course-details-portlet

NEVR3004 - Neural Networks

About

Examination arrangement

Examination arrangement: Home examination
Grade: Letters

Evaluation Weighting Duration Grade deviation Examination aids
Home examination 100/100 4 hours

Course content

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. Basic programming in MATLAB
  2. Basic neural network models
  3. Neural Network models of memory: Hopfield Network, Continuous attractor models
  4. Neural Coding: tuning curves, neural coding and decoding, dimensionality reduction techniques.

The course involves doing a project and writing a project report 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.

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.

Learning outcome

After successfully passing the course NEVR3004, the student will be able to

  • understand basic programming concepts and use them for neural data analysis and neural network modelling.
  • understand the role of quantitative approaches to neural data analysis and neural modelling.
  • approach relevant methods and theories that are useful for his/her field of research.
  • grasp the historical steps taken for the development of major themes in theoretical.

Learning methods and activities

The course is taught in the second half of the spring semester. The language of teaching and evaluation is English.

The course will consist of a set of lectures and a teacher assistant will be available during the semester at times to be determined together with the students. In addition to the final exam an essay on one aspect of the course (of the students choosing) must be submitted.

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

  • Essay

Further on evaluation

Compulsory assignment: Project assessed based on written project report (essay). Passed essay is a requirement for the students to take the final written exam. Approved obligatory assignment essay is valid for four semesters.

Specific conditions

Limited admission to classes.

Compulsory activities from previous semester may be approved by the department.

Course materials

To be announced.

Credit reductions

Course code Reduction From To
NEVR3030 7.5
More on the course

No

Facts

Version: 1
Credits:  7.5 SP
Study level: Second degree level

Coursework

Term no.: 1
Teaching semester:  SPRING 2022
Extraordinary deadline for course registration: 2021-12-01

Language of instruction: English

Location: Trondheim

Subject area(s)
  • Computer and Information Science
  • Neuroscience
  • Biology
  • Philosophy
  • Physics
  • Informatics
  • Chemistry
  • Medicine
  • Psychology
Contact information
Course coordinator: Lecturer(s):

Department with academic responsibility
Kavli Institute for Systems Neuroscience

Timetable

List view
Calendar view

Examination

Examination arrangement: Home examination

Term Status code Evaluation Weighting Examination aids Date Time Examination system Room *
Autumn UTS Home examination 100/100

Release
2021-11-30

Submission
2021-11-30


09:00


13:00

INSPERA
Room Building Number of candidates
Spring ORD Home examination (1) 100/100

Release
2022-06-01

Submission
2022-06-01


15:00


19:00

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.
  • 1) Merk at eksamensform er endret som et smittevernstiltak i den pågående koronasituasjonen.
Examination

For more information regarding registration for examination and examination procedures, see "Innsida - Exams"

More on examinations at NTNU