Duration
26h Th, 26h Pr
Number of credits
| Master of Science (MSc) in Biomedical Engineering | 5 crédits | |||
| Master of Science (MSc) in Electrical Engineering | 5 crédits |
Lecturer
Language(s) of instruction
English language
Organisation and examination
Teaching in the first semester, review in January
Schedule
Units courses prerequisite and corequisite
Prerequisite or corequisite units are presented within each program
Learning unit contents
This course provides an overview of different mathematical modeling approaches to cognition. The goal of the course is to compare the various approaches constructively, trying to identify which aspects of cognition, which kind of experimental data, which type of questions are best addressed by each approach. The dynamical systems/embodied intelligence approach studied in detail in the course Brain Inspired Computing will also be touched upon here as one out of many approaches to understanding cognition.
Part I. Cognitive Modeling Paradigms (lectures)
- Connectionists Models of Cognition
- Bayesian Models of Cognition
- Symbolic and Hybrid Models of Cognition
- Logic-Based Modeling of Cognition
- Dynamical Systems Approaches to Cognition
- Quantum Models of Cognition
- Constraints in Cognitive Architectures
- Deep Learning
- Reinforcement Learning
Each student will select a topic among the following. She/he will develop the chosen topic in the form of a research project and report her/his advances weekly to the class.
- Computational modeling of basic cognitive functionalities: categorization; inductive reasoning; deduction; decision-making; skill acquisition; episodic memory; working memory; cognitive control; associative learning; reinforcement learning.
- Application to cognitive fields: developmental psychology; personality and social psychology; industrial-organizational psychology; psychiatry; psycholinguistic; natural language understanding and generation; creativity; emotions and cognition-emotions interaction; morality; cognitive engineering; vision; motor control.
Learning outcomes of the learning unit
Through theoretical lectures, mathematical modeling, and computational experiments, the students will be able to understand and use the most important and most widely used models of cognition.
Prerequisite knowledge and skills
Basic knowledge in mathematical modeling (calculus, probability, algebra, elementary dynamical systems/differential equations) is strongly recommended. However, students lacking a strong mathematical background are also invited to take the course and efforts will be made for everybody to be able to follow along.
Planned learning activities and teaching methods
The course includes face-to-face lectures, exercise sessions (mostly focusing on computational examples), and projects. For project development, students will be able to access hardware and experimental data from the Department Neuromorphic Lab.
Mode of delivery (face to face, distance learning, hybrid learning)
Face-to-face course
Additional information:
Face-to-face.
Recommended or required readings
The course will be based on the monograph:
The Cambridge Handbook of Computational Cognitive Sciences, 2nd Edition, Ron Sun Ed., Cambridge University Press, 2023.
Exam(s) in session
Any session
- In-person
written exam AND oral exam
Written work / report
Additional information:
Class presentation (30%). Final project presentation and oral exam (70%).