Hosted in a different country every year, these events are a great opportunity to meet leading experts from computational neuroscience, neuroscience methodology and experimental neuroscience!
- Theoretical background of large-scale brain network modeling
- Interacting with The Virtual Brain using GUI and command line interface
- Personalization pipelines: Processing of brain images (MRI, fMRI, DTI, PET) andelectrophysiological data (EEG, MEG) for individualization of brain network modeling
- Modeling resting-state networks, brain disorders, mouse, macaque, human brainactivity
- Concepts of nonlinear dynamics (bifurcation analysis, phase plane, manifolds, flowson manifolds)
- Running workflows on high performance computers
- Parameter optimization and model inference
- Application of brain network modeling for clinical questions
- Visualizations of multimodal brain dynamics
- Making use of and contributing to collaborative informatics simulation platformssuch as The Virtual Brain or Human Brain Project’s EBRAINS
- Multiscale co-simulation using The Virtual Brain and microscopic simulators such asNEST
- Architecture of The Virtual Brain simulator
- Basic programming skills in Python
- Good English language skills
- Basic programming expertise
- Dec 3rd 15-16:30
- Dec 17th 15-18:10
- Jan 7th 15-18:10
- Jan 21st 15-18:10
- Feb 4th 15-18:10
- Feb 18th 15-18:10
- Poldrack, Feingold, Frank, Gleeson, de Hollander, Huys, Love, Markiewitcz, Moran, Ritter, Turner, Yarkoni, Zhang, Cohen. (2019) The importance of standards for sharing of computational models and data. Computational Brain & Behavior
- Shen K, Bezgin G, Schirner M, Ritter P, Everling S, McIntosh AR (2019) A macaque connectome for large-scale network simulations in TheVirtualBrain. Nature Scientific Data
- Leon Stefanovski, Paul Triebkorn, Andreas Spiegler, Margarita-Arimatea Diaz-Cortes, Ana Solodkin, Viktor Jirsa, Anthony Randal McIntosh, Petra Ritter; for the Alzheimer’s Disease Neuroimaging Initiative (2019). Linking molecular pathways and large-scale computational modeling to assess candidate disease mechanisms and pharmacodynamics in Alzheimer’s disease. Frontiers Computational Neuroscience
- Schirner, McIntosh, Jirsa, Deco, Ritter (2018) Inferring multi-scale neural mechanisms with brain network modelling. eLife
- Deco, Kringelbach, Jirsa, Ritter (2017) The dynamics of resting fluctuations in the brain: metastability and its dynamical core. Scientific Reports
- Kringelbach, McIntosh, Ritter, Jirsa, Deco (2015) The rediscovery of slowness: exploring the timing of cognition. Trends in Cognitive Science 19(10):616-28
- Schirner, M., S. Rothmeier, V. Jirsa, A. R. McIntosh and Ritter, P. (2015). An automated pipeline for constructing personalised virtual brains from multimodal neuroimaging data. Neuroimage
- The Virtual Brain INCF Course
- Some lecture notes will be available as jupyter notebooks that are accessible and executable via a joint workspace – the EBRAINS Collaboratory of the Human Brain Project.
This course is open for students and scientists outside the Charité. For registration please contact petra.ritter at charite.de
This course provides basic knowledge on personalized brain network modeling and will include both lectures and tutorials. Required interdisciplinary methods will be introduced. A focus will be set on the open-source simulation platform The Virtual Brain
After completing this course, students will know:
Basic concepts and methods for personalized brain network modeling and simulation. Students will gain theoretical knowledge and subsequently use this knowledge to constructmodels, process multimodal imaging data for creating individualized models, run simulations and use supporting neuroinformatics tool such as collaboratories, pipelines, workflows and data repositories. Students will be able to operate the open source neuroinformatics platform The Virtual Brain (TVB).
Description of Teaching and Learning Methods
The lecture part consists of biweekly virtual teaching using the free tool GoToMeeting. In additional to the presentation of theoretical concepts, it comprises several demonstrations of how to operate workflows, simulation engines, high performance computers and collaborative platforms. Participants are expected to practice content after class, using their class notes, digital jupyter notebooks, video tutorials and recommended literature, in preparation for the exercises and tutorials.
Homework assignments are given biweekly and must be solved within two weeks. Homework assignments are exclusively for students who want to earn credits. These assignments cover the different methods of the course and comprise setting up simulations, operating workflows or modifying existing code to address specific scientific problems. Working in small teams of ca. 3 individuals is encouraged. Homework assignments and their solutions are discussed during the hands-on tutorials. In the hand-on tutorials we address specific problems and are solving them together. This requires operating simulation software and informatics tools.
Requirements for Participation and Examination
Registration via email is required: petra.ritter at charite.de
Breaks from 16:30-16:40
Home assignments are exclusively for students who want to earn credits. This course is an official course of the Bernstein Center for Computational Neuroscience Berlin and of the Charité Doctorate Program.
The course is open for students and scientists outside the Charité.
This course is free.