Course catalogue doctoral education - VT24
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| Title | Computational modelling and data analysis for cognitive neuroscience |
|---|---|
| Course number | 3045 |
| Programme | Neurovetenskap |
| Language | English |
| Credits | 1.5 |
| Date | 2018-05-09 -- 2018-05-25 | 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. |
| Purpose of the course | The purpose of the course is to introduce doctoral students to computational techniques for modelling and analysing cognitive neuroscience data, giving them practical experience applying these tools so that they acquire enough understanding to enable them to 1) critically interpret the results of the studies in the field and 2) adapt them to new experimental paradigms for their own research. |
| Intended learning outcomes | After successful course completion, the students will be able to independently use software packages to analyse behavioural data from cognitive neuroscience experiments. |
| Contents of the course | The course will cover both theory-driven computational modelling and data-driven analysis approaches. Theory-driven approaches: Bayesian modelling; introduction to reinforcement learning; classical models for decision-making tasks (drift diffusion model, intertemporal choice). Data-driven approaches: supervised and unsupervised models in machine learning; classification and regression; dimensionality reduction; validation. |
| 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 (both presentation and assignment) is compulsory. |
| Examination | Examination consists of a practical assignment where students will model and analyse behavioural data from a cognitive neuroscience experiment. The results will be reported and presented in front of the other students in the last session. |
| Literature and other teaching material | Sutton R & Barto A, Reinforcement Learning: An Introduction. McElreath R, Statistical Rethinking. Wagenmakers M & Lee E, Bayesian Cognitive Modeling. |
| 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 | The course dates are: 9th, 16th, 18th, 23rd and 25th of May 2018, from 9:00 to 16:00. Previous experience with statistical analysis and R, python or a similar language are desirable. |
| Additional course leader | Benjamín Garzón (benjamin.garzon@ki.se) Rita Almeida (rita.almeida@ki.se) |
| Latest course evaluation | Not available |
| Course responsible |
Benjamin Garzon Institutionen för neurobiologi, vårdvetenskap och samhälle 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 |
