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In this tutorial, users will learn how to create a trial-averaged BOLD response and store it in a matrix in MATLAB. 

Difficulty level: Intermediate
Duration: 20:12
Speaker: : Mike X. Cohen

This tutorial teaches users how to create animations of BOLD responses over time, to allow researchers and clinicians to visualize time-course activity patterns.

Difficulty level: Intermediate
Duration: 12:52
Speaker: : Mike X. Cohen

This tutorial demonstrates how to use MATLAB to create event-related BOLD time courses from fMRI datasets. 

Difficulty level: Intermediate
Duration: 13:39
Speaker: : Mike X. Cohen

In this tutorial, users learn how to compute and visualize a t-test on experimental condition differences.

Difficulty level: Intermediate
Duration: 17:54
Speaker: : Mike X. Cohen

This lesson introduces various methods in MATLAB useful for dealing with data generated by calcium imaging. 

Difficulty level: Intermediate
Duration: 5:02
Speaker: : Mike X. Cohen

This tutorial demonstrates how to use MATLAB to generate and visualize animations of calcium fluctuations over time. 

Difficulty level: Intermediate
Duration: 15:01
Speaker: : Mike X. Cohen

This tutorial instructs users how to use MATLAB to programmatically convert data from cells to a matrix.

Difficulty level: Intermediate
Duration: 5:15
Speaker: : Mike X. Cohen

In this tutorial, users will learn how to identify and remove background noise, or "blur", an important step in isolating cell bodies from image data. 

Difficulty level: Intermediate
Duration: 17:08
Speaker: : Mike X. Cohen

This lesson teaches users how MATLAB can be used to apply image processing techniques to identify cell bodies based on contiguity.

Difficulty level: Intermediate
Duration: 11:23
Speaker: : Mike X. Cohen

This tutorial demonstrates how to extract the time course of calcium activity from each clusters of neuron somata, and store the data in a MATLAB matrix.

Difficulty level: Intermediate
Duration: 22:41
Speaker: : Mike X. Cohen

This lesson demonstrates how to use MATLAB to implement a multivariate dimension reduction method, PCA, on time series data.

Difficulty level: Intermediate
Duration: 17:19
Speaker: : Mike X. Cohen

This lesson is a general overview of overarching concepts in neuroinformatics research, with a particular focus on clinical approaches to defining, measuring, studying, diagnosing, and treating various brain disorders. Also described are the complex, multi-level nature of brain disorders and the data associated with them, from genes and individual cells up to cortical microcircuits and whole-brain network dynamics. Given the heterogeneity of brain disorders and their underlying mechanisms, this lesson lays out a case for multiscale neuroscience data integration.

Difficulty level: Intermediate
Duration: 1:09:33
Speaker: : Sean Hill

This is a tutorial introducing participants to the basics of RNA-sequencing data and how to analyze its features using Seurat. 

Difficulty level: Intermediate
Duration: 1:19:17
Speaker: : Sonny Chen

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 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. 

Difficulty level: Intermediate
Duration: 1:28:14

This lesson provides an overview of the current status in the field of neuroscientific ontologies, presenting examples of data organization and standards, particularly from neuroimaging and electrophysiology. 

Difficulty level: Intermediate
Duration: 33:41

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

Following the previous lesson on neuronal structure, this lesson discusses neuronal function, particularly focusing on spike triggering and propogation. 

Difficulty level: Intermediate
Duration: 6:58
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

In this lesson, you will learn about some typical neuronal models employed by machine learners and computational neuroscientists, meant to imitate the biophysical properties of real neurons. 

Difficulty level: Intermediate
Duration: 3:12
Speaker: : Dan Goodman