Foundations of Neural and Cognitive Modelling

Course manual 2024/2025

Course content

How does the brain, and the networks of spiking neurons it consists of, support the computations required for reasoning, categorisation, vision, language, navigation and many other aspects of human cognition? This course aims at introducing students to the key insights that computational models in the brain and cognitive sciences offer to ultimately answer that question. We will discuss multiple modelling paradigms and the relations between them, including:

1. Dynamical systems and models of single spiking neurons
2. McCulloch-Pitts neurons, Hopfields networks, Attractor Networks
3. Perceptrons, Backpropagation, Deep Learning
4. Reinforcement Learning
5. Bayesian Modelling
6. Symbolic models & the Binding Problem

Throughout the course, we discuss methodological issues in modelling. The course involves lectures, discussion of literature, pen & paper exercises and a series of computer labs where students, with help of the TA, study key computational models in each of these 6 domains. The courses finishes with student presentations of some key papers, and a miniproject where students do some original modelling.

Objectives

• Students are able to explain the conceptual and technical foundations of the major modelling paradigms in brain and cognitive science.
• Students can demonstrate their technical skills by solving representative exercises from these fields.
• Students can demonstrate their conceptual understanding by critically assessing published models in various subfields, distinguishing between appropriate abstractions and inappropriate simplifications, and by describing relations to models formulated in other paradigms.

Teaching methods

• Lecture
• Seminar
• Laptop seminar
• Self-study
• Presentation/symposium
• Working independently on e.g. a project or thesis

Lectures, seminars, computer labs

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

Assignments

Assignment 1,2,3,4,5,6

• Weekly assignments

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

Additional information

More information on the 2019-2020 edition of the course, including more details on the recommended prior knowledge, can be found via: https://cl-illc.github.io/teaching.html

Students taking this course need to have a strong interest in modelling and brain and cognitive science, and some talent for mathematical thinking. The course is also open for motivated students in artificial intelligence and logic. Contact the lecturer (zuidema@uva.nl) when in doubt.

Last year's student feedback

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

Contact information

Coordinator

• dr. Jelle Zuidema