This talk covers EBRAINS, an open research infrastructure that gathers data, tools and computing facilities for brain-related research, built with interoperability at the core.

This lesson discusses the need for and approaches to integrating data across the various temporal and spatial scales in which brain activity can be measured. 

This lesson consists of lecture and tutorial components, focusing on resources and tools which facilitate multi-scale brain modeling and simulation. 

In this talk, challenges of handling complex neuroscientific data are discussed, as well as tools and services for the annotation, organization, storage, and sharing of these data. 

This lecture describes the neuroscience data respository G-Node Infrastructure (GIN), which provides platform independent data access and enables easy data publishing. 

This lesson provides an introduction to the course Neuroscience Data Integration Through Use of Digital Brain Atlases, during which attendees will learn about concepts for integration of research data, approaches and resources for assigning anatomical location to brain data, and infrastructure for sharing experimental brain research data. 

This talk covers the various concepts, motivations, and trends within the neuroscientific community related to the sharing and integration of brain research data. 

This lesson focuses on the neuroanatomy of the human brain, delving into macrostructures like cortices, lobes, and hemispheres, and microstructures like neurons and cortical laminae.

This lesson provides an introduction to the European open research infrastructure EBRAINS and its digital brain atlas resources.

In this lesson, attendees will learn about the challenges in assigning experimental brain data to specific locations, as well as the advantages and shortcomings of current location assignment procedures. 

This lesson covers the inherent difficulties associated with integrating neuroscientific data, as well as the current methods and approaches to do so. 

Attendees of this talk will learn about QuickNII, a tool for user-guided affine registration of 2D experimental image data to 3D atlas reference spaces, which also facilitates data integration through standardized coordinate systems. 

This lesson provides an overview of DeepSlice, a Python package which aligns histology to the Allen Brain Atlas and Waxholm Rat Atlas using deep learning.

This lecture covers visualizing extracellular neurotransmitter dynamics

This lesson goes over the basic mechanisms of neural synapses, the space between neurons where signals may be transmitted. 

This lesson describes spike timing-dependent plasticity (STDP), a biological process that adjusts the strength of connections between neurons in the brain, and how one can implement or mimic this process in a computational model. You will also find links for practical exercises at the bottom of this page. 

This lesson discusses a gripping neuroscientific question: why have neurons developed the discrete action potential, or spike, as a principle method of communication? 

This lecture consists of the second half of the introduction to signal transduction, here focusing on cell receptors and signalling cascades.

In this lesson, you will learn about GABAergic interneurons and local inhibition on the circuit level.

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.