Tutorial on how to use TVB-NEST toolbox on your local computer. Authors: D. Perdikis, L. Domide, M. Schirner, P. Ritter
Tutorial on how to perform multi-scale simulation of Alzheimer's disease on The Virtual Brain Simulation Platform. Authors: L. Stefanovski, P. Triebkorn, M.A. Diaz-Cortes, A. Solodkin, V. Jirsa, A.R. McIntosh, P. Ritter
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.
Watch this video recap of a QBIN SciComm seminar that took place over Zoom on April 26, 2021. Nikola Stikov talks about the history and future of academic publishing and the need for transparency, and Agah Karakuzu gives a live demo of an alpha version of NeuroLibre, a preprint server that goes beyond the PDF to complement research articles.
Basic knowledge and comfort with Command Line Interfaces (CLI) is highly beneficial for learning how to use countless neuroscience tools and acquiring programming skills. Furthermore, CLIs are better disposed to reproducibility, automation, concatenation in pipelines, execution on multiple platforms, and remote access.
Ross Markello takes you through this general introduction to the essentials of navigating through a Bash terminal environment. The lesson is based on the Software Carpentries "Introduction to the Shell" and was given in the context of the BrainHack School 2020.
Ross Markello provides an overview of Python applications to data analysis, demonstrating why it has become ubiquitous in data science and neuroscience.
The lesson was given in the context of the BrainHack School 2020.
As a part of NeuroHackademy 2021, Noah Benson gives an introduction to Pytorch, one of the two most common software packages for deep learning applications to the neurosciences.
In this hands-on tutorial, Dr. Robert Guangyu Yang works through a number of coding exercises to see how RNNs can be easily used to study cognitive neuroscience questions, with a quick demonstration of how we can train and analyze RNNs on various cognitive neuroscience tasks. Familiarity of Python and basic knowledge of Pytorch are assumed.