Course catalogue doctoral education - VT24

    Startpage
  • Ansökan kan ske mellan 2023-10-16 och 2023-11-15
Application closed
Print
Title Computational Modelling for Cognitive Neuroscience and Psychiatry Research
Course number 5567
Programme Neurovetenskap
Language English
Credits 1.5
Date 2022-10-20 -- 2022-11-17
Responsible KI department Institutionen för neurobiologi, vårdvetenskap och samhälle
Specific entry requirements Background in medicine, biomedicine, biology, psychology, cognitive science, medical imaging, computational biology or similar. Basic knowledge on statistics and programming will be needed in the course.
Purpose of the course The purpose of the course is to introduce doctoral students to computational techniques for modelling and analyzing behavioral 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) identify and choose the most appropriate methods to model their data.
Contents of the course Basic concepts in computational modelling such as parameter fitting and model comparison; introduction to reinforcement learning; classical models for decision-making tasks (drift diffusion model, intertemporal choice, two-armed bandit). Applications in psychiatry: psychosis, addiction, depression, anxiety.
Teaching and learning activities Lectures, hands-on practical sessions, article discussion in seminars.
Compulsory elements Attending the lectures, seminars, and hands-on sessions is mandatory. Absence from a lecture may be compensated by writing an essay on the corresponding topic. The examination is compulsory (seminars, as well as report and presentation of the practical assignment).
Examination The examination consists of two moments: 1) presentation and active discussion in the seminars; 2) 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 Recommended literature:
1. Sutton, R.S. and A.G. Barto, Reinforcement learning: an introduction. 1998, Cambridge, Massachusetts: The MIT Press.
2. Daw, N., Trial-by-trial data analysis using computational models, in Decision Making, Affect, and Learning, M.R. Delgado, E.A. Phelps, and T.W. Robbins, Editors. 2011, Oxford University Press.
3. Piray, P., et al., Hierarchical Bayesian inference for concurrent model fitting and comparison for group studies. PLoS Comput Biol, 2019. 15(6): p. e1007043.
4. Wilson, R.C. and A.G. Collins, Ten simple rules for the computational modeling of behavioral data. Elife, 2019. 8.
5. Huys, Q.J., T.V. Maia, and M.J. Frank, Computational psychiatry as a bridge from neuroscience to clinical applications. Nat Neurosci, 2016. 19(3): p. 404-13.
6. Huys, Q.J., N.D. Daw, and P. Dayan, Depression: a decision-theoretic analysis. Annu Rev Neurosci, 2015. 38: p. 1-23.
7. Sterzer, P., et al., The Predictive Coding Account of Psychosis. Biol Psychiatry, 2018. 84(9): p. 634-643.
Number of students 10 - 20
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) start date of doctoral studies (priority given to earlier start date)
More information The course will be held once a week, every Thursday, for a period of 5 weeks.
Additional course leader
Latest course evaluation Not available
Course responsible Marc Guitart-Masip
Institutionen för neurobiologi, vårdvetenskap och samhälle

marc.guitart-masip@ki.se
Contact person Andreas Olsson
Institutionen för klinisk neurovetenskap
0852482459
andreas.olsson@ki.se