This is an introductory lecture on whole-brain modelling, delving into the various spatial scales of neuroscience, neural population models, and whole-brain modelling. Additionally, the clinical applications of building and testing such models are characterized. 

This is a tutorial on designing a Bayesian inference model to map belief trajectories, with emphasis on gaining familiarity with Hierarchical Gaussian Filters (HGFs).

This lesson corresponds to slides 65-90 of the PDF below. 

Similarity Network Fusion (SNF) is a computational method for data integration across various kinds of measurements, aimed at taking advantage of the common as well as complementary information in different data types. This workshop walks participants through running SNF on EEG and genomic data using RStudio.

In this third and final hands-on tutorial from the Research Workflows for Collaborative Neuroscience workshop, you will learn about workflow orchestration using open source tools like DataJoint and Flyte. 

This lecture aims to help researchers, students, and health care professionals understand the place for neuroinformatics in the patient journey using the exemplar of an epilepsy patient. 

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 describes how to build research workflows, including a demonstrate using DataJoint Elements to build data pipelines.

This lesson provides an introduction to modeling single neurons, as well as stability analysis of neural models.

This lesson continues a thorough description of the concepts, theories, and methods involved in the modeling of single neurons. 

In this lesson you will learn about fundamental neural phenomena such as oscillations and bursting, and the effects these have on cortical networks. 

This lesson continues discussing properties of neural oscillations and networks. 

In this lecture, you will learn about rules governing coupled oscillators, neural synchrony in networks, and theoretical assumptions underlying current understanding.

This lesson provides a continued discussion and characterization of coupled oscillators. 

This lesson gives an overview of modeling neurons based on firing rate. 

This lesson characterizes the pattern generation observed in visual system hallucinations.

This lesson gives an introduction to stability analysis of neural models.

This lesson continues from the previous lectures, providing introduction to stability analysis of neural models.

In this lesson, you will learn about phenomena of neural populations such as synchrony, oscillations, and bursting.

This lesson continues from the previous lecture, giving an overview of various neural phenomena such as oscillations and bursting.