In this lesson, the simulation of a virtual epileptic patient is presented as an example of advanced brain simulation as a translational approach to deliver improved clinical results. You will learn about the fundamentals of epilepsy, as well as the concepts underlying epilepsy simulation. By using an iPython notebook, the detailed process of this approach is explained step by step. In the end, you are able to perform simple epilepsy simulations your own.
This module covers many of the types of non-invasive neurotech and neuroimaging devices including electroencephalography (EEG), electromyography (EMG), electroneurography (ENG), magnetoencephalography (MEG), and more.
This lesson provides a brief overview of the Python programming language, with an emphasis on tools relevant to data scientists.
In this lesson, users can follow along as a spaghetti script written in MATLAB is turned into understandable and reusable code living happily in a powerful GitHub repository.
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.
This lecture covers the linking neuronal activity to behavior using AI-based online detection.
This lesson gives an in-depth introduction of ethics in the field of artificial intelligence, particularly in the context of its impact on humans and public interest. As the healthcare sector becomes increasingly affected by the implementation of ever stronger AI algorithms, this lecture covers key interests which must be protected going forward, including privacy, consent, human autonomy, inclusiveness, and equity.
This lesson describes a definitional framework for fairness and health equity in the age of the algorithm. While acknowledging the impressive capability of machine learning to positively affect health equity, this talk outlines potential (and actual) pitfalls which come with such powerful tools, ultimately making the case for collaborative, interdisciplinary, and transparent science as a way to operationalize fairness in health equity.
This lesson provides a conceptual overview of the rudiments of machine learning, including its bases in traditional statistics and the types of questions it might be applied to. The lesson was presented in the context of the BrainHack School 2020.
This lesson provides a hands-on, Jupyter-notebook-based tutorial to apply machine learning in Python to brain-imaging data.
This lesson presents advanced machine learning algorithms for neuroimaging, while addressing some real-world considerations related to data size and type.
In this lecture, attendees will learn about the opportunities and challenges associated with Recurrent Neural Networks (RNNs), which, when trained with machine learning techniques on cognitive tasks, have become a widely accepted tool for neuroscientists.
This lecture provides an introduction to optogenetics, a biological technique to control the activity of neurons or other cell types with light.
This lesson provides an overview of Neurodata Without Borders (NWB), an ecosystem for neurophysiology data standardization. The lecture also introduces some NWB-enabled tools.
This lecture discusses the FAIR principles as they apply to electrophysiology data and metadata, the building blocks for community tools and standards, platforms and grassroots initiatives, and the challenges therein.
This lecture contains an overview of electrophysiology data reuse within the EBRAINS ecosystem.
This lecture contains an overview of the Distributed Archives for Neurophysiology Data Integration (DANDI) archive, its ties to FAIR and open-source, integrations with other programs, and upcoming features.
This lecture contains an overview of the Australian Electrophysiology Data Analytics Platform (AEDAPT), how it works, how to scale it, and how it fits into the FAIR ecosystem.
This lecture discusses how to standardize electrophysiology data organization to move towards being more FAIR.
This lecture will provide an overview of the INCF Training Suite, a collection of tools that embraces the FAIR principles developed by members of the INCF Community. This will include an overview of TrainingSpace, Neurostars, and KnowledgeSpace.