Concept in data analysis, Kavli Institute for Systems Neuroscience
Concepts in data analysis 2022
NEVR8011 - Concepts in Data Analysis
Time: Fall semester 2022 (mid-August)
Place: Kavli Institute for Systems Neuroscience
The goal of this course is to provide attendees with an overview of the different techniques and concepts used in the analysis of neural data, and examples of how to apply these tools.
Requirements
This course is meant for Master's students, PhD candidates and postdocs. PhD students will get 7.5 ECTS after completing the course and upon evaluation. Experience with programming and familiarity with concepts in neuroscience, linear algebra, probability theory and calculus are required. Students will be free to write their analysis codes in any programming language, though the lecturers will expect programming questions in Matlab and/or Python. All those who want participate in this course, please contact Soledad (soledad.g.cogno@ntnu.no).
Course description
During this course we will introduce the most standard techniques for analysis of neural data, as well as cutting edge techniques that are becoming very prominent in the analysis of big datasets. Each presented topic will be accompanied by exercises. Lecturers will encourage all attendees to try to implement the introduced techniques to their own data. Time will be allocated for discussions and to provide some guidance.
Course modality
The course will be taught on Zoom. Each lecture will have a theoretical introduction and a practical part with a demonstration of how to apply the introduced techniques to datasets.
Evaluation
Students who want to get 7.5 ECTS will have to hand in exercises that span the different topics covered during the course. Time will be allocated during the course to discuss those exercises. In addition, there will be an oral exam. The course evaluation will be as pass/fail.
Learning outcome
After completing the course the student will:
- Have an overview of techniques currently used to analyze neural data.
- Have experience in analyzing different types of datasets.
- Be able to critically appraise publications about data analysis.
Syllabus
- Dimensionality Reduction
- Clustering / Classification
- Decoders
- Information Theory
- Time Frequency Analysis
- Linear Regression and GLMs
- Learning and Validation
- Bayesian Inference
Important
If you are interested in taking the full course or just attending some of the lectures (both modalities are possible), please contact Soledad so that she can add you to the dedicated mailing list. If you are a PhD student and you want to formally sign up for this course to take 7.5 ECTS, please contact Soledad.