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In this lesson, you will learn about the intrinsic plasticity of single neurons.

Difficulty level: Intermediate
Duration: 2:08

This lesson covers short-term facilitation, a process whereby a neuron's synaptic transmission is enhanced for a short (sub-second) period.

Difficulty level: Intermediate
Duration: 1:58

This lesson describes short-term depression, a reduction of synaptic information transfer between neurons.

Difficulty level: Intermediate
Duration: 1:40

This lesson briefly wraps up the course on Computational Modeling of Neuronal Plasticity.

Difficulty level: Intermediate
Duration: 0:37

This lesson provides an overview of The Virtual Brain integrated workflows on EBRAINS.

Difficulty level: Intermediate
Duration: 32:21
Speaker: : Petra Ritter

This lesson walks users through the Image Processing Pipeline, an integral part of the TVB on EBRAINS integrated workflows.

Difficulty level: Intermediate
Duration: 24:31
Speaker: : Michael Schirner

This lesson gives an overview of The Virtual Brain simulator and its integration into the Human Brain Project Cloud and EBRAINS infrastructure.

Difficulty level: Intermediate
Duration: 24:55
Speaker: : Lia Domide

In this lesson, users will get an overview of the EBRAINS integrated Fast TVB, a C implementation of TVB that is orders of magnitude faster than the original Python TVB, and capable of performing parallelizable simulations in the cloud.

Difficulty level: Intermediate
Duration: 8:38
Speaker: : Michael Schirner

In this lesson you will learn about the Bayesian Virtual Epileptic Patient (BVEP), a research use case using TVB supported on the EBRAINS infrastructure.

Difficulty level: Intermediate
Duration: 15:39
Speaker: : Meysam Hashemi

This lesson gives a brief overview of the multi-scale co-simulation between TVB-NEST and Elephant on the EBRAINS infrastructure.

Difficulty level: Intermediate
Duration: 6:05
Speaker: : Wouter Klijn

In this lesson, you will learn about the process of constructing models for TVB automatically on the EBRAINS infrastructure.

Difficulty level: Intermediate
Duration: 23:11

This tutorial demonstrates how to use PyNN, a simulator-independent language for building neuronal network models, in conjunction with the neuromorphic hardware system SpiNNaker. 

Difficulty level: Intermediate
Duration: 25:49

This lecture focuses on ontologies for clinical neurosciences.

Difficulty level: Intermediate
Duration: 21:54

This talk presents state-of-the-art methods for ensuring data privacy with a particular focus on medical data sharing across multiple organizations.

Difficulty level: Intermediate
Duration: 22:49

This talk introduces data sharing initiatives in Epilepsy, particularly across Europe.

Difficulty level: Intermediate
Duration: 13:56
Speaker: : J. Helen Cross

In this session the Medical Informatics Platform (MIP) federated analytics is presented. The current and future analytical tools implemented in the MIP will be detailed along with the constructs, tools, processes, and restrictions that formulate the solution provided. MIP is a platform providing advanced federated analytics for diagnosis and research in clinical neuroscience research. It is targeting clinicians, clinical scientists and clinical data scientists. It is designed to help adopt advanced analytics, explore harmonized medical data of neuroimaging, neurophysiological and medical records as well as research cohort datasets, without transferring original clinical data. It can be perceived as a virtual database that seamlessly presents aggregated data from distributed sources, provides access and analyze imaging and clinical data, securely stored in hospitals, research archives and public databases. It leverages and re-uses decentralized patient data and research cohort datasets, without transferring original data. Integrated statistical analysis tools and machine learning algorithms are exposed over harmonized, federated medical data.

Difficulty level: Intermediate
Duration: 15:05

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.

Difficulty level: Intermediate
Duration: 20:26
Speaker: : Jason Skellariou

This lecture talks about the usage of knowledge graphs in hospitals and related challenges of semantic interoperability.

Difficulty level: Intermediate
Duration: 24:32