MATLAB Applied to Neuronal Data

Course manual 2025/2026

Course content

Modern neuroscience research demands strong computational skills for working with increasingly complex neural data. This course introduces students to the analytical techniques commonly used in cognitive and systems neuroscience, with a focus on their practical implementation through programming.

Although the course is formally titled MATLAB Applied to Neuronal Data, the current edition reflects an ongoing shift toward Python as the primary language of instruction. This transition is driven by the growing relevance of Python in neuroscience and the broader scientific community. As such, most examples, assignments, and tools used in the course will now be based on Python, with occasional references to MATLAB for context or comparison.

Students will work with real neuroscientific datasets and learn how to implement key techniques in Python, including signal processing, spike train analysis, time–frequency decomposition, and data visualization. The course also emphasizes reproducibility and good coding practices, preparing students to navigate open-source research workflows and collaborative environments.

Topics covered include:

• Python programming fundamentals for neuroscience
• Data handling, visualization, and scientific plotting (e.g., NumPy, Matplotlib, Pandas)
• Signal processing (filtering, Fourier transforms, time–frequency analysis)
• Spike train and LFP analysis (PSTHs, STA, spectral analysis)
• Using Python tools specialized in the analysis of neuronal data, such as MNE
• Interpreting and replicating analysis pipelines from the literature

Study materials

Practical training material

• Hurt, J (2023) A Beginners Guide to Python 3 Programming. Springer Cham. ISBN: 978-3-031-35121-1. (https://link.springer.com/book/10.1007/978-3-031-35122-8#bibliographic-information)

• Cohen, MX (2017) MATLAB for Brain and Cognitive Scientists. MIT Press. ISBN: 9780262035828576 pp. (https://mitpress.mit.edu/books/matlab-brain-and-cognitive-scientists)

• Spronck, P. (2024) The Coder's Apprentice. Learning Programming with Python 3.  
  https://www.spronck.net/pythonbook/  

• McKinney, W. (2022). Python for data analysis: Data wrangling with pandas, NumPy, and Jupyter (3rd ed.). O’Reilly Media.

   

Other

• https://wesmckinney.com/book/

Objectives

• Understand and interpret Python code used in neuroscience applications.
•  Critically evaluate which analytical methodologies are appropriate for different neuroscientific questions.
• Strengthen problem-solving and computational thinking skills as applied to neuroscience data analysis.
• Apply Python programming techniques to process, visualize, and interpret real neuroscientific datasets.
•  Develop modular and reproducible Python code for common analysis tasks in cognitive and systems neuroscience.
• Apply signal processing techniques (e.g., filtering, spectral analysis, time–frequency decomposition) to real neuronal datasets.
• Analyze and visualize spike trains and continuous signals (e.g., LFP, EEG) using open-source Python libraries.

Teaching methods

• Lecture
• Computer lab session/practical training
• Self-study
• Supervision/feedback meeting
• Laptop seminar

The course is organized into lectures (hoorcolleges), laptop seminars, practical training, and self-study. The lectures introduce the key theoretical concepts and tools in Python and show how to apply them to the analysis of neuronal data. The laptop seminars offer guided, hands-on tutorials on configuring and using Python, NumPy, scikit-learn, pandas, and basic analytical approaches for ERP processing, Fourier-based analysis techniques, image processing, and related topics. During the computer lab sessions you will work on dedicated exercises for each topic, and you will be given structured opportunities to schedule self-study hours throughout the course.

Learning activities

Attendance

Assessment

Inspection of assessed work

The manner of inspection will be communicated via the digitial learning environment.

Assignments

Report Computer Practical LFP Analyses

• A report about the activities developed during the first week of the course

Report Computer Practical Spike Analyses

• A report about the activities developed during the second week of the course

Report Computer Practical Calcium Imaging

• A report about the activities developed during the third week of the course

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

Course structure

Additional information

Previous knowledge of basic programming (Python, R, or MATLAB) is desirable, although not compulsory.

Contact information

Coordinator

• C.A. Bosman Vittini

Staff

• Toon Brouwer
• A.W. Franzke
• dr. Jan Willem de Gee
• Lars Kopel
• dr. Anouk Schrantee