This video will document the process of importing a dataset archived on OpenNeuro from the Datasets tab into a brainlife project.

This lecture introduces you to the basics of the Amazon Web Services public cloud. It covers the fundamentals of cloud computing and goes through both the motivations and processes involved in moving your research computing to the cloud.

This lecture discusses how FAIR practices affect personalized data models, including workflows, challenges, and how to improve these practices.

In this talk, you will learn how brainlife.io works, and how it can be applied to neuroscience data.

As a part of NeuroHackademy 2020, this lecture delves into cloud computing, focusing on Amazon Web Services. 

This talk presents an overview of CBRAIN, a web-based platform that allows neuroscientists to perform computationally intensive data analyses by connecting them to high-performance computing facilities across Canada and around the world.

This lightning talk describes an automated pipline for positron emission tomography (PET) data. 

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 module covers many of the types of non-invasive neurotech and neuroimaging devices including electroencephalography (EEG), electromyography (EMG), electroneurography (ENG), magnetoencephalography (MEG), and more. 

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.