Course - Neural Networks - NEVR3004
NEVR3004 - Neural Networks
About
Examination arrangement
Examination arrangement: School exam
Grade: Letter grades
| Evaluation | Weighting | Duration | Grade deviation | Examination aids |
|---|---|---|---|---|
| School exam | 100/100 | 4 hours | D |
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.
Compulsory assignments
- 7 approved short assignments
Further on evaluation
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.
Specific conditions
Limited admission to classes. For more information: https://i.ntnu.no/wiki/-/wiki/English/Admission+to+courses+with+restricted+admission
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
| Course code | Reduction | From | To |
|---|---|---|---|
| NEVR3030 | 7.5 |
No
Version: 1
Credits:
7.5 SP
Study level: Second degree level
Term no.: 1
Teaching semester: SPRING 2025
Extraordinary deadline for course registration: 2024-12-01
Language of instruction: English
Location: Trondheim
- Computer and Information Science
- Neuroscience
- Biology
- Philosophy
- Physics
- Informatics
- Chemistry
- Medicine
- Psychology
Department with academic responsibility
Kavli Institute for Systems Neuroscience
Examination
Examination arrangement: School exam
- Term Status code Evaluation Weighting Examination aids Date Time Examination system Room *
- Autumn UTS School exam 100/100 D INSPERA
-
Room Building Number of candidates - Spring ORD School exam 100/100 D INSPERA
-
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.
For more information regarding registration for examination and examination procedures, see "Innsida - Exams"