This module is intended to provide a foundation in energy-based models. 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),

These lessons give an overview of the principles underpinning the objectives, policies, and practice of Open Science, including several representative policy documents that will be increasingly relevant to neuroscience research.

This course corresponds to the second session of INCF's Neuroinformatics Assembly 2023. This series of talks continues a discussion of FAIR principles from the first session, with a greater emphasis on brain data (humans and animals) atlases for data analysis and integation. 

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 course consists of a series of webinars organized by the International Neuroethics Society on various neuroethics topics. 

As research methods and experimental technologies become ever more sophisticated, the amount of health-related data per individual which has become accessible is vast, giving rise to a corresponding need for cross-domain data integration, whole-person modelling, and improved precision medicine. This course provides lessons describing state of the art methods and repositories, as well as a tutorial on computational methods for data integration. 

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.

The field of neuroscience is one of the most interdisciplinary scientific fields. It is constantly expanded and developed further and unites researchers from a vast variety of backgrounds such as chemistry, biology, physics, medicine, or psychology. By examining the principles that influence the development and function of the human nervous system, it advances the understanding of the fundamental mechanisms of human behaviour, emotions, and thoughts, and what happens if they fail.

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.

These courses give introductions and overviews of some of the major statistics software packages currently used in neuroscience research.

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 course consists of a series of lessons which aim to introduce the basic conceptual and experimental approaches in computational neuroscience. 

This course offers lectures on the origin and functional significance of certain electrophysiological signals in the brain, as well as a hands-on tutorial on how to simulate, statistically evaluate, and visualize such signals. Participants will learn the simulation of signals at different spatial scales, including single-cell (neuronal spiking) and global (EEG), and how these may serve as biomarkers in the evaluation of mental health data.

This course offers lectures on the origin and functional significance of certain electrophysiological signals in the brain, as well as a hands-on tutorial on how to simulate, statistically evaluate, and visualize such signals. Participants will learn the simulation of signals at different spatial scales, including single-cell (neuronal spiking) and global (EEG), and how these may serve as biomarkers in the evaluation of mental health data.

As research methods and experimental technologies become ever more sophisticated, the amount of health-related data per individual which has become accessible is vast, giving rise to a corresponding need for cross-domain data integration, whole-person modelling, and improved precision medicine. This course provides lessons describing state of the art methods and repositories, as well as a tutorial on computational methods for data integration. 

These lessons give an overview of the principles underpinning the objectives, policies, and practice of Open Science, including several representative policy documents that will be increasingly relevant to neuroscience research.

This course is intended for those interested in electroencephalography (EEG) and event-related potentials (ERPs) techniques, and those interested in collecting, annotating, standardizing, storing, processing, sharing, and publishing data from electrical activity of the human brain.

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.

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

This workshop is organized by the German National Research Data Infrastructure Initiative Neuroscience (NFDI-Neuro). The initiative is community driven and comprises around 50 contributing national partners and collaborators. NFDI-Neuro partners with EBRAINS AISB, the coordinating entity of the EU Human Brain Project and the EBRAINS infrastructure. We will introduce common methods that enable digital reproducible neuroscience.