Examination arrangement

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 a project (assignment) which will be supported by a series of lectures on the relevant theoretical concepts surrounding the problem, with some class meetings reserved for interactive discussions about the project 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 teacher assistant will generally expect programming questions in Matlab.

In addition to the project an essay on one aspect of the project (of the students choosing) must be submitted. Further information on the project and requirements will be given at the onset of the course.

Compulsory assignments

• Essay

Specific conditions

Recommended previous knowledge

NEVR2010 (Introduction to neuroscience) or equivalent background. 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) as well as basic programming (e.g. Python or Matlab) are strongly recommended. The book Matlab for Neuroscientists, by Pascal Wallisch and others, is a convenient resource for students less familiar with programming or data analysis.

Course materials

To be announced.

Credit reductions