Course infoSchedule
Course moduleSOW-BKI136
Credits (ECTS)3
CategoryB1 (First year bachelor)
Language of instructionEnglish
Offered byRadboud University; Faculty of Social Sciences; Artificial Intelligence;
dr. T.C. Kietzmann
Other course modules lecturer
Contactperson for the course
dr. T.C. Kietzmann
Other course modules lecturer
dr. T.C. Kietzmann
Other course modules lecturer
Academic year2020
PER3  (25/01/2021 to 04/04/2021)
Starting block
Course mode
RemarksPlease sign up for any course at (, it is obligatory.
Registration using OSIRISYes
Course open to students from other facultiesYes
Waiting listNo
Placement procedure-
At the end of the course, as a student, you
  • acquired basic knowledge on brain topography and vocabulary, neural processing, neuroimaging and advanced analysis techniques.
  • acquired basic understanding in various areas of cognitive computational neuroscience, including
    • visual processing
    • decision making
    • neural plasticity
    • computational modelling
  • will know how techniques from Artificial Intelligence can help understand the brain, and why understanding Natural Intelligence holds promise for creating Artificial Intelligence
  • can organize, understand, and learn a large set of information and self-organize your learning individually, in teams, and in the class as a whole.
Understanding the human brain is one of the biggest scientific challenges of all time. It promises to bring us closer to understanding who we are, how the orchestrated activity of billions of neurons in our head give rise to our perception of the outside world, to our hopes, fears, thoughts, and dreams. Moreover, understanding functional mechanisms of the brain will ultimately help us cure neural diseases.
Understanding the brain, however, is not an easy task. After all, it is the most complex and intricate ‘machine’ on the planet, and the only existence proof of a system that can generate intelligent behaviour. In this lecture series, you will be introduced to basic principles of brain function, and learn how today’s brain science uses advanced experimental and analysis techniques to generate insight into cognitive function. This involves the application of analysis techniques from the domain of artificial intelligence and machine learning, which enable researchers to look for patterns in high-dimensional neural data that cannot be found with more traditional techniques.
While machine learning is an essential aspect of today’s brain science, the reverse holds true, too. Many aspects of today’s Artificial Intelligence were inspired by insights into brain function, ranging from artificial neural networks to cognitive robotics. Perhaps not surprisingly, many tech companies (including Google Apple, Deepmind, Microsoft Research, Facebook, Samsung, IBM, etc.). invest heavily into the interdisciplinary field of neuroscience and artificial intelligence. Neuroscience in A.I. is a hot topic, and a training in A.I. may be incomplete without a basic understanding of neural function.
This course will allow you to acquire basic knowledge from the domain of cognitive computational neuroscience. Although the scope of this single course is limited, you will find inspiration in our understanding of the brain which will be useful for your future work in A.I., and enable you to work successfully in interdisciplinary teams.
Follow-up courses will dive deeper in many of the in-depth applications (e.g., Brain-Computer Interfacing, Neural Networks, Computational and Formal Modelling). At the end of the course, you sufficiently understand the current knowledge on the human brain to build upon in future courses and your career.
Presumed foreknowledge
There are no special prerequisites.
Test information
The course will end with a written exam covering topics from the lecture and background reading. Additional points can be obtained by answering in-lecture questions and through active participation in the workgroups.

Exam                                    75 points
Workgroup presentations    25 points
Total                                    100

To pass the course, you need to acquire 42 points in the exam, and a total of 56 points.
The course is built on weekly lectures, workgroups and self-study of corresponding book chapters. The lecture will present materials that are not covered in the book and vice versa. The workgroups will give students a chance to ask questions about the course materials. In addition, groups of students can collect points towards the final exam by preparing and presenting answers to questions that are made available at the end of the lecture.

The final lecture of the course will be a Q&A session to help students prepare for the exam. It will revisit lecture materials and answer open questions posed by the students.
Required materials
Course material
Note that the lectures cover additional information not included in the book.

Recommended materials
An Introduction to Brain and Behavior 5th Edition, by Bryan Kolb, Ian Q. Whishaw, G. Campbell Teskey. The 4th edition is not recommended, as it differs significantly on a few topics.

Instructional modes

The course consists of 7 lectures (6 content lectures and one wrap-up session).

Attendance MandatoryYes

Working group
Attendance MandatoryYes

Contribution to group work
Attendance and presentations at the workgroup is required to obtain points that affect the final grade.

The course consists of 4 workgroup meetings.

Final grade
Test weight100
Test typeExam
OpportunitiesBlock PER3, Block PER4