rita.almeida@ki.se |
