Course content

Neural data analysis and neural network models are the primary focus of this course. We will cover current computational models and discuss how these models continue to develop together with experiments to further our understanding of the brain. Lectures will include topics such as neural coding and decoding, information theory, dimensionality reduction, attractor networks for memory and navigation, introduction to programming, and analysis of neural data.

Learning outcome

After successfully passing the course, the student will have achieved the following:

Knowledge:

• understand the foundations of models and their application to the brain
• gain familiarity with concepts such as neural coding/decoding, information processing in the brain, and attractor neural networks

Skills:

• perform basic analysis and interpretation of neural data
• critically evaluate quantitative methods and identify underlying assumptions

General competence:

• understand the role of quantitative approaches to neural data analysis and neural modelling
• understand the relationship between major theoretical concepts in neuroscience and experimental data
• approach methods and theories that are useful for their field of research

Learning methods and activities

The course is taught in the Spring semester. The language of teaching and evaluation is English.

The course will consist of lectures, participation in group-based work/discussions, and short assignments.

This course has restricted admission. Students admitted to the MSc in Neuroscience are guaranteed a seat. Other students must apply for a seat by the given deadlines.

Specific conditions

Recommended previous knowledge

• Neuroscience: NEVR2010(Introduction to Neuroscience) or equivalent. We recommend taking this course in parallel with NEVR3003 (Behavioral and cognitive neuroscience).
• Mathematics: Familiarity with preliminary concepts in mathematics (see e.g. appendices A1 and A2 of the book Neural Networks and Brain Function by Rolls and Treves).
• Programming: Basic programming will be taught in the course, but because learning to program requires practice, it is strongly recommended that the student acquire programming skills prior to the course. The following topics are most relevant: variables, data types, control flow, and plotting. Numerous resources are available for free online (e.g. MathWorks introduction videos, Intro to MATLAB course from UCL).

Required previous knowledge

Admission to a programme of study is required:Msc in Neuroscience

Limited admission to classes:

Students not enrolled in MSc in Neuroscience may be individually assessed for a seat if they have a relevant background in accordance with the admission criteria to the MSc in Neuroscience programme: https://www.ntnu.edu/studies/msneur/admission More information about restricted admission: https://i.ntnu.no/wiki/-/wiki/English/Admission+to+courses+with+restricted+admission

Course materials

To be announced.

Credit reductions