This lesson gives an introduction to high-performance computing with the Compute Canada network, first providing an overview of use cases for HPC and then a hands-on tutorial. Though some examples might seem specific to the Calcul Québec, all computing clusters in the Compute Canada network share the same software modules and environments.
This lecture covers FAIR atlases, including their background and construction, as well as how they can be created in line with the FAIR principles.
This lesson discusses both state-of-the-art detection and prevention schema in working with neurodegenerative diseases.
This lecture provides an overview of some of the essential concepts in neuropharmacology (e.g. receptor binding, agonism, antagonism), an introduction to pharmacodynamics and pharmacokinetics, and an overview of the drug discovery process relative to diseases of the central nervous system.
In this lesson, you will learn about the current challenges facing the integration of machine learning and neuroscience.
This lesson provides a brief overview of the Python programming language, with an emphasis on tools relevant to data scientists.
The lecture provides an overview of the core skills and practical solutions required to practice reproducible research.
This lecture covers the description and brief history of data science and its use in neuroinformatics.
This lesson provides an overview of self-supervision as it relates to neural data tasks and the Mine Your Own vieW (MYOW) approach.