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This lecture (11/15) is part of the Computational Modeling of Neuronal Plasticity Course that aims to teach users how to build a mathematical model of a neuron, its inputs, and its neuronal plasticity mechanisms, by writing your own Python program. This lecture provides users with a brief video introduction to the concepts that serves as a companion to the lecture notes and solution figures.

Authors: Florence I. Kleberg and Prof. Jochen Triesch.

Difficulty level: Intermediate
Duration: 2:58

This lecture (12/15) is part of the Computational Modeling of Neuronal Plasticity Course that aims to teach users how to build a mathematical model of a neuron, its inputs, and its neuronal plasticity mechanisms, by writing your own Python program. This lecture provides users with a brief video introduction to the concepts that serves as a companion to the lecture notes and solution figures.

Authors: Florence I. Kleberg and Prof. Jochen Triesch.

Difficulty level: Intermediate
Duration: 2:08

This lecture (13/15) is part of the Computational Modeling of Neuronal Plasticity Course that aims to teach users how to build a mathematical model of a neuron, its inputs, and its neuronal plasticity mechanisms, by writing your own Python program. This lecture provides users with a brief video introduction to the concepts that serves as a companion to the lecture notes and solution figures. Authors: Florence I. Kleberg and Prof. Jochen Triesch.

Difficulty level: Intermediate
Duration: 1:58

This lecture (14/15) is part of the Computational Modeling of Neuronal Plasticity Course that aims to teach users how to build a mathematical model of a neuron, its inputs, and its neuronal plasticity mechanisms, by writing your own Python program. This lecture provides users with a brief video introduction to the concepts that serves as a companion to the lecture notes and solution figures.

Authors: Florence I. Kleberg and Prof. Jochen Triesch.

Difficulty level: Intermediate
Duration: 1:40

This lecture (15/15) is part of the Computational Modeling of Neuronal Plasticity Course that aims to teach users how to build a mathematical model of a neuron, its inputs, and its neuronal plasticity mechanisms, by writing your own Python program. This lecture provides users with a brief video introduction to the concepts that serves as a companion to the lecture notes and solution figures.

Authors: Florence I. Kleberg and Prof. Jochen Triesch.

Difficulty level: Intermediate
Duration: 0:37

An overview of The Virtual Brain integrated workflows on EBRAINS.

Difficulty level: Intermediate
Duration: 0:32:21
Speaker: : Petra Ritter

Walk through of the Image Processing Pipeline, an integral part of the TVB on EBRAINS integrated workflows.

Difficulty level: Intermediate
Duration: 0:24:31
Speaker: : Michael Schirner

An overview of The Virtual Brain simulator and it's integration into the Human Brain Project Cloud and EBRAINS infrastructure.

Difficulty level: Intermediate
Duration: 0:24:55
Speaker: : Lia Domide

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: 0:08:38
Speaker: : Michael Schirner

An overview of the Bayesian Virtual Epileptic Patient (BVEP), a research use case using TVB supported on the EBRAINS infrastructure.

Difficulty level: Intermediate
Duration: 0:15:39
Speaker: : Meysam Hashemi

An overview of the multi-scale co-simulation between TVB-NEST and Elephant on the EBRAINS infrastructure.

Difficulty level: Intermediate
Duration: 0:06:05
Speaker: : Wouter Klijn

An overview of the process of constructing models for TVB automatically on the EBRAINS infrastructure.

Difficulty level: Intermediate
Duration: 0:23:11

DAQCORD is a framework for the design, documentation and reporting of data curation methods in order to advance the scientific rigour, reproducibility and analysis of the data. This lecture covers the rationale for developing the framework, the process in which the framework was developed, and ends with a presentation of the framework. While the driving use case for DAQCORD was clinical traumatic brain injury research, the framework is applicable to clinical studies in other domains of clinical neuroscience research.

Difficulty level: Intermediate
Duration: 17:08
Speaker: : Ari Ercole

PyNN is a simulator-independent language for building neuronal network models. The PyNN API aims to support modelling at a high-level of abstraction (populations of neurons, layers, columns and the connections between them) while still allowing access to the details of individual neurons and synapses when required. PyNN provides a library of standard neuron, synapse, and synaptic plasticity models which have been verified to work the same on the different supported simulators. PyNN also provides a set of commonly-used connectivity algorithms (e.g. all-to-all, random, distance-dependent, small-world) but makes it easy to provide your own connectivity in a simulator-independent way. This lecture was part of the 7th SpiNNaker Workshop held 3 - 6 October 2017.

Difficulty level: Intermediate
Duration: 25:49

This lecture focuses on ontologies for clinical neurosciences.

Difficulty level: Intermediate
Duration: 21:54

This presentation discusses the impact of data sharing in stroke.

Difficulty level: Intermediate
Duration: 16:33
Speaker: : Valeria Caso

This talks discusses data sharing in the context of dementia. It explains why data sharing in dementia is important, how data is usually shared in the field and illustrates two examples: the Netherlands Consortium Dementia cohorts and the European Platform for Neurodegenerative Diseases.

Difficulty level: Intermediate
Duration: 21:21

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

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

This talks presents an overview of the potential for data federation in stroke research.

Difficulty level: Intermediate
Duration: 21:37

This lecture explains the need for data federation in medicine and how it can be achieved.

Difficulty level: Intermediate
Duration: 27:09
Speaker: : Philippe Ryvlin