Course manual 2025/2026

Course content

This course aims to bring students across Brain and Cognitive Sciences up to speed on some current, exciting computational models in their fields. The course is organized around 5 or 6 recent papers in Brain and Cognitive Science, updated every year. Frequent topics include:

deep learning models for predicting brain activity in language processing and vision,
dynamical systems models of music processing,
reinforcement learning and the cognitive (neuro)science of spatial cognition,
non-parametric Bayesian models and artificial language learning, and
models of number cognition and the neural basis of mathematics.

During the first three weeks of the course, we cover the background you need to appreciate these papers, including lectures on dynamical systems, deep learning, and attractor networks, a selection of video lectures, computer labs and some math tutorials. Around week 4, students present the core papers and lead detailed discussions about the models. You will choose, with a small group, which paper you want to present.

In the weeks after the presentations, you will work in a small group on a miniproject. In a typical miniproject, you will continue working with the paper you presented, replicate part of the results and try to obtain a small extension over the published findings. We will help you develop the miniproject, and we will continue to meet in the final two weeks with guest lectures on capita selecta in computational modelling.

Study materials

Literature

All reading will be made available on the course website

Software

Students are free to use use any appropriate software for working with the models discussed. The specific best choices will vary per paper (and per students’ skills and interests).

Objectives

Explain the possibilities and limitations of major modelling paradigms for advancing research in the brain and cognitive sciences
Replicate results from modern computational modelling studies in the field, in order to critically reassess the validity and relevance of those results
Critically assess published models in various subfields

Teaching methods

Lecture
Computer lab session/practical training
Laptop seminar
Presentation/symposium

Learning activities

Activity	Hours
Presentatie	2
Werkcollege	28
Self study	138
Total	168	(6 EC x 28 uur)

Attendance

Some course components require compulsory attendance. If compulsory attendance applies, this will be indicated in the Course Catalogue which can be consulted via the UvA-website. The rationale for and implementation of this compulsory attendance may vary per course and, if applicable, is included in the Course Manual.

Assessment

Item and weight	Details
Final grade
25% Lab Portfolio
25% Literature Presentation
50% Project Report

Fraud and plagiarism

The 'Regulations governing fraud and plagiarism for UvA students' applies to this course. This will be monitored carefully. Upon suspicion of fraud or plagiarism the Examinations Board of the programme will be informed. For the 'Regulations governing fraud and plagiarism for UvA students' see: www.student.uva.nl

Course structure

Weeknummer	Onderwerpen	Studiestof
1
2
3
4
5
6
7
8

Owner	Master Brain and Cognitive Sciences
Coordinator	dr. J.A. Burgoyne
Part of	Master Logic, Master Brain and Cognitive Sciences, Master Logic, track Logic and Computation, Master Logic, track Logic and Philosophy, Master Logic, track Logic and Language, Master Logic, track Logic and Mathematics,

Advanced Neural and Cognitive Modelling