This lecture provides a history of data management, recent developments data management, and a brief description of scientific data management.
This lesson briefly goes over the outline of the Neuroscience for Machine Learners course.
This tutorial covers the fundamentals of collaborating with Git and GitHub.
This talk presents state-of-the-art methods for ensuring data privacy with a particular focus on medical data sharing across multiple organizations.
This lecture talks about the usage of knowledge graphs in hospitals and related challenges of semantic interoperability.
This lesson describes the principles underlying functional magnetic resonance imaging (fMRI), diffusion-weighted imaging (DWI), tractography, and parcellation. These tools and concepts are explained in a broader context of neural connectivity and mental health.
This tutorial introduces pipelines and methods to compute brain connectomes from fMRI data. With corresponding code and repositories, participants can follow along and learn how to programmatically preprocess, curate, and analyze functional and structural brain data to produce connectivity matrices.
This lesson introduces the practical exercises which accompany the previous lessons on animal and human connectomes in the brain and nervous system.
This lecture and tutorial focuses on measuring human functional brain networks, as well as how to account for inherent variability within those networks.
This lecture presents an overview of functional brain parcellations, as well as a set of tutorials on bootstrap agregation of stable clusters (BASC) for fMRI brain parcellation.
This video gives a brief introduction to Neuro4ML's lessons on neuromorphic computing - the use of specialized hardware which either directly mimics brain function or is inspired by some aspect of the way the brain computes.
In this lesson, you will learn in more detail about neuromorphic computing, that is, non-standard computational architectures that mimic some aspect of the way the brain works.
This video provides a very quick introduction to some of the neuromorphic sensing devices, and how they offer unique, low-power applications.
This lesson presents a simulation software for spatial model neurons and their networks designed primarily for GPUs.
The lecture covers a brief introduction to neuromorphic engineering, some of the neuromorphic networks that the speaker has developed, and their potential applications, particularly in machine learning.
Learn how to create a standard extracellular electrophysiology dataset in NWB using Python.
Learn how to create a standard calcium imaging dataset in NWB using Python.
In this tutorial, you will learn how to create a standard intracellular electrophysiology dataset in NWB using Python.
In this tutorial, you will learn how to use the icephys-metadata extension to enter meta-data detailing your experimental paradigm.
This lesson provides instructions on how to build and share extensions in NWB.