Introduction to Matlab Programming for Neuroscientists

Course manual 2018/2019

Course content

The course will comprise a basic introduction to MATLAB, designed to be suitable for students with little or no prior programming knowledge. The program will be based on introductory lectures and hand-on assignments, with the help and supervision of expert programmers. As far as possible, the work will be based on real data from neuroscience experiments. There will be an introduction of basic techniques to analysis behavioral tracking data, EEG-LFP and single unit electrophysiological data, including spectral analysis techniques, neuronal connectivity measurements and use of statistical tools provided by MATLAB and other developers.

Study materials

Literature

•  Matlab - A Practical Introduction to Programming and Problem Solving from Stormy Attaway, 4th edition

Syllabus

• Link to course manual

Software

• Matlab

Other

• Manual

Objectives

Research in contemporary neuroscience requires a solid foundation in data acquisition and data analysis. This course is aimed to provide students in the Brain and Cognitive Sciences Research Master with a first introduction to programming, with basic applications to acquire and analyze data in neuroscience. We will use the software MATLAB, since it is the most widely used programming platform for neuroscientists in many different specialist fields.

Students who successfully take this course should acquire the following competences,

1. Solve simple programming problems, as they appear in the daily practice of research neuroscientists
2. Apply this knowledge to a simple problem of data acquisition of behavioral data
3. Create a MATLAB based tool to gather behavioral data to answer a self-developed hypothesis
4. Obtain a basic knowledge of several tool-boxes in MATLAB that are used to analyze EEG data

The course will comprise a basic introduction to MATLAB, designed to be suitable for students with little or no prior programming knowledge. The program will be based on introductory lectures and hand-on assignments, with the help and supervision of expert programmers. During the first half of the course the students will be guided to program their first tool to acquire actual neuroscientific data. In the second half students come up with a neuroscientific research question themselves. They will use their new knowledge to create a tool to acquire data to answer this question. Additionally, there will be introductory lectures on so called tool-boxes commonly used for data analysis in neuroscience research.

Teaching methods

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

Every week starts with a lecture to introduce the topics and guide the students through the most challenging parts.

During computer lab sessions the students can practice what they have learned under supervision and guidance based on assignments.

The course ends with a presentation of the final project to a) practice elevator pitches and b) give the students a better idea on possible applications of their knowledge.

During self-study students have to a) acquire new knowledge and in depth knowledge on their own (the lectures only contain the challenging parts and an overview of the topics) and b) have to finish the assignments that couldn't be finished during the computer lab sessions.

Learning activities

Attendance

Requirements of the programme concerning attendance (OER-B):

1. In the case of practicals, the student must attend at least 80%. Should the student attend less than 80%, he/she must redo the practical, or the Examinations Board may have one or more supplementary assignments issued.
2. In the case of study-group sessions with assignments, the student must attend at least 80% of the study-group sessions. Should the student attend less than 80%, he/she must redo the study group, or the Examinations Board may have one or more supplementary assignments issued.
3. The student must attend 80% of the teaching per study unit of the mandatory courses, entry courses and specialisation courses.

Assessment

Assignments

week 1

• short programming assignment in Matlab

week 2

• short programming assignment in Matlab

week 4

• code of the visual search task (group) and report about the programming process (individually)

week 7

• code, article and elevator pitch about self chosen experiment (group) and report about programming process (individually) 

For week 1 and 2 are assignments that are graded on correctness. A short feedback on the most important points will be given on Canvas.

For week 4 and 7 there are rubrics for all parts of the assignments.

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

Timetable

The schedule for this course is published on DataNose.

Additional information

The course is designed to for people with little/no prior programming knowledge. 

Last year's course evaluation

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

• Julia Dawitz MSc