Hardware for computing for non-ICT specialists
This lecture provides a history of data management, recent developments data management, and a brief description of scientific data management.
Computer arithmetic is necessarily performed using approximations to the real numbers they are intended to represent, and consequently it is possible for the discrepancies between the actual solution and the approximate solutions to diverge, i.e. to become increasingly different. This lecture focuses on how this happens and techniques for reducing the effects of these phenomena and discuss systems which are chaotic.
This lecture will addresses what it means for a problem to have a computable solution, methods for combining computability results to analyse more complicated problems, and finally look in detail at one particular problem which has no computable solution: the halting problem.
This lecture focuses on computational complexity which lies at the heart of computer science thinking. In short, it is a way to quickly gauge an approximation to the computational resource required to perform a task. Methods to analyse a computer program and to perform the approximation are presented. Speaker: David Lester.
JupyterHub is a simple, highly extensible, multi-user system for managing per-user Jupyter Notebook servers, designed for research groups or classes. This lecture covers deploying JupyterHub on a single server, as well as deploying with Docker using GitHub for authentication.
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.
The practical usage of The Virtual brain in its graphical user interface and via python scripts is introduced. 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 The Virtual brain.
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.
Tutorial on collaborating with Git and GitHub. This tutorial 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.
Colt Steele provides a comprehensive introduction to the command line and 50 popular Linux commands. This is a long course (nearly 5 hours) but well worth it if you are going to spend a good part of your career working from a terminal, which is likely if you are interested in flexibility, power, and reproducibility in neuroscience research.
This lesson is courtesy of freeCodeCamp.
Tutorial describing the basic search and navigation features of the Allen Mouse Brain Atlas
Tutorial describing the basic search and navigation features of the Allen Developing Mouse Brain Atlas
This tutorial demonstrates how to use the differential search feature of the Allen Mouse Brain Atlas to find gene markers for different regions of the brain and to visualize this gene expression in three-dimensional space. Differential search is also available for the Allen Developing Mouse Brain Atlas and the Allen Human Brain Atlas.
The Mouse Phenome Database (MPD) provides access to primary experimental trait data, genotypic variation, protocols and analysis tools for mouse genetic studies. Data are contributed by investigators worldwide and represent a broad scope of phenotyping endpoints and disease-related traits in naïve mice and those exposed to drugs, environmental agents or other treatments. MPD ensures rigorous curation of phenotype data and supporting documentation using relevant ontologies and controlled vocabularies. As a repository of curated and integrated data, MPD provides a means to access/re-use baseline data, as well as allows users to identify sensitized backgrounds for making new mouse models with genome editing technologies, analyze trait co-inheritance, benchmark assays in their own laboratories, and many other research applications. MPD’s primary source of funding is NIDA. For this reason, a majority of MPD data is neuro- and behavior-related.