This lecture provides an introduction to optogenetics, a biological technique to control the activity of neurons or other cell types with light.

This primer on optogenetics primer discusses how to manipulate neuronal populations with light at millisecond resolution and offers possible applications such as curing the blind and "playing the piano" with cortical neurons.

This lesson provides an overview of the current status in the field of neuroscientific ontologies, presenting examples of data organization and standards, particularly from neuroimaging and electrophysiology. 

This lesson continues from part one of the lecture Ontologies, Databases, and Standards, diving deeper into a description of ontologies and knowledg graphs. 

This lecture covers the NIDM data format within BIDS to make your datasets more searchable, and how to optimize your dataset searches.

This lecture covers positron emission tomography (PET) imaging and the Brain Imaging Data Structure (BIDS), and how they work together within the PET-BIDS standard to make neuroscience more open and FAIR.

This lecture discusses the FAIR principles as they apply to electrophysiology data and metadata, the building blocks for community tools and standards, platforms and grassroots initiatives, and the challenges therein.

This lecture discusses how to standardize electrophysiology data organization to move towards being more FAIR.

The International Brain Initiative (IBI) is a consortium of the world’s major large-scale brain initiatives and other organizations with a vested interest in catalyzing and advancing neuroscience research through international collaboration and knowledge sharing. This workshop introduces the IBI, the efforts of the Data Standards and Sharing Working Group,  and keynote lectures on the impact of data standards and sharing on large-scale brain projects, as well as a discussion on prospects and needs for neural data sharing.

This lecture covers the application of diffusion MRI for clinical and preclinical studies.

This tutorial introduces pipelines and methods to compute brain connectomes from fMRI data. With corresponding code and repositories, participants can follow along and learn how to programmatically preprocess, curate, and analyze functional and structural brain data to produce connectivity matrices.