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.
In this lesson you will learn how to simulate seizure events and epilepsy in The Virtual Brain. We will look at the paper On the Nature of Seizure Dynamics, which describes a new local model called the Epileptor, and apply this same model in The Virtual Brain. This is part 1 of 2 in a series explaining how to use the Epileptor. In this part, we focus on setting up the parameters.
This lecture provides an introduction to optogenetics, a biological technique to control the activity of neurons or other cell types with light.
EyeWire is a game to map the brain. Players are challenged to map branches of a neuron from one side of a cube to the other in a 3D puzzle. Players scroll through the cube and reconstruct neurons with the help of an artificial intelligence algorithm developed at Seung Lab in Princeton University. EyeWire gameplay advances neuroscience by helping researchers discover how neurons connect to process visual information.
This module explains how neurons come together to create the networks that give rise to our thoughts. The totality of our neurons and their connection is called our connectome. Learn how this connectome changes as we learn, and computes information.
This lecture provides an introduction to the study of eye-tracking in humans.
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.
This demonstration walks through how to import your data into MATLAB.
This lesson provides instruction regarding the various factors one must consider when preprocessing data, preparing it for statistical exploration and analyses.
This tutorial outlines, step by step, how to perform analysis by group and how to do change-point detection.
This tutorial walks through several common methods for visualizing your data in different ways depending on your data type.
This tutorial illustrates several ways to approach predictive modeling and machine learning with MATLAB.
This brief tutorial goes over how you can easily work with big data as you would with any size of data.
In this tutorial, you will learn how to deploy your models outside of your local MATLAB environment, enabling wider sharing and collaboration.
This lesson provides a brief overview of the Python programming language, with an emphasis on tools relevant to data scientists.
This lecture gives an introduction to the FAIR (findability, accessibility, interoperability, and reusability) science principles and examples of their application in neuroscience research.
The lecture provides an overview of the core skills and practical solutions required to practice reproducible research.
This lecture covers the description and brief history of data science and its use in neuroinformatics.
In this talk, you will learn how brainlife.io works, and how it can be applied to neuroscience data.