Advanced Neural and Cognitive Modelling

Course manual 2023/2024

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

Attendance

Requirements of the programme concerning attendance (OER-B):

1. In the case of practicals, the student must attend at least 80%. Should the student attend less than 80%, he/she must redo the practical, or the Examinations Board may have one or more supplementary assignments issued.
2. In the case of study-group sessions with assignments, the student must attend at least 80% of the study-group sessions. Should the student attend less than 80%, he/she must redo the study group, or the Examinations Board may have one or more supplementary assignments issued.

Assessment

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

Timetable

The schedule for this course is published on DataNose.

Additional information

For this course's website, and websites of other courses of the ILLC's 'Natural Language Processing & Digital Humanities' group, see: https://cl-illc.github.io/teaching.html

Last year's student feedback

In order to provide students some insight how we use the feedback of student feedback to enhance the quality of education, we decided to include the table below in all course guides.

Contact information

Coordinator

• dr. Jelle Zuidema