Tools and services tutorials
The single entry point to tutorials for EBRAINS tools and services. Here, you can find a list of EBRAINS offerings, sorted by topic, and links to their tutorials.
Discover simulation resources below.
The single entry point to tutorials for EBRAINS tools and services. Here, you can find a list of EBRAINS offerings, sorted by topic, and links to their tutorials.
Modeling in neuroscience occurs at the intersection of different points of view and approaches. Typically, hypothesis-driven modeling brings a question into focus so that a model is constructed to investigate a specific hypothesis about how the system works or why certain phenomena are observed. Data-driven modeling, on the other hand, follows a more unbiased approach, with model construction informed by the computationally intensive use of data.
Interplay between the second messengers cAMP and Ca2+ is a hallmark of dynamic cellular processes. A common motif is the opposition of the Ca2+-sensitive phosphatase calcineurin and the major cAMP receptor, protein kinase A (PKA). Calcineurin dephosphorylates sites primed by PKA to bring about changes including synaptic long-term depression (LTD).
Dynamical models describing intracellular phenomena are increasing in size and complexity as more information is obtained from experiments. These models are often over-parameterized with respect to the quantitative data used for parameter estimation, resulting in uncertainty in the individual parameter estimates as well as in the predictions made from the model. Here we combine Bayesian analysis with global sensitivity analysis (GSA) in order to give better informed predictions; to point out weaker parts of the model that are important targets for further experiments, as well as to give guidance on parameters that are essential in distinguishing different qualitative output behaviours.
Neuroscience incorporates knowledge from a range of scales, from single molecules to brain wide neural networks. Modeling is a valuable tool in understanding processes at a single scale or the interactions between two adjacent scales and researchers use a variety of different software tools in the model building and analysis process. Here we focus on the scale of biochemical pathways, which is one of the main objects of study in systems biology.
In this tutorial, you will learn how to build a simple network of integrate-and-fire neurons using PyNN, how to run simulation experiments with this network using different simulators, and how to visualize the data generated by these experiments.
In this short series of lectures, participants look at articles using TVB in a clinical context. Specifically, participants will see how TVB can help to predict recovery after stroke and how individual epileptic seizures are simulated. The course lecturers will briefly describe the methods and results achieved in the articles. Using the graphical user interface, participants will replicate the principle ideas of the articles and see how artificial lesions introduced in the connectome alter brain dynamics and how seizures spreading through the brain network can be modelled. All videos use the default TVB dataset, so you can follow each step in your TVB GUI.
Mini-videos explaining The Virtual Brain on EBRAINS.
An explainer video describing the The Virtual Brain tool set integrated into EBRAINS supercomputing resources. The tool set includes everything you need for brain simulation - processing pipelines, example datasets, multi-scale simulating tools and learning resources to help you get started.
A short explanatory video about the TVB ecosystem on the EBRAINS infrastructure. Learn about the range of TVB tools available, such as simulators, pipelines, super computing backends, fast TVB, and learning resources.
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