This lesson continues with the second workshop on reproducible science, focusing on additional open source tools for researchers and data scientists, such as the R programming language for data science, as well as associated tools like RStudio and R Markdown. Additionally, users are introduced to Python and iPython notebooks, Google Colab, and are given hands-on tutorials on how to create a Binder environment, as well as various containers in Docker and Singularity.

This lecture provides an introduction to Plato’s concept of rationality and Aristotle’s concept of empiricism, and the enduring discussion between rationalism and empiricism to this day.

This lecture goes into further detail about the hard problem of developing a scientific discipline for subjective consciousness.

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.

In this workshop talk, you will receive a tour of the Code Ocean ScienceOps Platform, a centralized cloud workspace for all teams. 

This talk describes approaches to maintaining integrated workflows and data management schema, taking advantage of the many open source, collaborative platforms already existing.

This lesson gives a brief introduction to the course Neuroscience for Machine Learners (Neuro4ML). 

This lesson covers the history of neuroscience and machine learning, and the story of how these two seemingly disparate fields are increasingly merging. 

In this lesson, you will learn about the current challenges facing the integration of machine learning and neuroscience. 

This lecture provides an overview of depression (epidemiology and course of the disorder), clinical presentation, somatic co-morbidity, and treatment options.

In this lesson, you will learn about how genetics can contribute to our understanding of psychiatric phenotypes.

This lesson gives an introduction to simple spiking neuron models.

This lesson provides an introduction to simple spiking neuron models.

This lesson discusses FAIR principles and methods currently in development for assessing FAIRness.

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.