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.
Best practices: the tips and tricks on how to get your Miniscope to work and how to get your experiments off the ground.
"Balancing size & function in compact miniscopes" was presented by Tycho Hoogland at the 2021 Virtual Miniscope Workshop as part of a series of talks by leading Miniscope users and developers.
"Computational imaging for miniature miniscopes" was presented by Laura Waller at the 2021 Virtual Miniscope Workshop as part of a series of talks by leading Miniscope users and developers.
"Online 1-photon vs 2-photon calcium imaging data analysis: Current developments and future plans" was presented by Andrea Giovannucci at the 2021 Virtual Miniscope Workshop as part of a series of talks by leading Miniscope users and developers.
"Ensemble fluidity supports memory flexibility during spatial reversal" was presented by William Mau at the 2021 Virtual Miniscope Workshop as part of a series of talks by leading Miniscope users and developers.
How to start processing the raw imaging data generated with a Miniscope, including developing a usable pipeline and demoing the Minion pipeline
This lecture covers infrared LED oblique illumination for studying neuronal circuits in in vitro block-preparations of the spinal cord and brain stem.
This lecture covers the application of diffusion MRI for clinical and preclinical studies.
Longitudinal Online Research and Imaging System (LORIS) is a web-based data and project management software for neuroimaging research studies. It is an open source framework for storing and processing behavioural, clinical, neuroimaging and genetic data. LORIS also makes it easy to manage large datasets acquired over time in a longitudinal study, or at different locations in a large multi-site study.