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.

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.

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.

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.

Computational models provide a framework for integrating data across spatial scales and for exploring hypotheses about the biological mechanisms underlying neuronal and network dynamics. However, as models increase in complexity, additional barriers emerge to the creation, exchange, and re-use of models. Successful projects have created standards for describing complex models in neuroscience and provide open source tools to address these issues. This lecture provides an overview of these projects and make a case for expanded use of resources in support of reproducibility and validation of models against experimental data.

Introduction to the Brain Imaging Data Structure (BIDS): a standard for organizing human neuroimaging datasets. 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.

NWB: An ecosystem for neurophysiology data standardization

NWB: An ecosystem for neurophysiology data standardization

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.

Lecture on functional brain parcellations and a set of tutorials on bootstrap agregation of stable clusters (BASC) for fMRI brain parcellation which 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.

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.

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.

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.

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.​

In this presentation by the OHBM OpenScienceSIG, Tom Shaw and Steffen Bollmann cover how containers can be useful for running the same software on different platforms and sharing analysis pipelines with other researchers. They demonstrate how to build docker containers from scratch, using Neurodocker, and cover how to use containers on an HPC with singularity.

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.

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.

This module explores sensation in the brain: what organs are involved, sensory pathways, processing centers, and theories of integration. We cover sensory transduction, vision, audition olfaction, gustation, and somatosensation.

This module covers how the brain interacts with the world through motor movements. Motor movements underlie so much of our functioning, our speech, the opening and closing of our eyes, and the beating of our hearts. We’ll learn about areas of the brain involved in movement and some of its pathways.

This module covers the structure and function of the neuron, its components and mechanisms, action potentials, and the many glial cells that support it.