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This tutorial walks participants through the application of dynamic causal modelling (DCM) to fMRI data using MATLAB. Participants are also shown various forms of DCM, how to generate and specify different models, and how to fit them to simulated neural and BOLD data.

 

This lesson corresponds to slides 158-187 of the PDF below. 

Difficulty level: Advanced
Duration: 1:22:10

Optimization for machine learning - Day 02 lecture of the Foundations of Machine Learning in Python course.

High-Performance Computing and Analytics Lab, University of Bonn

Difficulty level: Advanced
Duration: 34:52
Speaker: : Moritz Wolter

Linear Algebra for Machine Learning - Day 03 lecture of the Foundations of Machine Learning in Python course.

High-Performance Computing and Analytics Lab, University of Bonn

Difficulty level: Advanced
Duration: 57.45
Speaker: : Moritz Wolter

Support Vector Machines -  Day 06 lecture of the  Foundations of Machine Learning in Python course.

High-Performance Computing and Analytics Lab, University of Bonn

Difficulty level: Advanced
Duration: 53.39
Speaker: : Elena Trunz

Decision Trees and Random Forests -  Day 07 lecture of the  Foundations of Machine Learning in Python course.

High-Performance Computing and Analytics Lab, University of Bonn

Difficulty level: Advanced
Duration: 1:15:39
Speaker: : Elena Trunz

Clustering and Density Estimation -  Day 08 lecture of the  Foundations of Machine Learning in Python course.

High-Performance Computing and Analytics Lab, University of Bonn

Difficulty level: Advanced
Duration: 59:35
Speaker: : Elena Trunz

Dimensionality Reduction -  Day 09 lecture of the  Foundations of Machine Learning in Python course.

High-Performance Computing and Analytics Lab, University of Bonn

Difficulty level: Advanced
Duration: 51:02
Speaker: : Elena Trunz

Introduction to Neural Networks -  Day 10 lecture of the  Foundations of Machine Learning in Python course.

High-Performance Computing and Analytics Lab, University of Bonn

Difficulty level: Advanced
Duration: 54:12
Speaker: : Moritz Wolter

Introduction to Convolutional Neural Networks  -  Day 11 lecture of the  Foundations of Machine Learning in Python course.

High-Performance Computing and Analytics Lab, University of Bonn

Difficulty level: Advanced
Duration: 42:07
Speaker: : Moritz Wolter

Initialization, Optimization, and Regularization  -  Day 12 lecture of the  Foundations of Machine Learning in Python course.

High-Performance Computing and Analytics Lab, University of Bonn

Difficulty level: Advanced
Duration: 42:07
Speaker: : Moritz Wolter

U-Nets for medical Image-Segmentation  -  Day 13 lecture of the  Foundations of Machine Learning in Python course.

High-Performance Computing and Analytics Lab, University of Bonn

Difficulty level: Advanced
Duration: 16:45
Speaker: : Moritz Wolter

Sequence Processing -  Day 15 lecture of the  Foundations of Machine Learning in Python course.

High-Performance Computing and Analytics Lab, University of Bonn

Difficulty level: Advanced
Duration: 47:45
Speaker: : Moritz Wolter

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 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

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

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

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 lecture introduces neuroscience concepts and methods such as fMRI, visual respones in BOLD data, and the eccentricity of visual receptive fields. 

Difficulty level: Intermediate
Duration: 7:15
Speaker: : Mike X. Cohen

This tutorial walks users through the creation and visualization of activation flat maps from fMRI datasets. 

Difficulty level: Intermediate
Duration: 12:15
Speaker: : Mike X. Cohen

This tutorial demonstrates to users the conventional preprocessing steps when working with BOLD signal datasets from fMRI. 

Difficulty level: Intermediate
Duration: 12:05
Speaker: : Mike X. Cohen