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This hands-on tutorial explains how to run your own Minion session in the MetaCell cloud using jupityr notebooks.

Difficulty level: Beginner
Duration: 01:28:03

This lesson continues with the second workshop on reproducible science, focusing on additional open source tools for researchers and data scientists, such as the R programming language for data science, as well as associated tools like RStudio and R Markdown. Additionally, users are introduced to Python and iPython notebooks, Google Colab, and are given hands-on tutorials on how to create a Binder environment, as well as various containers in Docker and Singularity.

Difficulty level: Beginner
Duration: 1:16:04

This hands-on tutorial walks you through DataJoint platform, highlighting features and schema which can be used to build robost neuroscientific pipelines. 

Difficulty level: Beginner
Duration: 26:06
Speaker: : Milagros Marin

In this hands-on session, you will learn how to explore and work with DataLad datasets, containers, and structures using Jupyter notebooks. 

Difficulty level: Beginner
Duration: 58:05

This lesson contains practical exercises which accompanies the first few lessons of the Neuroscience for Machine Learners (Neuro4ML) course. 

Difficulty level: Intermediate
Duration: 5:58
Speaker: : Dan Goodman

This lesson introduces some practical exercises which accompany the Synapses and Networks portion of this Neuroscience for Machine Learners course. 

Difficulty level: Intermediate
Duration: 3:51
Speaker: : Dan Goodman

In this lesson, you will learn how to train spiking neural networks (SNNs) with a surrogate gradient method. 

Difficulty level: Intermediate
Duration: 11:23
Speaker: : Dan Goodman

This video briefly goes over the exercises accompanying Week 6 of the Neuroscience for Machine Learners (Neuro4ML) course, Understanding Neural Networks.

Difficulty level: Intermediate
Duration: 2:43
Speaker: : Marcus Ghosh

This tutorial provides instruction on how to simulate brain tumors with TVB (reproducing publication: Marinazzo et al. 2020 Neuroimage). This tutorial comprises a didactic video, jupyter notebooks, and full data set for the construction of virtual brains from patients and health controls.

Difficulty level: Intermediate
Duration: 10:01

The tutorial on modelling strokes in TVB includes a didactic video and jupyter notebooks (reproducing publication: Falcon et al. 2016 eNeuro).

Difficulty level: Intermediate
Duration: 7:43

This lesson provides a brief overview of the Python programming language, with an emphasis on tools relevant to data scientists.

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

This lesson gives a 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: 1:12:22
Speaker: : Ross Markello
Course:

This lesson covers 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: 2:38:45
Speaker: : Ross Markello

This lesson provides a hands-on, Jupyter-notebook-based tutorial to apply machine learning in Python to brain-imaging data.

Difficulty level: Beginner
Duration: 02:13:53
Speaker: : Jake Vogel

This lesson from freeCodeCamp introduces Scikit-learn, the most widely used machine learning Python library.

Difficulty level: Beginner
Duration: 02:09:22
Speaker: :
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
Course:

This book was written with the goal of introducing researchers and students in a variety of research fields to the intersection of data science and neuroimaging. This book reflects our own experience of doing research at the intersection of data science and neuroimaging and it is based on our experience working with students and collaborators who come from a variety of backgrounds and have a variety of reasons for wanting to use data science approaches in their work. The tools and ideas that we chose to write about are all tools and ideas that we have used in some way in our own research. Many of them are tools that we use on a daily basis in our work. This was important to us for a few reasons: the first is that we want to teach people things that we ourselves find useful. Second, it allowed us to write the book with a focus on solving specific analysis tasks. For example, in many of the chapters you will see that we walk you through ideas while implementing them in code, and with data. We believe that this is a good way to learn about data analysis, because it provides a connecting thread from scientific questions through the data and its representation to implementing specific answers to these questions. Finally, we find these ideas compelling and fruitful. That’s why we were drawn to them in the first place. We hope that our enthusiasm about the ideas and tools described in this book will be infectious enough to convince the readers of their value.

 

Difficulty level: Intermediate
Duration:
Speaker: :
Course:

This Jupyter Book is a series of interactive tutorials about quantitative T1 mapping, powered by qMRLab. Most figures are generated with Plot.ly – you can play with them by hovering your mouse over the data, zooming in (click and drag) and out (double click), moving the sliders, and changing the drop-down options. To view the code that was used to generate the figures in this blog post, hover your cursor in the top left corner of the frame that contains the tutorial and click the checkbox “All cells” in the popup that appears.

Jupyter Lab notebooks of these tutorials are also available through MyBinder, and inline code modification inside the Jupyter Book is provided by Thebelab. For both options, you can modify the code, change the figures, and regenerate the html that was used to create the tutorial below. This Jupyter Book also uses a Script of Scripts (SoS) kernel, allowing us to process the data using qMRLab in MATLAB/Octave and plot the figures with Plot.ly using Python, all within the same Jupyter Notebook.

Difficulty level: Intermediate
Duration:
Speaker: :

This tutorial demonstrates how to work with neuronal data using MATLAB, including actional potentials and spike counts, orientation tuing curves in visual cortex, and spatial maps of firing rates.

Difficulty level: Intermediate
Duration: 5:17
Speaker: : Mike X. Cohen

This lesson instructs users on how to import electrophysiological neural data into MATLAB, as well as how to convert spikes to a data matrix.

Difficulty level: Intermediate
Duration: 11:37
Speaker: : Mike X. Cohen