This lecture and tutorial focuses on measuring human functional brain networks. The lecture and tutorial were part of the 2019 Neurohackademy, a 2-week hands-on summer institute in neuroimaging and data science held at the University of Washington eScience Institute.
Lecture on functional brain parcellations and a set of tutorials on bootstrap agregation of stable clusters (BASC) for fMRI brain parcellation which were part of the 2019 Neurohackademy, a 2-week hands-on summer institute in neuroimaging and data science held at the University of Washington eScience Institute.
Introduction to the central concepts of machine learning, and how they can be applied in Python using the Scikit-learn Package. This lecture was part of the 2018 Neurohackademy, a 2-week hands-on summer institute in neuroimaging and data science held at the University of Washington eScience Institute.
Much like neuroinformatics, data science uses techniques from computational science to derive meaningful results from large complex datasets. In this session, we will explore the relationship between neuroinformatics and data science, by emphasizing a range of data science approaches and activities, ranging from the development and application of statistical methods, through the establishment of communities and platforms, and through the implementation of open-source software tools. Rather than rigid distinctions, in the data science of neuroinformatics, these activities and approaches intersect and interact in dynamic ways. Together with a panel of cutting-edge neuro-data-scientist speakers, we will explore these dynamics
This lecture covers self-supervision as it relates to neural data tasks and the Mine Your Own vieW (MYOW) approach.
Jake Vogel gives a hands-on, Jupyter-notebook-based tutorial to apply machine learning in Python to brain-imaging data.
The lesson was presented in the context of the BrainHack School 2020.
This lecture introduces you to the basics of the Amazon Web Services public cloud. It covers the fundamentals of cloud computing and go through both motivation and process involved in moving your research computing to the cloud. This lecture was part of the 2018 Neurohackademy, a 2-week hands-on summer institute in neuroimaging and data science held at the University of Washington eScience Institute.
Shawn Brown presents an overview of CBRAIN, a web-based platform that allows neuroscientists to perform computationally intensive data analyses by connecting them to high-performance-computing facilities across Canada and around the world.
This talk was given in the context of a Ludmer Centre event in 2019.
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.
Overview of the Braintorm package for analyzing extracellular electrophysiology, including preprocessing, spike sorting, trial alignment, and spectrotemporal decomposition
Overview of the CaImAn package, and demonstration of usage with NWB
Overview of the SpikeInterface package, including demonstration of data loading, preprocessing, spike sorting, and comparison of spike sorters
Overview of the NWBWidgets package, including coverage of different data types, and information for building custom widgets within this framework
Since their introduction in 2016, the FAIR data principles have gained increasing recognition and adoption in global neuroscience. FAIR defines a set of high level principles and practices for making digital objects, including data, software and workflows, Findable, Accessible, Interoperable and Reusable. But FAIR is not a specification; it leaves many of the specifics up to individual scientific disciplines to define. INCF has been leading the way in promoting, defining and implementing FAIR data practices for neuroscience. We have been bringing together researchers, infrastructure providers, industry and publishers through our programs and networks. In this session, we will hear some perspectives on FAIR neuroscience from some of these stakeholders who have been working to develop and use FAIR tools for neuroscience. We will engage in a discussion on questions such as: how is neuroscience doing with respect to FAIR? What have been successes? What is currently very difficult? Where does neuroscience need to go?
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.