Explore how to setup an epileptic seizure simulation with the TVB graphical user interface. This lesson will show you how to program the epileptor model in the brain network to simulate a epileptic seizure originating in the hippocampus. It will also show how to upload and view mouse connectivity data, as well as give a short introduction to the python script interface of TVB.
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 lecture provides an introduction to the Brain Imaging Data Structure (BIDS), a standard for organizing human neuroimaging datasets.
This lecture covers the rationale for developing the DAQCORD, a framework for the design, documentation, and reporting of data curation methods in order to advance the scientific rigour, reproducibility, and analysis of data.
This is a continuation of the talk on the cellular mechanisms of neuronal communication, this time at the level of brain microcircuits and associated global signals like those measureable by electroencephalography (EEG). This lecture also discusses EEG biomarkers in mental health disorders, and how those cortical signatures may be simulated digitally.
This lecture gives an overview of how to prepare and preprocess neuroimaging (EEG/MEG) data for use in TVB.
This lecture goes into detailed description of how to process workflows in the virtual research environment (VRE), including approaches for standardization, metadata, containerization, and constructing and maintaining scientific pipelines.
In this third and final hands-on tutorial from the Research Workflows for Collaborative Neuroscience workshop, you will learn about workflow orchestration using open source tools like DataJoint and Flyte.
This lecture describes how to build research workflows, including a demonstrate using DataJoint Elements to build data pipelines.
This lesson breaks down the principles of Bayesian inference and how it relates to cognitive processes and functions like learning and perception. It is then explained how cognitive models can be built using Bayesian statistics in order to investigate how our brains interface with their environment.
This lesson corresponds to slides 1-64 in the PDF below.
This lesson delves into the human nervous system and the immense cellular, connectomic, and functional sophistication therein.