This talk presents state-of-the-art methods for ensuring data privacy with a particular focus on medical data sharing across multiple organizations.
This lecture talks about the usage of knowledge graphs in hospitals and related challenges of semantic interoperability.
This lecture provides an introduction to the Brain Imaging Data Structure (BIDS), a standard for organizing human neuroimaging datasets.
This lesson outlines Neurodata Without Borders (NWB), a data standard for neurophysiology which provides neuroscientists with a common standard to share, archive, use, and build analysis tools for neurophysiology data.
This lecture covers the rationale for developing the DAQCORD, a framework for the design, documentation, and reporting of data curation methods in order to advance the scientific rigour, reproducibility, and analysis of data.
This tutorial demonstrates how to use PyNN, a simulator-independent language for building neuronal network models, in conjunction with the neuromorphic hardware system SpiNNaker.
In this talk the speakers will give a brief introduction of the Fenix Infrastructure and Service Offering, before focusing on Data Safety. The speaker will take the participants through the ETHZ-CSCS offering for EBRAINS and all the HBP Communities highlighting the Infrastructure role in a service implementation in respect of Security. Particular attention will be on showing what tools ETHZ-CSCS provides to a Portal/Service provider such as EBRAINS, MIP/HIP, TVB, NRP amongst others. Finally there will be given a quick glimpse into the future and the role that “multi-tenancy” will play.
This lecture gives an overview of how to prepare and preprocess neuroimaging (EEG/MEG) data for use in TVB.
This lesson describes the principles underlying functional magnetic resonance imaging (fMRI), diffusion-weighted imaging (DWI), tractography, and parcellation. These tools and concepts are explained in a broader context of neural connectivity and mental health.
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 continues from part one of the lecture Ontologies, Databases, and Standards, diving deeper into a description of ontologies and knowledg graphs.
This lecture focuses on ontologies for clinical neurosciences.
This lesson introduces population models and the phase plane, and is part of the The Virtual Brain (TVB) Node 10 Series, a 4-day workshop dedicated to learning about the full brain simulation platform TVB, as well as brain imaging, brain simulation, personalised brain models, and TVB use cases.
This lesson introduces TVB-multi-scale extensions and other TVB tools which facilitate modeling and analyses of multi-scale data.
This lecture delves into cortical (i.e., surface-based) brain simulations, as well as subcortical (i.e., deep brain) stimulations, covering the definitions, motivations, and implementations of both.
This lecture provides an introduction to entropy in general, and multi-scale entropy (MSE) in particular, highlighting the potential clinical applications of the latter.
In this lecture, you will learn about various neuroinformatic resources which allow for 3D reconstruction of brain models.
In this lesson, you will hear about some of the open issues in the field of neuroscience, as well as a discussion about whether neuroscience works, and how can we know?
This lesson characterizes different types of learning in a neuroscientific and cellular context, and various models employed by researchers to investigate the mechanisms involved.
In this lesson, you will learn about different approaches to modeling learning in neural networks, particularly focusing on system parameters such as firing rates and synaptic weights impact a network.