This lesson describes spike timing-dependent plasticity (STDP), a biological process that adjusts the strength of connections between neurons in the brain, and how one can implement or mimic this process in a computational model. You will also find links for practical exercises at the bottom of this page.
This lesson discusses a gripping neuroscientific question: why have neurons developed the discrete action potential, or spike, as a principle method of communication?
This tutorial covers the fundamentals of collaborating with Git and GitHub.
This lecture and tutorial focuses on measuring human functional brain networks, as well as how to account for inherent variability within those networks.
This lesson provides an overview of Jupyter notebooks, Jupyter lab, and Binder, as well as their applications within the field of neuroimaging, particularly when it comes to the writing phase of your research.
This lecture introduces you to the basics of the Amazon Web Services public cloud. It covers the fundamentals of cloud computing and goes through both the motivations and processes involved in moving your research computing to the cloud.
This lecture covers how you can make your data public through EBRAINS. This talk focuses on the ethical considerations for sharing data, the requirements that are imposed by various regulations, particularly for sharing human data. The lecture also includes a discussion of how EBRAINS designs its services to deal with the ethical and regulatory aspects of sharing these kinds of data.
This lecture discusses differential privacy and synthetic data in the context of medical data sharing in clinical neurosciences.
This talk presents state-of-the-art methods for ensuring data privacy with a particular focus on medical data sharing across multiple organizations.
The Medical Informatics Platform (MIP) is a platform providing federated analytics for diagnosis and research in clinical neuroscience research. The federated analytics is possible thanks to a distributed engine that executes computations and transfers information between the members of the federation (hospital nodes). In this talk the speaker will describe the process of designing and implementing new analytical tools, i.e. statistical and machine learning algorithms. Mr. Sakellariou will further describe the environment in which these federated algorithms run, the challenges and the available tools, the principles that guide its design and the followed general methodology for each new algorithm. One of the most important challenges which are faced is to design these tools in a way that does not compromise the privacy of the clinical data involved. The speaker will show how to address the main questions when designing such algorithms: how to decompose and distribute the computations and what kind of information to exchange between nodes, in order to comply with the privacy constraint mentioned above. Finally, also the subject of validating these federated algorithms will be briefly touched.
This talks presents ethics requirements of the Medical Informatics Platform, a data sharing platform for medical data using data federation mechanisms. The talk presents how the Medical Informatics Platform (MIP) works and which ethical requirements need to be considered when working with federated data.
This lecture discusses risk-based anonymization approaches for medical research.