Neuromatch Academy aims to introduce traditional and emerging tools of computational neuroscience to trainees.

This course contains sessions from the first day of INCF's Neuroinformatics Assembly 2022.

This course provides several visual walkthroughs documenting how to execute various processes in brainlife.io, an open-source, free and secure reproducible neuroscience analysis platform. The platform allows to analyze Magnetic Resonance Imaging (MRI), electroencephalography (EEG) and magnetoencephalography (MEG) data. Data can either be uploaded from local computers or imported from public archives such as OpenNeuro.org.

Notebook systems are proving invaluable to skill acquisition, research documentation, publication, and reproducibility.  This series of presentations introduces the most popular platform for computational notebooks, Project Jupyter, as well as other resources like Binder and NeuroLibre. 

This is a freely available online course on neuroscience for people with a machine learning background. The aim is to bring together these two fields that have a shared goal in understanding intelligent processes. Rather than pushing for “neuroscience-inspired” ideas in machine learning, the idea is to broaden the conceptions of both fields to incorporate elements of the other in the hope that this will lead to new, creative thinking.

Neuromatch Academy aims to introduce traditional and emerging tools of computational neuroscience to trainees.

In this short course, you will learn about Jupyter Notebooks, an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text. Uses include: data cleaning and transformation, numerical simulation, statistical modeling, data visualization, machine learning, and much more.

As models in neuroscience have become increasingly complex, it has become more difficult to share all aspects of models and model analysis, hindering model accessibility and reproducibility. In this session, we will discuss existing resources for promoting FAIR data and models in computational neuroscience, their impact on the field, and remaining barriers.

This module covers the concepts of gradient descent, stochastic gradient descent, and momentum. It is a part of the Deep Learning Course at NYU's Center for Data Science, 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. Pr

Neuromatch Academy aims to introduce traditional and emerging tools of computational neuroscience to trainees.

This course consists of one lesson and one tutorial, focusing on the neural connectivity measures derived from neuroimaging, specifically from methods like functional magnetic resonance imaging (fMRI) and diffusion-weighted imaging (DWI). Additional tools such as tractography and parcellation are discussed in the context of brain connectivity and mental health. The tutorial leads participants through the computation of brain connectomes from fMRI data. 

This module covers the concept of associative memories in deep learning. It is a part of the Deep Learning Course at NYU's Center for Data Science. Prerequisites for this module include: Introduction to Deep Learning (module 1 of the course), Parameter Sharing (module 2 of the course), 

This course consists of two workshops which focus on the need for reproducibility in science, particularly under the umbrella roadmap of FAIR scienctific principles. The tutorials also provide an introduction to some of the most commonly used open-source scientific tools, including Git, GitHub, Google Colab, Binder, Docker, and the programming languages Python and R. 

EEGLAB is an interactive MATLAB toolbox for processing continuous and event-related EEG, MEG, and other electrophysiological data incorporating independent component analysis (ICA), time/frequency analysis, artifact rejection, event-related statistics, and several useful modes of visualization of the averaged and single-trial data.

This workshop provides basic knowledge on personalized brain network modeling using the open-source simulation platform The Virtual Brain (TVB). Participants will gain theoretical knowledge and apply this knowledge to construct brain models, process multimodal neuroimaging data for reconstructing individual brains, run simulations, and use supporting neuroinformatics tools such as collaboratories, pipelines, workflows, and data repositories.

This course contains sessions from the first day of INCF's Neuroinformatics Assembly 2022.

Presented by the Neuroscience Information Framework (NIF), this series consists of several lectures characterizing cutting-edge, open-source software platforms and computational tools for neuroscientists. This course offers detailed descriptions of various neuroinformatic resources such as cloud-computing services, web-based annotation tools, genome browsers, and platforms for designing and building biophysically detailed models of neurons and neural ensembles.

In this short series of lectures, participants will take a look at articles using TVB in a clinical context. Specifically, participants will see how TVB can help to predict recovery after stroke and how individual epileptic seizures are simulated. The course lecturers will briefly describe the methods used and results achieved in the articles.

This course tackles the issue of maintaining ethical research and healthcare practices in the age of increasingly powerful technological tools like machine learning and artificial intelligence. While there is great potential for innovation and improvement in the clinical space thanks to AI development, lecturers in this course advocate for a greater emphasis on human-centric care, calling for algorithm design which takes the full intersectionality of individuals into account.

This workshop delves into the need for, structure of, tools for, and use of hierarchical event descriptor (HED) annotation to prepare neuroimaging time series data for storing, sharing, and advanced analysis. HED are a controlled vocabulary of terms describing events in a machine-actionable form so that algorithms can use the information without manual recoding.