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
Personalization pipelines: Processing of brain images for individualization of brain network modeling
Concepts of nonlinear dynamics
Running workflows on high-performance computers
Parameter optimization and model inference
Application of brain network modeling for clinical questions
Introduction to the medical condition targeted through brain simulation: dementias, psychosis and epilepsy
Visualizations of multimodal brain dynamics, ontologies, machine learning, graph theory
Making use of digital Research Infrastructures used for data integration and simulation in compliance with the EU general Data Protection Regulations (GDPR)
Good english language skills
Basic programming expertise
We are offering a virtual course on The Virtual Brain in Clinical Research this fall.
Dates: October 5th, 2021 to February 1st, 2022
Every Tuesday from 6.00pm to 7.30pm
Registration: The course is free but online registration is required here
Training type: Webinar
Credits: 2 ECTS for Master students at the Bernstein Center Computational Neuroscience Berlin (BCCN) and 3 ECTS for PhD students at the Charité (Promotionsumgebung)
Responsible Person: Prof. Dr. Petra Ritter
Organizers: Dr. Leon Stefanovski, Dr. Konstantin Bülau, Leon Martin, Prof. Dr. Petra Ritter
Office: Robert-Koch Platz 4, Charité Campus Mitte
This module provides basic knowledge on personalized brain network modeling for state-of-the-art clinical research. Required interdisciplinary methods will be introduced. A focus will be set on the open-source simulation platform The Virtual Brain.
Description of Teaching and Learning Methods
The lecture part consists of weekly virtual teaching using the free tool GoToMeeting. In addition 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 rehearse content after class, using their class notes, digital jupyter notebooks, video tutorials and recommended literature.
Requirements for Participation
Find out More
Complete description of the virtual course can be found in the following information sheet below: