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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

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

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

This lecture and tutorial focuses on measuring human functional brain networks, as well as how to account for inherent variability within those networks. 

Difficulty level: Intermediate
Duration: 50:44
Speaker: : Caterina Gratton

This lecture presents an overview of functional brain parcellations, as well as a set of tutorials on bootstrap agregation of stable clusters (BASC) for fMRI brain parcellation.

Difficulty level: Advanced
Duration: 50:28
Speaker: : Pierre Bellec

This lesson describes spike timing-dependent plasticity (STDP), a biological process that adjusts the strength of connections between neurons in the brain, and how one can implement or mimic this process in a computational model. You will also find links for practical exercises at the bottom of this page. 

Difficulty level: Intermediate
Duration: 12:50
Speaker: : Dan Goodman

This lesson provides a brief introduction to the Computational Modeling of Neuronal Plasticity.

Difficulty level: Intermediate
Duration: 0:40

In this lesson, you will be introducted to a type of neuronal model known as the leaky integrate-and-fire (LIF) model.

Difficulty level: Intermediate
Duration: 1:23

This lesson goes over various potential inputs to neuronal synapses, loci of neural communication.

Difficulty level: Intermediate
Duration: 1:20

This lesson describes the how and why behind implementing integration time steps as part of a neuronal model.

Difficulty level: Intermediate
Duration: 1:08

In this lesson, you will learn about neural spike trains which can be characterized as having a Poisson distribution.

Difficulty level: Intermediate
Duration: 1:18

This lesson covers spike-rate adaptation, the process by which a neuron's firing pattern decays to a low, steady-state frequency during the sustained encoding of a stimulus.

Difficulty level: Intermediate
Duration: 1:26

This lesson provides a brief explanation of how to implement a neuron's refractory period in a computational model.

Difficulty level: Intermediate
Duration: 0:42

In this lesson, you will learn a computational description of the process which tunes neuronal connectivity strength, spike-timing-dependent plasticity (STDP).

Difficulty level: Intermediate
Duration: 2:40

This lesson reviews theoretical and mathematical descriptions of correlated spike trains.

Difficulty level: Intermediate
Duration: 2:54

This lesson investigates the effect of correlated spike trains on spike-timing dependent plasticity (STDP).

Difficulty level: Intermediate
Duration: 1:43

This lesson goes over synaptic normalisation, the homeostatic process by which groups of weighted inputs scale up or down their biases.

Difficulty level: Intermediate
Duration: 2:58

In this lesson, you will learn about the intrinsic plasticity of single neurons.

Difficulty level: Intermediate
Duration: 2:08

This lesson covers short-term facilitation, a process whereby a neuron's synaptic transmission is enhanced for a short (sub-second) period.

Difficulty level: Intermediate
Duration: 1:58

This lesson describes short-term depression, a reduction of synaptic information transfer between neurons.

Difficulty level: Intermediate
Duration: 1:40