Neurophysiology

Neurophysiology: Introduction to Electrophysiology and Imaging

Course manual 2025/2026

Course content

This course gives an overview of modern approaches in (cognitive) neuroscience. These methods integrate across different scales of analysis and understanding: from large-scale measurements of brain-wide networks in humans to small-scale measurements and manipulations of neural circuits and individual neurons in animals. Each method will be explained from the ground up, covering: the principles of the signals we are recording in the brain, the raw data we acquire, the analyses we can perform, and the conclusions we can finally draw.

The neurophysiological methods we will cover can be subdivided into two categories:

1) Methods that answer questions at the regional, inter-regional and systems level, which are (generally) not invasive and thus are mainly conducted in humans (ECoG; EEG; fMRI).

2) Methods that answer questions at the molecular and cellular level and which are more invasive and thus mainly use animal models (extracellular recordings; 2-photon imaging).

Next to these base methods, we will touch on
- Advanced imaging techniques (DTI, fMRS)
- Computational modeling of neurophysiological data
- Ways to perturb brain functioning, which permit asking causal questions (optogenetics, deep brain stimulation)

Each lecture is given by an experienced researcher who actively uses the method that he/she will discuss with the class. The lecturers will explain: (i) what aspect of brain activity a particular method measures or manipulates, (ii) the advantages and disadvantages of the method, (iii) what type of questions can be answered by using the method, (iv) experimental designs used to incorporate the method, and (v) common applications of the method.

Students will apply their understanding in a weekly journal club. In the weekly journal clubs, one host-group will lead the discussion of a relevant paper to the weeks materials, while the rest of the group get a redacted version of the paper without the author's interpretations. Through open discussions the students will interpret the paper, and critically evaluate its conclusions.

Study materials

Literature

• For each lecture :

  - 1 review paper and/or book chapter

  - 1 original research paper.

  The details of the reading material will be made available via Canvas.

Practical training material

Objectives

• Explain principles underlying common cognitive neuroscience methods
• Select appropriate cognitive neuroscience methods for specific research questions.
• Interpret analyses of cognitive neuroscience data.
• Discuss design choices in state-of-the art cognitive neuroscience research.
• Integrate physiological and psychological findings to address cognitive neuroscience questions.

Teaching methods

• Lecture
• Self-study
• Seminar
• Presentation/symposium
• Computer lab session/practical training

Lectures: Will cover all the basics required for student to independently and critically think about each method.

Journal clubs: actively participating in the journal clubs will allow students to critically assess published research findings 

Self-study: will cover all the basics required for student to independently and critically think about each method.

Learning activities

Attendance

Additional requirements for this course:

Lectures: Not mandatory but highly recommended.

Journal clubs: Students can miss one of six journal clubs. Missing more than one journal club means the student fails this part of the course and needs to either redo it next year or the student will be given an alternative assignment. Absence needs to be communicated to the course coordinator.

Assessment

Grading matrices/rubrics and criteria for each of the graded component will be uploaded on Canvas at the beginning of the course.

The grade will be made up by the following components:

Partial exam 1: 35%

Partial exam 2: 65%

The exam grades are determined in direct proportion to the points obtained. For example, a student who obtains 83 out of a 100 points will score an 8.3 for the exam.

Students will receive written feedback on the first draft of their assignment. The grade for the final draft is determined based on the rubric (on Canvas). Not meeting the deadlines (for the first or final drafts) means 1 point is deducted from the assignment grade. 

Journal clubs: Pass / fail based on overall engagement across journal clubs.

Table of how the course objectives are assessed:

In order to pass the course, the final weighted grade (partial exam 1 + partial exam 2) must be >=5.5, and the grade for the journal clubs must be a "pass".  

In case the average grade for the exams is <5.0, students will have the opportunity to attend the resit. The resit will combine topics covered in the partial exam 1 and partial exam 2 and will account for 80% of the course grade.

Inspection of assessed work

Contact the course coordinator via email (j.w.degee@uva.nl) to make an appointment for inspection.

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

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. Jan Willem de Gee

Staff

• C.A. Bosman Vittini
• dr. T.K. Knapen
• dr. Jorge Mejias
• Umberto Olcese
• prof. dr. C.M.A. Pennartz
• dr. Anouk Schrantee
• Margot Steijger MSc
• dr. T. Nandi