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Explore how to setup an epileptic seizure simulation with the TVB graphical user interface. This lesson will show you how to program the epileptor model in the brain network to simulate a epileptic seizure originating in the hippocampus. It will also show how to upload and view mouse connectivity data, as well as give a short introduction to the python script interface of TVB.

Difficulty level: Intermediate
Duration: 58:06
Speaker: : Paul Triebkorn

Learn how to simulate seizure events and epilepsy in The Virtual Brain. We will look at the paper: On the Nature of Seizure Dynamics which describes a new local model called the Epileptor, and apply this same model in The Virtual Brain. This is part 1 of 2 in a series explaining how to use the Epileptor. In this part, we focus on setting up the parameters.

Difficulty level: Beginner
Duration: 4:44
Speaker: : Paul Triebkorn
Difficulty level: Beginner
Duration: 6:10
Speaker: : MATLAB®
Difficulty level: Beginner
Duration: 15:10
Speaker: : MATLAB®
Difficulty level: Beginner
Duration: 2:49
Speaker: : MATLAB®
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®
Difficulty level: Beginner
Duration: 3:55
Speaker: : MATLAB®
Difficulty level: Beginner
Duration: 3:52
Speaker: : MATLAB®

A brief overview of the Python programming language, with an emphasis on tools relevant to data scientists. This lecture was part of the 2018 Neurohackademy, a 2-week hands-on summer institute in neuroimaging and data science held at the University of Washington eScience Institute.

Difficulty level: Beginner
Duration: 1:16:36
Speaker: : Tal Yarkoni

This tutorial was part of the 2018 Neurohackademy, a 2-week hands-on summer institute in neuroimaging and data science held at the University of Washington eScience Institute.

Difficulty level: Intermediate
Duration: 1:26:02
Speaker: : Ariel Rokem

This tutorial was part of the 2018 Neurohackademy, a 2-week hands-on summer institute in neuroimaging and data science held at the University of Washington eScience Institute.

Difficulty level: Beginner
Duration: 1:21:40
Speaker: : Tal Yarkoni
Course:

Agah Karakuzu takes a spaghetti script written in MATLAB and turns it into understandable and reusable code living happily in a powerful GitHub repository.

Difficulty level: Beginner
Duration: 02:08:19
Speaker: :
Course:

Basic knowledge and comfort with Command Line Interfaces (CLI) is highly beneficial for learning how to use countless neuroscience tools and acquiring programming skills.  Furthermore, CLIs are better disposed to reproducibility, automation, concatenation in pipelines, execution on multiple platforms, and remote access.

 

Ross Markello takes you through this general introduction to the essentials of navigating through a Bash terminal environment.  The lesson is based on the Software Carpentries "Introduction to the Shell" and was given in the context of the BrainHack School 2020.

Difficulty level: Beginner
Duration: 01:12:22
Speaker: :
Course:

Ross Markello provides an overview of Python applications to data analysis, demonstrating why it has become ubiquitous in data science and neuroscience.

 

The lesson was given in the context of the BrainHack School 2020.

Difficulty level: Beginner
Duration: 02:38:45
Speaker: :

This is the Introductory Module to the Deep Learning Course at CDS, a course that covered the latest techniques in deep learning and representation learning, focusing on supervised and unsupervised deep learning, embedding methods, metric learning, convolutional and recurrent nets, with applications to computer vision, natural language understanding, and speech recognition. Prerequisites for this course include: Introduction to Data Science or a Graduate Level Machine Learning.

Difficulty level: Intermediate
Duration: 50:17

This module covers the concepts of gradient descent and the backpropagation algorithm and is a part of the Deep Learning Course at CDS, a course that covered the latest techniques in deep learning and representation learning, focusing on supervised and unsupervised deep learning, embedding methods, metric learning, convolutional and recurrent nets, with applications to computer vision, natural language understanding, and speech recognition. Prerequisites for this course include: Introduction to Data Science or a Graduate Level Machine Learning.

Difficulty level: Intermediate
Duration: 1:51:03
Speaker: : Yann LeCun

This lecture covers the concept of neural nets--rotation and squashing and is a part of the Deep Learning Course at CDS, a course that covered the latest techniques in deep learning and representation learning, focusing on supervised and unsupervised deep learning, embedding methods, metric learning, convolutional and recurrent nets, with applications to computer vision, natural language understanding, and speech recognition. Prerequisites for this course include: Introduction to Data Science or a Graduate Level Machine Learning.

Difficulty level: Intermediate
Duration: 1:01:53
Speaker: : Alfredo Canziani

This lecture covers the concept of neural nets training (tools, classification with neural nets, and PyTorch implementation) and is a part of the Deep Learning Course at CDS, a course that covered the latest techniques in deep learning and representation learning, focusing on supervised and unsupervised deep learning, embedding methods, metric learning, convolutional and recurrent nets, with applications to computer vision, natural language understanding, and speech recognition. Prerequisites for this course include: Introduction to Data Science or a Graduate Level Machine Learning.

Difficulty level: Intermediate
Duration: 1:05:47
Speaker: : Alfredo Canziani

This lecture covers the concept of parameter sharing: recurrent and convolutional nets and is a part of the Deep Learning Course at CDS, a course that covered the latest techniques in deep learning and representation learning, focusing on supervised and unsupervised deep learning, embedding methods, metric learning, convolutional and recurrent nets, with applications to computer vision, natural language understanding, and speech recognition. Prerequisites for this course include: Introduction to Deep Learning and Introduction to Data Science or a Graduate Level Machine Learning.

Difficulty level: Intermediate
Duration: 1:59:47