Course:

This lecture describes the principles of EEG electrode placement in both 2- and 3-dimensional formats.

Difficulty level: Intermediate

Duration: 12:16

Speaker: : Mike X. Cohen

Course:

This tutorial walks users through performing Fourier Transform (FFT) spectral analysis of a single EEG channel using MATLAB.

Difficulty level: Intermediate

Duration: 13:39

Speaker: : Mike X. Cohen

Course:

This tutorial builds on the previous lesson's demonstration of spectral analysis of one EEG channel. Here, users will learn how to compute and visualize spectral power from all EEG channels using MATLAB.

Difficulty level: Intermediate

Duration: 12:34

Speaker: : Mike X. Cohen

Course:

In this lesson, users will learn more about the steady-state visually evoked potential (SSEVP), as well as how to create and interpret topographical maps derived from such studies.

Difficulty level: Intermediate

Duration: 9:10

Speaker: : Mike X. Cohen

Course:

This lesson teaches users how to extract edogenous brain waves from EEG data, specifically oscillations constrained to the 8-12 Hz frequency band, conventionally named alpha.

Difficulty level: Intermediate

Duration: 13:23

Speaker: : Mike X. Cohen

Course:

In the final lesson of this module, users will learn how to correlate endogenous alpha power with SSVEP amplitude from EEG data using MATLAB.

Difficulty level: Intermediate

Duration: 12:36

Speaker: : Mike X. Cohen

Course:

This lesson provides an introduction to biologically detailed computational modelling of neural dynamics, including neuron membrane potential simulation and F-I curves.

Difficulty level: Intermediate

Duration: 8:21

Speaker: : Mike X. Cohen

Course:

In this lesson, users learn how to use MATLAB to build an adaptive exponential integrate and fire (AdEx) neuron model.

Difficulty level: Intermediate

Duration: 22:01

Speaker: : Mike X. Cohen

Course:

In this lesson, users learn about the practical differences between MATLAB scripts and functions, as well as how to embed their neuronal simulation into a callable function.

Difficulty level: Intermediate

Duration: 11:20

Speaker: : Mike X. Cohen

Course:

This lesson teaches users how to generate a frequency-current (F-I) curve, which describes the function that relates the net synaptic current (I) flowing into a neuron to its firing rate (F).

Difficulty level: Intermediate

Duration: 20:39

Speaker: : Mike X. Cohen

This is the first of two workshops on reproducibility in science, during which participants are introduced to concepts of FAIR and open science. After discussing the definition of and need for FAIR science, participants are walked through tutorials on installing and using Github and Docker, the powerful, open-source tools for versioning and publishing code and software, respectively.

Difficulty level: Intermediate

Duration: 1:20:58

Speaker: : Erin Dickie and Sejal Patel

This is a hands-on tutorial on PLINK, the open source whole genome association analysis toolset. The aims of this tutorial are to teach users how to perform basic quality control on genetic datasets, as well as to identify and understand GWAS summary statistics.

Difficulty level: Intermediate

Duration: 1:27:18

Speaker: : Dan Felsky

This is a tutorial on using the open-source software PRSice to calculate a set of polygenic risk scores (PRS) for a study sample. Users will also learn how to read PRS into R, visualize distributions, and perform basic association analyses.

Difficulty level: Intermediate

Duration: 1:53:34

Speaker: : Dan Felsky

This is an in-depth guide on EEG signals and their interaction within brain microcircuits. Participants are also shown techniques and software for simulating, analyzing, and visualizing these signals.

Difficulty level: Intermediate

Duration: 1:30:41

Speaker: : Frank Mazza

Course:

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.

Difficulty level: Intermediate

Duration: 1:39:04

Speaker: : Erin Dickie and John Griffiths

Course:

In this tutorial on simulating whole-brain activity using Python, participants can follow along using corresponding code and repositories, learning the basics of neural oscillatory dynamics, evoked responses and EEG signals, ultimately leading to the design of a network model of whole-brain anatomical connectivity.

Difficulty level: Intermediate

Duration: 1:16:10

Speaker: : John Griffiths

This lesson contains practical exercises which accompanies the first few lessons of the Neuroscience for Machine Learners (Neuro4ML) course.

Difficulty level: Intermediate

Duration: 5:58

Speaker: : Dan Goodman

This lesson introduces some practical exercises which accompany the Synapses and Networks portion of this Neuroscience for Machine Learners course.

Difficulty level: Intermediate

Duration: 3:51

Speaker: : Dan Goodman

This lesson introduces the practical exercises which accompany the previous lessons on animal and human connectomes in the brain and nervous system.

Difficulty level: Intermediate

Duration: 4:10

Speaker: : Dan Goodman

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.

Difficulty level: Intermediate

Duration: 58:06

Speaker: : Paul Triebkorn

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