Vurderingsordning

Faglig innhold

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.Neural data analysis and network models of brain functions are the primary focus of this course. We will review current computational models and how these models continue to develop together with experiments to further our understanding of the brain. The lectures will include the following topics:

1. Principles of single neuron models: from point neurons to Hudgkin-Huxley
2. Network models of memory: Hopfield Network, Attractor Networks of Spiking neurons, Balanced Networks
3. Models of grid cells: Continuous attractor models and single neuron models
4. Neural Coding: Information theory, role of correlated activity

The course involves writing an essay based on one of the topics covered in the course, or a topic agreed with the course coordinator. The essay must be written in the style of an academic article, reviewing the chosen topic, its historical development in relation to experiments, and also discussing at least one result (from published literature) related to the topic but not covered in the course.

The essays are due at the end of the course. The essay will be evaluated as pass/fail. The grade for the course will be determined from a written exam.

Læringsutbytte

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

Læringsformer og aktiviteter

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.

Obligatoriske aktiviteter

• 7 approved short assignments

Mer om vurdering

Students will be given 10 short assignments (ca. 5-10 min to complete one assignment) during the semester related to the course material. The assignments will be evaluated as passed/failed. A passing grade on at least 7 of 10 assignments is required to take the final exam.

Approved compulsory assignments are valid for two academic years.

Written exam (school exam); 4 hour duration

Regular final examination is given in the spring semester only. Students with legitimate leave of absence at the final examination and students who receive the grade F may take a re-sit examination in the autumn semester. In case of only a few candidates, the re-sit examination may be conducted as an oral examination.

Spesielle vilkår

Anbefalte forkunnskaper

• 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).

Forkunnskapskrav

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

Kursmateriell

To be announced.

Studiepoengreduksjon