Computational Cognitive Neuroscience
Course infoSchedule
Course moduleSOW-MKI49
Credits (ECTS)6
Language of instructionEnglish
Offered byRadboud University; Faculty of Social Sciences; Artificial Intelligence;
prof. dr. M.A.J. van Gerven
Other course modules lecturer
prof. dr. M.A.J. van Gerven
Other course modules lecturer
Contactperson for the course
L.E.C. Jacques
Other course modules lecturer
Academic year2017
PER1-PER2  (04/09/2017 to 04/02/2018)
Starting block
Course mode
Registration using OSIRISYes
Course open to students from other facultiesNo
Waiting listNo
Placement procedure-
After successful completion of the course, students:
  • Know about current developments and debates regarding the modeling and understanding of natural intelligence
  • Are able to implement computational models of cognitive processs in the Python programming language
  • Can conduct independent research in computational cognitive neuroscience
A main objective of artificial intelligence is to build machines whose cognitive abilities match (or surpass) those of humans. This is also referred to as strong AI. One way to achieve this goal is by developing cognitive architectures that implement the algorithms used by our own brains. This success of such an approach relies on a continuous interplay between AI and neuroscience.
In this course, we will explore how computational models, particularly neural networks, can yield new insights about the mechanisms that give rise to natural intelligence and provide us with the tools to model cognitive processes in artificial systems.
The course consists of different components: 
  • During the lectures, students will get acquainted with the formal aspects and practical development of computational models of cognitive processes. They will learn about the current state of research concerning the modeling and understanding of natural intelligence. 
  • Students will present key papers on the state of the art in class themselves. 
  • During the practical sessions, students will learn to write computer programs related to specific topics discussed in class. To this end, the Python programming language will be used. 
  • In the final part of the course, students will formulate their own research project. The outcome of the research project should be a working computational model accompanied by a NIPS style conference paper that provides original insights about cognitive processing in artificial and/or biological agents. 

Test information
Practical assignments + final project

The final grade is based on active participation during the lectures (20%), the practical grades (50%) and the grade for the final project (30%). Attendance of lectures and student presentations is mandatory.

• Basic Python programming experience (preferably including object-orientation)
• Mathematical skills at the AI Bachelor level (probability theory, calculus and linear algebra)
• Preferably some prior knowledge of neural networks and neuroscience

Contact information
Dr. M.A.J. van Gerven, T: 024 3652354, E:

Recommended materials
Literature list
Selected papers

Instructional modes

Practical sessions


Test weight1
OpportunitiesBlock PER2, Block PER3