The main focus of the course will be to understand and design neural models at multiple scales, ranging from cellular and circuit models to large-scale brain networks.

The goal of the course is to teach different techniques of computational neuroscience spanning different scales:

Day 1: Single-neuron models (Idan Segev, HUJI)
Day 2: Mean-field models (Alain Destexhe, CNRS UNIC EITN)
Day 3: Model comparison and validation (Sonja Grün, FZ Jülich and Gaute Einevoll, NMBU)
Day 4: From local circuits to whole-brain models (Markus Diesmann, FZ Jülich and Viktor Jirsa, AMU)
Day 5: Resting state and sensory processing (Gustavo Deco, UPF)

The students will be asked to form 5 interdisciplinary working groups on each one of these themes, and will do a short modeling project using one of the simulation packages presented at the course (Python, NEST, Brian, The Virtual Brain...) Tutorials and use cases will be provided to teach students how to use these packages.

The student selection will be based on their excellence, and also based on their need to follow such a course.  Experimentalists and theoreticians are welcome, a prior background in basic neuroscience is required.  We request that the students come to the course with their laptop computer and have software installed, which we will identify prior to the course, to allow working on the projects and tutorials.

The deadline for applications is February 15th, 2018.  Applications consist of a one-page CV and a motivation letter to explain why you would like to attend this course.