Course:

This tutorial demonstrates how to work with neuronal data using MATLAB, including actional potentials and spike counts, orientation tuing curves in visual cortex, and spatial maps of firing rates.

Difficulty level: Intermediate

Duration: 5:17

Speaker: : Mike X. Cohen

Course:

This lesson instructs users on how to import electrophysiological neural data into MATLAB, as well as how to convert spikes to a data matrix.

Difficulty level: Intermediate

Duration: 11:37

Speaker: : Mike X. Cohen

Course:

In this lesson, users will learn how to appropriately sort and bin neural spikes, allowing for the generation of a common and powerful visualization tool in neuroscience, the histogram.

Difficulty level: Intermediate

Duration: 5:31

Speaker: : Mike X. Cohen

Course:

Followers of this lesson will learn how to compute, visualize and quantify the tuning curves of individual neurons.

Difficulty level: Intermediate

Duration: 13:48

Speaker: : Mike X. Cohen

Course:

This lesson demonstrates how to programmatically generate a spatial map of neuronal spike counts using MATLAB.

Difficulty level: Intermediate

Duration: 12:16

Speaker: : Mike X. Cohen

Course:

In this lesson, users are shown how to create a spatial map of neuronal orientation tuning.

Difficulty level: Intermediate

Duration: 13:11

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 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.

Difficulty level: Intermediate

Duration: 1:11:04

Speaker: : Etay Hay

Course:

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.

Difficulty level: Intermediate

Duration: 1:47:22

Speaker: : Erin Dickie and John Griffiths

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

This is a tutorial on designing a Bayesian inference model to map belief trajectories, with emphasis on gaining familiarity with Hierarchical Gaussian Filters (HGFs).

This lesson corresponds to slides 65-90 of the PDF below.

Difficulty level: Intermediate

Duration: 1:15:04

Speaker: : Daniel Hauke

This lesson briefly goes over the outline of the Neuroscience for Machine Learners course.

Difficulty level: Intermediate

Duration: 3:05

Speaker: : Dan Goodman

This lesson delves into the the structure of one of the brain's most elemental computational units, the neuron, and how said structure influences computational neural network models.

Difficulty level: Intermediate

Duration: 6:33

Speaker: : Marcus Ghosh

In this lesson you will learn how machine learners and neuroscientists construct abstract computational models based on various neurophysiological signalling properties.

Difficulty level: Intermediate

Duration: 10:52

Speaker: : Dan Goodman

- Bayesian networks (2)
- Clinical neuroinformatics (2)
- Standards and Best Practices (1)
- Neuroimaging (19)
- Machine learning (9)
- Neuromorphic engineering (3)
- Tools (1)
- Animal models (1)
- Brain-hardware interfaces (1)
- (-) Clinical neuroscience (1)
- (-) General neuroscience (15)
- (-) Computational neuroscience (12)
- Statistics (5)
- (-) Computer Science (2)
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- (-) Open science (4)