This talk gives an overview of the Human Brain Project, a 10-year endeavour putting in place a cutting-edge research infrastructure that will allow scientific and industrial researchers to advance our knowledge in the fields of neuroscience, computing, and brain-related medicine.
This lecture gives an introduction to the European Academy of Neurology, its recent achievements and ambitions.
This talk enumerates the challenges regarding data accessibility and reusability inherent in the current scientific publication system, and discusses novel approaches to these challenges, such as the EBRAINS Live Papers platform.
This lesson aims to define computational neuroscience in general terms, while providing specific examples of highly successful computational neuroscience projects.
This lesson covers membrane potential of neurons, and how parameters around this potential have direct consequences on cellular communication at both the individual and population level.
In this lesson you will learn about neurons' ability to generate signals called action potentials, and biophysics of voltage-gated ion channels.
This lesson discusses voltage-gating kinetics of sodium and potassium channels.
In this lesson, you will learn about the ionic basis of the action potential, including the Hodgkin-Huxley model.
This lesson delves into the specifics of how action potentials propagate through individual neurons.
This lesson discusses long-range inhibitory connections in the brain, with examples from three different systems.
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.
This lecture discusses the the importance and need for data sharing in clinical neuroscience.
This lecture gives insights into the Medical Informatics Platform's current and future data privacy model.
This lecture gives an overview on the European Health Dataspace.
This is a tutorial on designing a Bayesian inference model to map belief trajectories, with emphasis on gaining familiarity with Hierarchical Gaussian Filters (HGFs).
This lesson corresponds to slides 65-90 of the PDF below.
This lesson contains the first part of the lecture Data Science and Reproducibility. You will learn about the development of data science and what the term currently encompasses, as well as how neuroscience and data science intersect.
In this second part of the lecture Data Science and Reproducibility, you will learn how to apply the awareness of the intersection between neuroscience and data science (discussed in part one) to an understanding of the current reproducibility crisis in biomedical science and neuroscience.
In this lesson, you will learn about the current challenges facing the integration of machine learning and neuroscience.
JupyterHub is a simple, highly extensible, multi-user system for managing per-user Jupyter Notebook servers, designed for research groups or classes. This lecture covers deploying JupyterHub on a single server, as well as deploying with Docker using GitHub for authentication.
This demonstration walks through how to import your data into MATLAB.