course-details-portlet

NEVR8011 - Concepts in Data Analysis

About

Examination arrangement

Examination arrangement: Oral examination
Grade: Passed / Not Passed

Evaluation Weighting Duration Grade deviation Examination aids
Oral examination 100/100

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

After completing the course the student will:

  1. Have a practical understanding of the different techniques that are currently used to analyse neural data.
  2. 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

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.

More on the course

No

Facts

Version: 1
Credits:  7.5 SP
Study level: Doctoral degree level

Coursework

Term no.: 1
Teaching semester:  SPRING 2024

Language of instruction: English

Location: Trondheim

Subject area(s)
  • Neuroscience
Contact information
Course coordinator: Lecturer(s):

Department with academic responsibility
Kavli Institute for Systems Neuroscience

Examination

Examination arrangement: Oral examination

Term Status code Evaluation Weighting Examination aids Date Time Examination system Room *
Autumn UTS Oral examination 100/100
Room Building Number of candidates
Spring ORD Oral examination 100/100
Room Building Number of candidates
  • * The location (room) for a written examination is published 3 days before examination date. If more than one room is listed, you will find your room at Studentweb.
Examination

For more information regarding registration for examination and examination procedures, see "Innsida - Exams"

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