This talk describes the NIH-funded SPARC Data Structure, and how this project navigates ontology development while keeping in mind the FAIR science principles.
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 structured data, databases, federating neuroscience-relevant databases, and ontologies.
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 lecture focuses on ontologies for clinical neurosciences.
This lesson contains practical exercises which accompanies the first few lessons of the Neuroscience for Machine Learners (Neuro4ML) course.
This video briefly goes over the exercises accompanying Week 6 of the Neuroscience for Machine Learners (Neuro4ML) course, Understanding Neural Networks.
This lecture covers the description and characterization of an input-output relationship in a information-theoretic context.
This lesson is part 1 of 2 of a tutorial on statistical models for neural data.
This lesson is part 2 of 2 of a tutorial on statistical models for neural data.
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.