Konsepter i Dataanalyse

During this course we will introduce the most standard techniques for the analysis of neural data, starting from their principles and highlighting strengths and limitations of each of the approaches. The topics of the course can be divided into two main modules: 1) Non-parametric or exploratory data analysis and 2) Parametric data analysis or statistical learning with models. Module 1 includes dimensionality reduction techniques, such as PCA, and Information Theory. Module 2 includes the simple linear regression model (and GLMs), methods for model inference and validation, model selection and decoding, Bayesian inference. Each presented topic will be accompanied by exercises, which will be introduced and partly worked through in class. Note that the focus of the course will be on neural data analysis.

Knowledge

After completing the course, the student will have a foundational and practical understanding of the different techniques that are currently used to analyse neural data.

Skills

After completing the course, the student will have the skills to analyse neural data in different ways, both from a single-cell and a neuronal population perspectives.

Competence

After completing the course, the student will be able to critically appraise publications about data analysis.

Each lecture day will be divided into a theoretical and a practical part. In the theoretical part the workings of the methods in data analysis will be explained through definitions, examples and clear statement of the assumptions. The practical part will consist of applying the introduced techniques to data that will be provided to the students. Students will be free to program in the language of their choice, though the lecturers will expect programming questions in Matlab or Python.

The course will be cancelled if few students sign up