This lecture covers visualizing extracellular neurotransmitter dynamics
This lecture provides an introduction to the study of eye-tracking in humans.
This lesson contains both a lecture and a tutorial component. The lecture (0:00-20:03 of YouTube video) discusses both the need for intersectional approaches in healthcare as well as the impact of neglecting intersectionality in patient populations. The lecture is followed by a practical tutorial in both Python and R on how to assess intersectional bias in datasets. Links to relevant code and data are found below.
This lecture discusses what defines an integrative approach regarding research and methods, including various study designs and models which are appropriate choices when attempting to bridge data domains; a necessity when whole-person modelling.
This lecture covers different perspectives on the study of the mental, focusing on the difference between Mind and Brain.
This lecture covers a lot of post-war developments in the science of the mind, focusing first on the cognitive revolution, and concluding with living machines.
Introduction of the Foundations of Machine Learning in Python course - Day 01.
High-Performance Computing and Analytics Lab, University of Bonn
This lesson discusses both state-of-the-art detection and prevention schema in working with neurodegenerative diseases.
This lecture provides an overview of depression (epidemiology and course of the disorder), clinical presentation, somatic co-morbidity, and treatment options.
This lecture gives an introduction to the types of glial cells, homeostasis (influence of cerebral blood flow and influence on neurons), insulation and protection of axons (myelin sheath; nodes of Ranvier), microglia and reactions of the CNS to injury.
This lesson gives an introduction to simple spiking neuron models.
This lesson provides an introduction to simple spiking neuron models.
This lesson gives an introductory presentation on how data science can help with scientific reproducibility.
This tutorial is part 1 of 2. It aims to provide viewers with an understanding of the fundamentals of R tool. Note: parts 1 and 2 of this tutorial are part of the same YouTube video; part 1 ends at 17:42.
This lesson introduces the practical usage of The Virtual Brain (TVB) in its graphical user interface and via python scripts. In the graphical user interface, you are guided through its data repository, simulator, phase plane exploration tool, connectivity editor, stimulus generator, and the provided analyses. The implemented iPython notebooks of TVB are presented, and since they are public, can be used for further exploration of TVB.
This hands-on tutorial focuses on a brief introduction to the GUI of TVB. You will visualize a structural connectome and use it for simulation. The local neural mass model will be explored through the phase plane viewer and a parameter space exploration will be performed to observe different dynamics of the large-scale brain model.
Simulate your own stimulation with the TVB graphical user interface. This hands-on shows you how to configure a stimulus for a specific brain region and apply it to the simulation. Afterwards the results are visualized with the TVB 3D viewer.
Manipulate the default connectome provided with TVB to see how structural lesions effect brain dynamics. In this hands-on session you will insert lesions into the connectome within the TVB graphical user interface (GUI). Afterwards, the modified connectome will be used for simulations and the resulting activity will be analysed using functional connectivity.
Learn how to simulate strokes with the simulation platform, The Virtual Brain. We will go through two papers: Functional Mechanisms of Recovery after Stroke: Modeling with The Virtual Brain and The Virtual Brain: Modeling Biological Correlates of Recovery After Chronic Stroke, and apply the same processes with our own structural connectivity dataset in The Virtual Brain.
In this lesson you will learn how to simulate seizure events and epilepsy in The Virtual Brain. We will look at the paper On the Nature of Seizure Dynamics, which describes a new local model called the Epileptor, and apply this same model in The Virtual Brain. This is part 1 of 2 in a series explaining how to use the Epileptor. In this part, we focus on setting up the parameters.