Examination arrangement

Course content

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 Theoretic methods. 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.

Learning outcome

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.

Learning methods and activities

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.

Compulsory assignments

• Exercises

Further on evaluation

For the evaluation the students will be required to hand in all the exercises discussed during the course. During the final oral exam the students will be required to further discuss the exercises and analysis they performed during the course with the lecturers and external evaluator. The final mark will be based on the student’s performance in the exercises and in the final oral exam. The evaluation will be as pass/fail.

Exam registration requires that class registration is approved in the same semester. Compulsory activities from previous semester may be approved by the department.

Required previous knowledge

Admission requirements: The student must be either enrolled in a PhD programme, be a Medical student, be enrolled in the Student Research Programme or be enrolled in a MSc programme at NTNU. Candidates enrolled in the Master in Neuroscience programme at NTNU have to be assessed individually by the course coordinator.

The students are required to have knowledge about calculus and/or linear algebra. Therefore, having approved either NEVR8012 or NEVR8015, or a similar course such as TMA4100 or TMA4110, is mandatory. The students with a degree in Physics or Mathematics are exempted from this requirement. Having familiarity with neuroscience and programming is highly recommended, but not a strict requirement.

Course materials

1. Hastie T, Tibshirani R, Friedman J. The elements of statistical learning: data mining, inference, and prediction. Springer Science & Business Media; 2009 Aug 26.

2. Gerstner W, Kistler WM, Naud R, Paninski L. Neuronal dynamics: From single neurons to networks and models of cognition. Cambridge University Press; 2014 Jul 24.

3. Kass RE, Eden UT, Brown EN. Analysis of neural data. New York: Springer; 2014 Jul 8.

4. Agresti A. Foundations of linear and generalized linear models. John Wiley & Sons; 2015 Jan 15.

5. MacKay DJ, Mac Kay DJ. Information theory, inference and learning algorithms. Cambridge university press; 2003 Sep 25.

6. Gregory P. Bayesian Logical Data Analysis for the Physical Sciences: A Comparative Approach with Mathematica® Support. Cambridge University Press; 2005 Apr 14.

7. Sox H, Higgins MC, Owens DK. Medical Decision Making. Wiley; 2013.