This video will document how to run a correlation analysis between the gray matter volume of two different structures using the output from brainlife app-freesurfer-stats.

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 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 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 lesson describes the principles underlying functional magnetic resonance imaging (fMRI), diffusion-weighted imaging (DWI), tractography, and parcellation. These tools and concepts are explained in a broader context of neural connectivity and mental health. 

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. 

In this lesson, you will hear about some of the open issues in the field of neuroscience, as well as a discussion about whether neuroscience works, and how can we know?

EyeWire is a game to map the brain. Players are challenged to map branches of a neuron from one side of a cube to the other in a 3D puzzle. Players scroll through the cube and reconstruct neurons with the help of an artificial intelligence algorithm developed at Seung Lab in Princeton University. EyeWire gameplay advances neuroscience by helping researchers discover how neurons connect to process visual information. 

Mozak is a scientific discovery game about neuroscience for citizen scientists and neuroscientists alike. Players to help neuroscientists build models of brain cells and learn more about the brain through their efforts.

This module explains how neurons come together to create the networks that give rise to our thoughts. The totality of our neurons and their connection is called our connectome. Learn how this connectome changes as we learn, and computes information.

This lecture covers the history of behaviorism and the ultimate challenge to behaviorism. 

This lecture covers various learning theories.

This lesson characterizes different types of learning in a neuroscientific and cellular context, and various models employed by researchers to investigate the mechanisms involved. 

In this lesson, you will learn about different approaches to modeling learning in neural networks, particularly focusing on system parameters such as firing rates and synaptic weights impact a network.