Neural Networks

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.

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

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.