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This lecture covers different perspectives on the study of the mental, focusing on the difference between Mind and Brain. 

Difficulty level: Beginner
Duration: 1:16:30

The Virtual Brain (TVB) is an open-source, multi-scale, multi-modal brain simulation platform. In this lesson, you get introduced to brain simulation in general and to TVB in particular. This lesson also presents the newest approaches for clinical applications of TVB - that is, for stroke, epilepsy, brain tumors, and Alzheimer’s disease - and show how brain simulation can improve diagnostics, therapy, and understanding of neurological disease.

Difficulty level: Beginner
Duration: 1:35:08
Speaker: : Petra Ritter

This lesson explains the mathematics of neural mass models and their integration to a coupled network. You will also learn about bifurcation analysis, an important technique in the understanding of non-linear systems and as a fundamental method in the design of brain simulations. Lastly, the application of the described mathematics is demonstrated in the exploration of brain stimulation regimes.

Difficulty level: Beginner
Duration: 1:49:24
Speaker: : Andreas Spiegler

In this lesson, the simulation of a virtual epileptic patient is presented as an example of advanced brain simulation as a translational approach to deliver improved clinical results. You will learn about the fundamentals of epilepsy, as well as the concepts underlying epilepsy simulation. By using an iPython notebook, the detailed process of this approach is explained step by step. In the end, you are able to perform simple epilepsy simulations your own.

Difficulty level: Beginner
Duration: 1:28:53
Speaker: : Julie Courtiol

This lesson provides a brief overview of the Python programming language, with an emphasis on tools relevant to data scientists.

Difficulty level: Beginner
Duration: 1:16:36
Speaker: : Tal Yarkoni

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

Difficulty level: Intermediate
Duration: 24:32

This lesson provides an introduction to the lifecycle of EEG/ERP data, describing the various phases through which these data pass, from collection to publication.

Difficulty level: Beginner
Duration: 35:30

In this lesson you will learn about experimental design for EEG acquisition, as well as the first phases of the EEG/ERP data lifecycle. 

Difficulty level: Beginner
Duration: 30:04

This lesson provides an overview of the current regulatory measures in place regarding experimental data security and privacy. 

Difficulty level: Beginner
Duration: 31:00

In this lesson, you will learn the appropriate methods for collection of both data and associated metadata during EEG experiments.

Difficulty level: Beginner
Duration: 29:14

This lesson goes over methods for managing EEG/ERP data after it has been collected, from annotation to publication. 

Difficulty level: Beginner
Duration: 39:25

In this final lesson of the course, you will learn broadly about EEG signal processing, as well as specific applications which make this kind of brain signal valuable to researchers and clinicians. 

Difficulty level: Beginner
Duration: 34:51

This lesson is a general overview of overarching concepts in neuroinformatics research, with a particular focus on clinical approaches to defining, measuring, studying, diagnosing, and treating various brain disorders. Also described are the complex, multi-level nature of brain disorders and the data associated with them, from genes and individual cells up to cortical microcircuits and whole-brain network dynamics. Given the heterogeneity of brain disorders and their underlying mechanisms, this lesson lays out a case for multiscale neuroscience data integration.

Difficulty level: Intermediate
Duration: 1:09:33
Speaker: : Sean Hill

This lesson breaks down the principles of Bayesian inference and how it relates to cognitive processes and functions like learning and perception. It is then explained how cognitive models can be built using Bayesian statistics in order to investigate how our brains interface with their environment. 

This lesson corresponds to slides 1-64 in the PDF below. 

Difficulty level: Intermediate
Duration: 1:28:14

This lecture and tutorial focuses on measuring human functional brain networks, as well as how to account for inherent variability within those networks. 

Difficulty level: Intermediate
Duration: 50:44
Speaker: : Caterina Gratton

This lecture presents an overview of functional brain parcellations, as well as a set of tutorials on bootstrap agregation of stable clusters (BASC) for fMRI brain parcellation.

Difficulty level: Advanced
Duration: 50:28
Speaker: : Pierre Bellec

This video gives a brief introduction to Neuro4ML's lessons on neuromorphic computing - the use of specialized hardware which either directly mimics brain function or is inspired by some aspect of the way the brain computes. 

Difficulty level: Intermediate
Duration: 3:56
Speaker: : Dan Goodman

In this lesson, you will learn in more detail about neuromorphic computing, that is, non-standard computational architectures that mimic some aspect of the way the brain works. 

Difficulty level: Intermediate
Duration: 10:08
Speaker: : Dan Goodman

This video provides a very quick introduction to some of the neuromorphic sensing devices, and how they offer unique, low-power applications.

Difficulty level: Intermediate
Duration: 2:37
Speaker: : Dan Goodman