NEVR3004 - Neural Networks

About

Examination arrangement

Examination arrangement: Portfolio assessment and Oral exam
Grade: Letters

Evaluation form Weighting Duration Examination aids Grade deviation
Oral examination 10/100 15 minutes C
Portfolio assessment 90/100 ALLE

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. To this end, each topic in the course will be accompanied by a project to test model predictions with actual neural data (the data will be provided to the students).

In addition to the topic projects, the course involves writing an essay based on one of the projects, written in the style of a research article and should include the student's own results.

Both the project results and essay are due at the end of the course. The essay will be evaluated as pass/fail while the grade for the course will be determined from the project results and demonstration.


Learning outcome

After completing and passing the course NEVR3004 the student 1) has an understanding of current neural network models; 2) can read and critically appraise publications dealing with modeling and analysis of neural network properties; 3) can complete basic data analysis to test predictions from neural network models; 4) can search and compare relevant sources of information to acquire literacy in basic neuroscience; 5) can critically appraise sources of information and contents of scientific publications and choose relevant information; 6) can report outcomes of research in a coherent written report that meets requirements of a scholarly publication.

Learning methods and activities

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

The course will consist of 5 projects, depending on the extent of the individual projects. Each project will be supported by a series of lectures on the relevant theoretical concepts surrounding the problem, while some class meetings will be reserved for interactive discussions about the projects and individual student progress. In addition to the class meetings, a teacher’s assistant will be available for 30 hours during the semester, with scheduling to be determined together with the students. Students will be free to program in the language of their choice, though the teachers assistant will generally expect programming questions in Matlab.

In addition to the topic projects, an essay on one of the projects (of the students choosing) must be handed in through it's learning. Further information on the projects and requirements will be given at the onset of the course.

Compulsory assignments

  • Essay

Further on evaluation

In the resit exam there will be given 5 new assignments as well as an oral exam to adjust the grade.

Specific conditions

Limited admission to classes.

Exam registration requires that class registration is approved in the same semester. 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
NEVR8010 4.0 2007-09-01 2011-08-31

Timetable

Detailed timetable

Examination

Examination arrangement: Portfolio assessment and Oral exam

Term Statuskode Evaluation form Weighting Examination aids Date Time Room *
Autumn UTS Oral examination 10/100 C 2017-12-07 09:00
Spring ORD Oral examination 10/100 C
Autumn UTS Portfolio assessment 90/100 ALLE
Spring ORD Portfolio assessment 90/100 ALLE
  • * 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.