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 project, the course involves writing an essay based on one aspect of the project, 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. The grade for the course will be determined from the project results and demonstration.

Learning outcome

This course is designed to include the following learning outcomes of the neuroscience master program (https://www.ntnu.edu/studies/msneur/objectives):

Knowledge
The student will be able to
• demonstrate knowledge of the research fields in neuroscience including its subareas; Systems and Computational Neuroscience.
• have knowledge about relevant methodologies and techniques in neuroscience including classical as well as more recent techniques.
• demonstrate knowledge of sensory systems and motor systems.
• have knowledge about association cortex both definitions and different levels such as prefrontal, parietal and temporal cortex.
• demonstrate knowledge of current theoretical concepts in neuroscience, and can apply this to his/her own research
• have knowledge about relevant historical perspectives in neuroscience, its traditions and the position in the society.

Skills
The student will be able to
• analyse existing theories and main outstanding issues in the field of neurosciences.
• find relevant methods, recognize and validate problems; formulate and test hypotheses.
• evaluate and formulate a theoretical concept. Evaluation includes originality, independence and applicability.
• perform a research project with supervision including the formulation of a research question, analyse experimental results, put them in a context and make a report.

General competence
The student will develop
• competence on how to carry out research independently and knows how to formulate and express results and interpretations of the research outcomes.
• capabilities to carry out and analyse complex experiments in neuroscience.
• competence to summarize, document, report, and reflect on own findings.
• competence to contribute to the generation of new ideas, concepts and technical approaches to experimental research questions.

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

Further on evaluation

Obligatory assignment: Essay.
Passed essay is a requirement for the project to be graded. Passed essay is valid for two academic years.

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