International Graduate School in Integrated Neuroscience

Courses and seminars

IGSIN presents several courses within neuroscience and statistics

Main content

PhD-Course in functional neuroimaging (fMRI) IGSIN911


Date: Preliminary date for 2023: Week 38, September 15-22. (Note that this date is pending final confirmation)

BB-building 9th floor 


1. Course content

The course is intended to PhD students holding a Master degree in neuroscience, psychology, natural sciences or equivalent, as well as medical students, taking part in a research-training programme in medicine. The course will give an introduction to the field of neuroimaging. The course is divided into three parts.

The first part is a short theoretical introduction into neuroscience and neuroimaging, covering all relevant aspects on physiology, neuroanatomy, some of the most relevant functional networks, as well as the technical aspects behind structural and functional magnetic resonance imaging and related methods, such as DTI and perfusion measurements.

In the second part, the course will introduce the most relevant experimental techniques, used in functional neuroimaging, as well as the methods, used for analysing functional as well as structural MRI data.

The third part is the practical part, where an experiment will be developed, performed on the scanner, analysed, and the results will be discussed.

2. Learning Outcomes

After finishing the course, the students will have basic knowledge of neuroanatomy, and functional neuroimaging approaches. This includes that they learned the technical and physical principles behind magnetic resonance imaging and its different applications, they now what the BOLD effect is and how it could be used for displaying neuronal activations. The students are familiar with the typical experimental fMRI designs, and know the limitations of the different method.

The students got an understanding of the parameters, which are most relevant for designing and fMRI experiment, acquiring and analysing the fMRI data. They will be able to perform the processing of the data and to specify a general linear model, based on the experimental design, they are able to analyse the data within standard fMRI analysis software, and they are able to describe the results in an appropriate way.

3. Course description

Part 1: Introduction to neuroscience

This first part of the course introduces to fundamental knowledge in physiology and neuroanatomy. The course is mostly aimed to repeat these issues rather than introducing them as new knowledge. The content of this part is restricted to those aspects, which are relevant for neuroimaging. These are in particular aspects of configuration of neurons, neuronal signal transmission, physiological basis of the BOLD effect, configuration of the cortex, the different lobes, Brodmann areas, functional division of different brain areas, which are relevant for visual, auditory and sensomotoric processing, as well as some important cognitive networks.  The first part continues by introducing the technical aspects of various measurement techniques, commonly used in the field of neuroscience. The main focus is thereby on MR based techniques, such as ordinary structural imaging, functional imaging, diffusion tensor imaging, perfusion imaging (ASL) and spectroscopy.

Part 2: Planning, performing, and analysing a fMRI study (Theory)

This part of the course introduced to the various experimental techniques, using in functional imaging. These are not only block- and event-related designs, but also parametric designs, resting state studies, longitudinal studies, and clinical applications of fMRI. The selection of the experimental design has also consequences on how the study could be performed on the scanner. The various aspects, which have to be considered, will be explained.

Further, different analysis strategies are introduced. These are mainly based on the general linear model (GLM) or on independent component analyses (ICA). The mathematical backgrounds and how they are implemented will be explained. The reference software for this course will be the matlab-based software ‘statistical parametric mapping’ (SPM) and the ‘group ICA fMRI toolbox’ (GIFT).

Other software packages, such as FSL and BrainVoyager will be mentioned, as well, but all practical tasks will be demonstrated in SPM.

This part of the course concludes with a discussion of the advantages and disadvantages of the methods explained, especially with respect to possible clinical applications.

Part 3: Planning, performing, and analysing a fMRI study (Praxis)

In the practical part of the course, students are encouraged to develop a fMRI study. This study will be performed on the scanner, located at the Haukeland university hospital and the data will be analysed with the methods, introduced in the second part. The practical session is also aimed to be used for discussing actual project. Students may have the possibility to present their own studies and to discuss issues such as experimental design and analysis strategies.

This part of the course concludes with some guidelines on how results should be reported, which tools are available for identifying brain areas, etc.

4. Evaluation

The course is concluded by a written home exam (essay).

5. Course credits


Note that in-person presence is required at least for 80% of the course to obtain any ECTS points. We discourage from registering for the course if this criterion cannot be met

6. Pre-requirements

The course is intended to PhD students that completed a Master degree in neuroscience, psychology, natural science or equivalent, as well as medical students that take part in a research training programme in medicine (forskerlinjen) or equivalent. 

Each student should have access to MATLAB®, preferably on their own laptop.

7. Prior knowledge:        

There is no prior knowledge necessary, but a basic introduction into Neuroscience is recommended.

Those students, more interested in technical aspects of MR imaging are encouraged to visit first a course in MR physics, like course MEDT8011, hold at NTNU.

8. Teaching methods

Lectures, group discussions, group work and practical session

9. Teaching language

The lectures will be held in English and the practical session will be held in Norwegian and English

10. Preliminary date: week 38


11. Course arrangement & Participants


The course will be part of the course program of the International Graduate School in Integrated Neuroscience (IGSIN; https://www.uib.no/rs/igsin). The lectures are open for everyone; the practical session is limited to 15 students.


Contact information

Professor Marco Hirnstein

Registration and administrative questions to Vivian Fosse

Registation deadline: August 15.

Registration information will be announced 




Course in the software package R

2023 Autumn (


BB-building 9th floor 


We have a limit of 15 participants in classroom lectures.



General content

The course gives an introduction into the software package R, which is a powerful tool for performing various types of statistical analyses. In contrast to other software packages for statistical analyses, R is a programming language, which has to be learned like any other programming language. Because of that, R is, on the one hand very flexible, but requires substantial knowledge about the inherent logic of this software and how to use it.

The course will demonstrate how to organise the data for various types of analyses and how the data can be analysed. The course will introduce in the options available within R for performing standard statistical analyses and analyses for special types of studies, like llinear models, mixed-effects analyses, generalized linear models, and Bayesian statistics.

The course will also introduce the various options for creating high-quality and illustrative figures and other outputs within R.

Further, the strengths and limitations of R will be discussed.


Type of course



General learning objectives

After completion of the course, the candidate will be able to prepare datasets and to perform statistical analyses within the software package R.



After completion of the course, the candidate…..

  • Has knowledge of the logical structure of the programming language R
  • Has knowledge of the fundamental commands and data handling
  • Has knowledge of how to organise data and to define appropriate data tables and types
  • Has knowledge of how to program an appropriate statistical analysis
  • Has knowledge of how to analyse longitudinal data
  • Has knowledge of how to treat missing data


After completion of the course, the candidate…..

  • Can perform standard statistical test within R, like t-tests, various types of linear models, non-parametric tests, mixed-effects, and generalized linear models
  • Can read and evaluate the output of statistical analyses performed in R and is able to interpret possible error messages
  • Can create various types of high-quality figures and result tables


    General competence

    After completion of the course, the candidate…..

- Can master how to best perform statistical analyses within R and how to create suitable outputs for publications.


Required previous knowledge: Master Degree in disciplines relevant to educational sciences, psychology and public health.

Recommended previous knowledge: Prior knowledge of any programming language is of advance but not mandatory

Course credits  The course has been approved as an elective 1 ECTS course


Register here: 


Contact information

Professor Marco Hirnstein, marco.hirnstein@uib.no

Administrative questions to Vivian Fosse, vivian.fosse@uib.no


Learning and memory mechanisms in language acquisition


Date:  NOT PLANNED FOR 2023 


Teaching language English

Course summary

Recent findings with artificial languages demonstrate remarkable learning abilities in infants and young children.  This 1-day course, addressing this topic, is divided into 4 sections.  The first section addresses statistical learning and its role in word segmentation and other learning processes.  The second section addresses different approaches to investigating early abstraction abilities.  The third section addresses learning thought to play a role in abstraction of lexical categories.  The fourth section addresses constraints on learning that arise from naturally occurring memory processes of consolidation and reconsolidation. Consolidation, a process stabilizing and even transforming memories during sleep, results in changes in memory traces after initial learning.  Reconsolidation is a process implicated in memory updating.  Both memory processes are important for understanding how children sustain sensitivity to prior knowledge while incorporating new information in complex learning scenarios such as those involved in acquiring language.


Learning outcomes

After completing the course, the participants should be able to discuss learning abilities in infants and young children. They should understand the concept of statistical learning and its role in word segmentation and other learning processes. Further, they should be able to discuss different approaches to investigating early abstraction abilities in young children and the role of learning in abstraction of lexical categories. In addition, you should be able to discuss constraints on learning that arise from naturally occurring memory processes of consolidation and reconsolidation.


Course credits

The course has been approved as an elective 3 ECTS course

Student evaluation

An essay focused on integrative perspectives on early learning and memory of 2000 words is mandatory for course approval.

Registration: latest ........................ to vivian.fosse@uib.no


Course outline


1.   Brief introduction

2.   Statistical learning in word segmentation and beyond

a.   Transitional probabilities and word segmentation

                                     i.    The phenomenon1

                                   ii.    Domain specificity2, 3

                                  iii.    Caveats/concerns/criticisms4

b.   Statistical learning and stress

                                     i.    Pitting stress against statistics5, 6

c.   Scaling up to real-world learning

                                     i.    Successes7,8, 9

                                   ii.    Failures10

d.   Non-adjacent dependency learning11



3.   Learning and abstraction

a.   Statistical learning and phrase structure12

                                     i.    Approximates the demands of language-based abstraction?

b.   Abstraction of stress rules13, 14

                                     i.    The phenomenon

                                   ii.    The role of variability in abstracting rules

                                  iii.    How developing language experience constrains learning

c.   ABA/ABB abstraction

                                     i.    The phenomenon15

                                   ii.    Language specific?16, 17

                                  iii.    Approximates the demands of category-based abstraction?18


12-13:30 — Break for lunch




4.   Category-based abstraction

a.   Frequent frames as a basis for forming categories19

b.   The role of correlated cues in forming categories

                                     i.    Demonstrations of need for

                                   ii.    Evidence for20

                                  iii.    Findings

1.   Easier cases21, 22

2.   Harder cases23

c.   Mapping structure and meaning24, 25

d.   How learners deal with noise in the input21




4.   The role of sleep and memory in learning

a.   Sleep

                                     i.    Sleep-dependent modifications to learning26, 27

                                   ii.    Architecture of sleep as it relates to different types of learning

                                  iii.    Theories of sleep-dependent memory consolidation28

                                  iv.    Implications for language learning

b.   Reconsolidation/Memory updating

                                     i.    The phenomenon29

                                   ii.    Boundary conditions on memory updating

1.   Reactivation

2.   Time

3.   Different for different types of memory?



5.   Conclusions/Integration/Discussion

a.   During this portion of the class, students will have the opportunity to discuss how the principles presented in the course connect with their own research or with their understanding of some aspect of language development, cognitive development, or cognition.  We will also raise outstanding/unanswered questions for discussion.


Suggested readings

* denotes readings recommended for course preparation


1.      Saffran JR, Aslin RN, Newport EL. Statistical learning by 8-month-old infants. Science. Dec 13 1996;274(5294):1926-1928.*

2.      Kirkham NZ, Slemmer JA, Johnson SP. Visual statistical learning in infancy: evidence for a domain general learning mechanism. Cognition. Mar 2002;83(2):B35-B42.

3.      Fiser J, Aslin RN. Statistical learning of new visual feature combinations by infants. P Natl Acad Sci USA. NOV 26 2002;99(24):15822-15826.

4.      Yang CD. Universal Grammar, statistics or both? Trends in Cognitive Sciences. Oct 2004;8(10):451-456.*

5.      Johnson EK, Jusczyk PW. Word segmentation by 8-month-olds: When speech cues count more than statistics. J Mem Lang. May 2001;44(4):548-567.*

6.      Thiessen ED, Saffran JR. When cues collide: Use of stress and statistical cues to word boundaries by 7-to 9-month-old infants. Dev Psychol. Jul 2003;39(4):706-716.

7.      Estes KG, Evans JL, Alibali MW, Saffran JR. Can infants map meaning to newly segmented words? Statistical segmentation and word learning. Psychol Sci. Mar 2007;18(3):254-260.

8.      Pelucchi B, Hay JF, Saffran JR. Statistical learning in a natural language by 8-month-old infants. Child Dev. May-Jun 2009;80(3):674-685.

9.      Pelucchi B, Hay JF, Saffran JR. Learning in reverse: eight-month-old infants track backward transitional probabilities. Cognition. Nov 2009;113(2):244-247.

10.    Johnson EK, Tyler MD. Testing the limits of statistical learning for word segmentation. Developmental Sci. Mar 2010;13(2):339-345.

11.    Gomez RL. Variability and detection of invariant structure. Psychol Sci. SEP 2002;13(5):431-436.*

12.    Saffran J, Hauser M, Seibel R, Kapfhamer J, Tsao F, Cushman F. Grammatical pattern learning by human infants and cotton-top tamarin monkeys. Cognition. May 2008;107(2):479-500.*

13.    Gerken L. Nine-month-olds extract structural principles required for natural language. Cognition. Oct 2004;93(3):B89-96.*

14.    Gerken LA, & Bollt, A. . Three exemplars allow at least some linguistic generalizations: Implications for generalization mechanisms and constraints Language Learning and Development. 2008;4:228-248.

15.    Marcus GF, Vijayan S, Bandi Rao S, Vishton PM. Rule learning by seven-month-old infants. Science. Jan 1 1999;283(5398):77-80.*

16.    Marcus GF, Fernandes KJ, Johnson SP. Infant rule learning facilitated by speech. Psychol Sci. May 2007;18(5):387-391.

17.    Johnson SP, Fernandas KJ, Frank MC, et al. Abstract Rule Learning for Visual Sequences in 8- and 11-Month-Olds. Infancy. 2009;14(1):2-18.

18.    Gomez RL, Gerken L. Infant artificial language learning and language acquisition. Trends in Cognitive Sciences. May 2000;4(5):178-186.

19.    Chemla E, Mintz TH, Bernal S, Christophe A. Categorizing words using 'frequent frames': what cross-linguistic analyses reveal about distributional acquisition strategies. Developmental Sci. May 2009;12(3):396-406.

20.    Farmer TA, Christiansen MH, Monaghan P. Phonological typicality influences on-line sentence comprehension. Proc Natl Acad Sci U S A. Aug 8 2006;103(32):12203-12208.

21.    Gomez RL, Lakusta L. A first step in form-based category abstraction by 12-month-old infants. Dev Sci. Nov 2004;7(5):567-580.*

22.    Lany J, Gomez RL. Twelve-month-old infants benefit from prior experience in statistical learning. Psychol Sci. Dec 2008;19(12):1247-1252.

23.    Gerken L, Wilson R, Lewis W. Infants can use distributional cues to form syntactic categories. J Child Lang. May 2005;32(2):249-268.*

24.    Lany J, Saffran JR. From Statistics to Meaning: Infants' Acquisition of Lexical Categories. Psychol Sci. Feb 2010;21(2):284-291.

25.    Lany J, Saffran JR. Interactions between statistical and semantic information in infant language development. Developmental Sci. Sep 2011;14(5):1207-1219.*

26.    Gomez RL, Bootzin RR, Nadel L. Naps promote abstraction in language-learning infants. Psychol Sci. AUG 2006;17(8):670-674.*

27.    Dumay N, Gaskell MG. Sleep-associated changes in the mental representation of spoken words. Psychol Sci. Jan 2007;18(1):35-39.*

28.    Diekelmann S, Born J. SLEEP The memory function of sleep. Nature Reviews Neuroscience. FEB 2010;11(2):114-126.*

29.    Hupbach A, Gomez R, Hardt O, Nadel L. Reconsolidation of episodic memories: A subtle reminder triggers integration of new information. Learn Memory. Jan-Feb 2007;14(1-2):47-53.*




Introduction to Practical Machine Learning


February 14., 0915 - 1445, Christiesgt. 12, room 005,

February 16., 0915 - 1445, Christiesgt 12, room 002, 

February 21., 0915 - 1445, Christiesgt 12, room 110

February 23., 0915 - 1445, Christiesgt 12, room 555

March 15., 0915-1430, Christiesgt 12, room 002


Teaching language: English

Lecturer: Senior Researcher Mohammad Khalil, SLATE

Course in cooperation with Center for the Science of Learning and Technology (SLATE)

General content

This course gives an intuitive introduction to machine learning both in theory and practice. The course has two main parts. The first part will cover “Machine learning: What is it?” and describe how it allows the discovery of knowledge from data. Since data is the fuel that drives machine learning, we will dive into data management where description of organization, storage, cleansing, filtration, and preparation of data collected and used in research projects will be explained. To handle practical examples of data management and machine learning, R software will be used. R will be approached from a beginner mind-set with a walk through the creation and execution of your first R scripts. In machine learning, multiple topics including data as a source for future decision-making, supervised and unsupervised learning techniques will be covered.

The second part includes a group project where PhD candidates will work together on datasets, either of their choice (i.e., PhD-related) or open datasets, and apply machine learning techniques to distill interesting patterns and knowledge. Candidates will be given 3 weeks to work on the project and present their results in the class.

The course will have a maximum capacity of 20 students.

Type of course


General learning objectives

After completion of the course, the PhD candidates will be able to conceptualise the different types of machine learning. Candidates will be able to begin writing and executing R scripts, filter, manage, and clean data as well as apply machine learning techniques on datasets.

Reading list: Nwanganga, F., & Chapple, M. (2020). Practical machine learning in R. John Wiley & Sons. Other literature will be provided before the class on Mitt UiB


Course credits

The course has been approved as an elective 2 ECTS course

In order to obtain the full 2 ECTS, every participant needs to:

1) attend - in person - at least 80 % of the lectures

2) participate in the group work

3) write a self-reflection note

In case one of the mandatory tasks are not carried out/delivered in time, no ECTS point can be obtained.

Registration latest January 30: Registration form

Contact information:

Course administrator: vivian.fosse@uib.no

Questions regarding course content etc. mohammad.khalil@uib.no