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 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

Speaker: : Andreea Diaconescu

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

Similarity Network Fusion (SNF) is a computational method for data integration across various kinds of measurements, aimed at taking advantage of the common as well as complementary information in different data types. This workshop walks participants through running SNF on EEG and genomic data using RStudio.

Difficulty level: Intermediate

Duration: 1:21:38

Speaker: : Dan Felsky

Course:

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

Course:

This lesson provides an overview of Jupyter notebooks, Jupyter lab, and Binder, as well as their applications within the field of neuroimaging, particularly when it comes to the writing phase of your research.

Difficulty level: Intermediate

Duration: 50:28

Speaker: : Elizabeth DuPre

This lecture gives an overview of how to prepare and preprocess neuroimaging (EEG/MEG) data for use in TVB.

Difficulty level: Intermediate

Duration: 1:40:52

Speaker: : Paul Triebkorn

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

Difficulty level: Intermediate

Duration: 0:40

Speaker: : Florence I. Kleberg

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

Speaker: : Florence I. Kleberg

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

Difficulty level: Intermediate

Duration: 1:20

Speaker: : Florence I. Kleberg

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

Difficulty level: Intermediate

Duration: 1:08

Speaker: : Florence I. Kleberg

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

Speaker: : Florence I. Kleberg

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

Speaker: : Florence I. Kleberg

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

Speaker: : Florence I. Kleberg

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

Speaker: : Florence I. Kleberg

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

Difficulty level: Intermediate

Duration: 2:54

Speaker: : Florence I. Kleberg

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

Difficulty level: Intermediate

Duration: 1:43

Speaker: : Florence I. Kleberg

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

Speaker: : Florence I. Kleberg

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

Difficulty level: Intermediate

Duration: 2:08

Speaker: : Florence I. Kleberg

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