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This module explores sensation in the brain: what organs are involved, sensory pathways, processing centers, and theories of integration.

Difficulty level: Beginner
Duration: 7:17
Speaker: : Colin Fausnaught

This module covers how the brain interacts with the world through motor movements. Motor movements underlie so much of our functioning, our speech, the opening and closing of our eyes, and the beating of our hearts.

Difficulty level: Beginner
Duration: 5:00
Speaker: : Harrison Canning

This lesson provides an overview of the structure and function of the neuron, its components and mechanisms, action potentials, and the many glial cells that support it.

Difficulty level: Beginner
Duration: 8:31
Speaker: : Colin Fausnaught

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.

Difficulty level: Beginner
Duration: 7:13
Speaker: : Harrison Canning
Course:

An introduction to data management, manipulation, visualization, and analysis for neuroscience. Students will learn scientific programming in Python, and use this to work with example data from areas such as cognitive-behavioral research, single-cell recording, EEG, and structural and functional MRI. Basic signal processing techniques including filtering are covered. The course includes a Jupyter Notebook and video tutorials.

 

Difficulty level: Beginner
Duration: 1:09:16
Speaker: : Aaron J. Newman

This demonstration walks through how to import your data into MATLAB.

Difficulty level: Beginner
Duration: 6:10
Speaker: : MATLAB®

This lesson provides instruction regarding the various factors one must consider when preprocessing data, preparing it for statistical exploration and analyses. 

Difficulty level: Beginner
Duration: 15:10
Speaker: : MATLAB®

This tutorial outlines, step by step, how to perform analysis by group and how to do change-point detection.

Difficulty level: Beginner
Duration: 2:49
Speaker: : MATLAB®

This tutorial walks through several common methods for visualizing your data in different ways depending on your data type.

Difficulty level: Beginner
Duration: 6:10
Speaker: : MATLAB®

This tutorial illustrates several ways to approach predictive modeling and machine learning with MATLAB.

Difficulty level: Beginner
Duration: 6:27
Speaker: : MATLAB®

This brief tutorial goes over how you can easily work with big data as you would with any size of data.

Difficulty level: Beginner
Duration: 3:55
Speaker: : MATLAB®

In this tutorial, you will learn how to deploy your models outside of your local MATLAB environment, enabling wider sharing and collaboration.

Difficulty level: Beginner
Duration: 3:52
Speaker: : MATLAB®

This lesson provides an overview of Jupyter notebooks, Jupyter lab, and Binder, as well as their applications within the field of neuroimaging, particularly when it comes to the writing phase of your research. 

Difficulty level: Intermediate
Duration: 50:28
Speaker: : Elizabeth DuPre

The lecture provides an overview of the core skills and practical solutions required to practice reproducible research.

Difficulty level: Beginner
Duration: 1:25:17
Speaker: : Fernando Perez

This lesson provides an overview of self-supervision as it relates to neural data tasks and the Mine Your Own vieW (MYOW) approach.

Difficulty level: Beginner
Duration: 25:50
Speaker: : Eva Dyer

This video gives a short introduction to the EBRAINS data sharing platform, why it was developed, and how it contributes to open data sharing.

Difficulty level: Beginner
Duration: 17:32
Speaker: : Ida Aasebø

This video introduces the key principles for data organization and explains how you could make your data FAIR for data sharing on EBRAINS.

Difficulty level: Beginner
Duration: 10:54
Course:

This lesson gives a quick walkthrough the Tidyverse, an "opinionated" collection of R packages designed for data science, including the use of readr, dplyr, tidyr, and ggplot2.

Difficulty level: Beginner
Duration: 1:01:39
Speaker: : Thomas Mock

The Medical Informatics Platform (MIP) is a platform providing federated analytics for diagnosis and research in clinical neuroscience research. The federated analytics is possible thanks to a distributed engine that executes computations and transfers information between the members of the federation (hospital nodes). In this talk the speaker will describe the process of designing and implementing new analytical tools, i.e. statistical and machine learning algorithms.  Mr. Sakellariou will further describe the environment in which these federated algorithms run, the challenges and the available tools, the principles that guide its design and the followed general methodology for each new algorithm. One of the most important challenges which are faced is to design these tools in a way that does not compromise the privacy of the clinical data involved. The speaker will show how to address the main questions when designing such algorithms: how to decompose and distribute the computations and what kind of information to exchange between nodes, in order to comply with the privacy constraint mentioned above. Finally, also the subject of validating these federated algorithms will be briefly touched.

Difficulty level: Intermediate
Duration: 20:26
Speaker: : Jason Skellariou

This lesson provides a hands-on tutorial for generating simulated brain data within the EBRAINS ecosystem. 

Difficulty level: Beginner
Duration: 32:58
Speaker: : Jil Meier