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. L. Geerligs
Other course modules lecturer
Contactperson for the course
dr. L. Geerligs
Other course modules lecturer
dr. L. Geerligs
Other course modules lecturer
Academic year2023
PER4  (08/04/2024 to 12/07/2024)
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-
The aims and setup of this course may be adjusted based on the evaluations of the 2022/2023 edition. 

At the end of the course, as a student, you:
  1. Can explain how techniques from Artificial Intelligence can help understand the brain, and why understanding Natural Intelligence holds promise for creating Artificial Intelligence
  2. Have acquired basic knowledge on the structure and functions of brain regions, neural processing, neuroimaging recording and analysis techniques.
  3. You are able to explain in general terms how the brain’s anatomy and physiology supports cognitive functions such as visual perception, language, attention, acting and memory.
  4. You can explain how specific experimental setups, neuroimaging techniques and analyses are used to gain specific insights in the areas of cognitive neuroscience listed in point 3 and what the limitations of different approaches are.
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 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 and discuss many of the in-depth applications (e.g., Brain-Computer Interfacing, Cognitive computational neuroscience, Cognitive neuropsychology). At the end of the course, you sufficiently understand the current knowledge on the human brain to build upon in future courses.
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. Bonus points can be obtained by answering in-lecture questions.

The final course grade consist of grades for the workgroup assignments (25%), a final exam (75%) and bonus points that can be can be obtained by answering in-lecture questions (10%). The minimum grade for the exam in order to pass the course is 5.5. In addition the average of all grading components should be sufficient.

There is no resit possibility for the workgroup assignments or the bonus points, only for the final exam.

Intermediate grades will be published in Brightspace. Only the final grade will be entered in Osiris.
The course is built on weekly lectures, knowledge clips, workgroups and self-study of corresponding book chapters. The lectures and the workgroups will present some materials that are not covered in the book. The workgroups will give students a chance to discuss exam-style questions about the course materials. By participating the in the quizzes during the lectures, students can earn bonus points. 

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.

Please sign up for any course at (, it is obligatory.

Students who are enrolled for a course are also provisionally registered for the exam. 

Resit: Manual register at ( until five working days prior to the date of the exam. No delayed registration is possible. 

We urge you to always read the course information on Brightspace. 
Required materials
The book that is required for this course is The Student's Guide to Cognitive Neuroscience by Jamie Ward, 4th Edition.
Title:The Student's Guide to Cognitive Neuroscience
Author:Jamie Ward
Publisher:Taylor & Francis Ltd
Course material
The study material consists of the book, knowledge clips and the lectures. Note that some lectures and knowledge clips cover additional information not included in the book.

Instructional modes

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

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 typeDigital exam with CIRRUS
OpportunitiesBlock PER4, Block PER4