Question and Answer Session for Asian and Australian participants.

Question and Answer Session for African and European participants. 

This lecture focuses on the purpose of model fitting, approaches to model fitting, model fitting for linear models, and how to assess the quality and compare model fits. We will present a 10-step practical guide on how to succeed in modeling. 

This lecture summarizes the concepts introduced in Model Fitting I and adds two additional concepts: 1) MLE is a frequentist way of looking at the data and the model, with its own limitations. 2) Side-by-side comparisons of bootstrapping and cross-validation.

Question and Answer Session for Asian and Australian participants.

Question and Answer Session for participants in the Americas.

Question and Answer Session for participants in Africa and Europe.

This lecture provides an overview of the generalized linear models (GLM) course, originally a part of the Neuromatch Academy (NMA), an interactive online summer school held in 2020. NMA provided participants with experiences spanning from hands-on modeling experience to meta-science interpretation skills across just about everything that could reasonably be included in the label "computational neuroscience". 

This lecture further develops the concepts introduced in Machine Learning I. This lecture is part of the Neuromatch Academy (NMA), an interactive online computational neuroscience summer school held in 2020.

Question and Answer Session for participants in the Americas.

Question and Answer Session for participants in Asia and Australia.

Question and Answer Session for participants in Africa and Europe.

This lesson provides an overview of The Virtual Brain integrated workflows on EBRAINS.

This lesson walks users through the Image Processing Pipeline, an integral part of the TVB on EBRAINS integrated workflows.

This lesson gives an overview of The Virtual Brain simulator and its integration into the Human Brain Project Cloud and EBRAINS infrastructure.

In this lesson, users will get an overview of the EBRAINS integrated Fast TVB, a C implementation of TVB that is orders of magnitude faster than the original Python TVB, and capable of performing parallelizable simulations in the cloud.

In this lesson you will learn about the Bayesian Virtual Epileptic Patient (BVEP), a research use case using TVB supported on the EBRAINS infrastructure.

This lesson provides an overview of the process of developing the TVB-NEST co-simulation on the EBRAINS infrastructure, and its use cases.

This lesson gives a brief overview of the multi-scale co-simulation between TVB-NEST and Elephant on the EBRAINS infrastructure.

In this lesson, you will learn about the process of constructing models for TVB automatically on the EBRAINS infrastructure.