Instructors: Mathis Lamarre, Anuja Negi, Fatma Deniz
Overview
| Language |
English |
| Credits |
3 ECTS |
| Lecture Period |
April 13 – July 18, 2026 |
| Time |
Thursdays @ 2:30-4:00pm |
| Location |
MAR 5.044 |
| ISIS |
link |
Content
In the neurocolloquium, we read and discuss recent scientific publications from the field of computational cognitive neuroscience.
A particular focus will be on literature that uses methods from the field of computer science and artificial intelligence as a means of modelling brain functions – in particular language – as represented in functional Magnetic Resonance Imaging (fMRI) data.
Learning outcomes
Students will become familiar with topics and debates within the field of language research and cognitive neuroscience.
Furthermore, they will learn to read and discuss scientific articles and gain an understanding of how computational approaches can be applied to brain research.
Structure
Each week, one paper will be read in advance and discussed together in detail.
At the beginning of the course, each student will be assigned one paper for which they will prepare a small presentation of the methods section which they will present prior to the discussion.
Depending in the scope of these method sections, presentations should be around 10-15 minutes and should be accompanied by slides.
Schedule
| Date |
Paper |
Presenter |
| Apr 16, 2026 |
Introduction to brain encoding and decoding models: part I |
Chris |
| Apr 23, 2026 |
Papers selection + Introduction to brain encoding and decoding models: part II |
Mathis |
| Apr 30, 2026 |
Conversation content is organized across multiple timescales in the brain |
Mathis |
| May 07, 2026 |
Hierarchical Brain–LLM Alignment Reveals Layer-Specific Neural Representations of Second Language Proficiency |
Peter |
| May 14, 2026 |
Holiday - No class |
- |
| May 21, 2026 |
Temporal structure of natural language processing in the human brain corresponds to layered hierarchy of large language models |
Sarah |
| May 28, 2026 |
Hierarchical dynamic coding coordinates speech comprehension in the human brain |
Anuja |
| Jun 04, 2026 |
Causal Interpretation of Neural Network Computations with Contribution Decomposition |
Omar |
| Jun 11, 2026 |
Why AI systems don’t learn and what to do about it
Lessons on autonomous learning from cognitive science |
Mathis |
| Jun 18, 2026 |
Text-to-music generation models capture musical semantic representations in the human brain |
Peter |
| Jun 25, 2026 |
A foundation model to predict and capture human cognition |
Anuja |
| Jul 02, 2026 |
A foundation model of vision, audition, and language for in-silico neuroscience |
Omar |
| Jul 09, 2026 |
Modeling the language cortex with form-independent and enriched representations of sentence meaning reveals remarkable semantic abstractness |
Sarah |
| Jul 16, 2026 |
The Platonic Representation Hypothesis |
Chris |
| Jul 16, 2026 |
Revisiting the Platonic Representation Hypothesis: An Aristotelian View |
Chris |