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The simulation of the virtual epileptic patient is presented as an example of advanced brain simulation as a translational approach to deliver improved results in clinics. The fundamentals of epilepsy are explained. On this basis, the concept of epilepsy simulation is developed. 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

The Virtual Brain is an open-source, multi-scale, multi-modal brain simulation platform. In this lesson, you get introduced to brain simulation in general and to The Virtual brain in particular. Prof. Ritter will present the newest approaches for clinical applications of The Virtual brain - 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

The concept of neural masses, an application of mean field theory, is introduced as a possible surrogate for electrophysiological signals in brain simulation. The mathematics of neural mass models and their integration to a coupled network are explained. Bifurcation analysis is presented as an important technique in the understanding of non-linear systems and as a fundamental method in the design of brain simulations. Finally, 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

A brief overview of the Python programming language, with an emphasis on tools relevant to data scientists. This lecture was part of the 2018 Neurohackademy, a 2-week hands-on summer institute in neuroimaging and data science held at the University of Washington eScience Institute.

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

The tutorial is intended primarily for beginners, but it will also beneficial to experimentalists who understand electroencephalography and event related techniques, but need additional knowledge in annotation, standardization, long-term storage and publication of data.

Difficulty level: Beginner
Duration: 35:30

This lecture on generating TVB ready imaging data by Paul Triebkorn is part of the TVB Node 10 series, a 4 day workshop dedicated to learning about The Virtual Brain, brain imaging, brain simulation, personalised brain models, TVB use cases, etc. TVB is a full brain simulation platform.

Difficulty level: Intermediate
Duration: 1:40:52
Speaker: : Paul Triebkorn

This module covers many of the types of non-invasive neurotech and neuroimaging devices including Electroencephalography (EEG), Electromyography (EMG), Electroneurography (ENG), Magnetoencephalography (MEG), functional Near-Infrared Spectroscopy (fNRIs), Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET), and Computed Tomography

Difficulty level: Beginner
Duration: 13:36
Speaker: : Harrison Canning

This lecture and tutorial focuses on measuring human functional brain networks. The lecture and tutorial were part of the 2019 Neurohackademy, a 2-week hands-on summer institute in neuroimaging and data science held at the University of Washington eScience Institute.

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

Estefany Suárez provides a conceptual overview of the rudiments of machine learning, including its bases in traditional statistics and the types of questions it might be applied to.

 

The lesson was presented in the context of the BrainHack School 2020.

Difficulty level: Beginner
Duration: 01:22:18
Speaker: :

Jake Vogel gives a hands-on, Jupyter-notebook-based tutorial to apply machine learning in Python to brain-imaging data.

 

The lesson was presented in the context of the BrainHack School 2020.

Difficulty level: Beginner
Duration: 02:13:53
Speaker: :

Gael Varoquaux presents some advanced machine learning algorithms for neuroimaging, while addressing some real-world considerations related to data size and type.

 

The lesson was presented in the context of the BrainHack School 2020.

Difficulty level: Beginner
Duration: 01:17:14
Speaker: :

Dr. Guangyu Robert Yang describes how Recurrent Neural Networks (RNNs) trained with machine learning techniques on cognitive tasks have become a widely accepted tool for neuroscientists. In comparison to traditional computational models in neuroscience, RNNs can offer substantial advantages at explaining complex behavior and neural activity patterns. Their use allows rapid generation of mechanistic hypotheses for cognitive computations. RNNs further provide a natural way to flexibly combine bottom-up biological knowledge with top-down computational goals into network models. However, early works of this approach are faced with fundamental challenges. In this talk, Dr. Guangyu Robert Yang discusses some of these challenges, and several recent steps that we took to partly address them and to build next-generation RNN models for cognitive neuroscience.​

Difficulty level: Beginner
Duration: 00:51:12
Speaker: :

Serving as good refresher, Shawn Grooms explains the maths and logic concepts that are important for programmers to understand, including sets, propositional logic, conditional statements, and more.

 

This compilation is courtesy of freeCodeCamp.

Difficulty level: Beginner
Duration: 01:00:07
Speaker: :

Linear algebra is the branch of mathematics concerning linear equations such as linear functions and their representations through matrices and vector spaces. As such, it underlies a huge variety of analyses in the neurosciences.  This lesson provides a useful refresher which will facilitate the use of Matlab, Octave, and various matrix-manipulation and machine-learning software.

 

This lesson was created by RootMath.

Difficulty level: Beginner
Duration: 01:21:30
Speaker: :

Introduction to the FAIR Principles and examples of applications of the FAIR Principles in neuroscience. This lecture was part of the 2019 Neurohackademy, a 2-week hands-on summer institute in neuroimaging and data science held at the University of Washington eScience Institute.

Difficulty level: Beginner
Duration: 55:57