NEVR8011 - Konsepter i Dataanalyse

Om emnet


Vurderingsordning: Oral Presentation
Karakter: Bestått/ Ikke bestått

Vurdering Vekting Varighet Delkarakter Hjelpemidler
Oral Presentation 100/100

Faglig innhold

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, Information Theoretic methods, Fourier and wavelet analysis and classification techniques. 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.


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.

Læringsformer og aktiviteter

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.

Obligatoriske aktiviteter

  • Exercises

Mer om vurdering

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.

Spesielle vilkår

Obligatorisk aktivitet fra tidligere semester kan godkjennes av instituttet.


Programming. Familiarity with concepts in neuroscience, linear algebra, probability theory and calculus. The course is meant for PhD students, master students in Neuroscience must get the approval from their program coordinator for enrolling in this course.


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.

Flere sider om emnet


Fakta om emnet

Versjon: 1
Studiepoeng:  7.5 SP
Studienivå: Doktorgrads nivå


Termin nr.: 1
Undervises:  HØST 2022

Undervisningsspråk: Engelsk

Sted: Trondheim

  • Nevrovitenskap


Vurderingsordning: Oral Presentation

Termin Statuskode Vurdering Vekting Hjelpemidler Dato Tid Eksamens- system Rom *
Høst ORD Oral Presentation 100/100
Rom Bygning Antall kandidater
Vår UTS Oral Presentation 100/100
Rom Bygning Antall kandidater
  • * Skriftlig eksamen plasseres på rom 3 dager før eksamensdato. Hvis mer enn ett rom er oppgitt, finner du ditt rom på Studentweb.

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