This lecture covers different perspectives on the study of the mental, focusing on the difference between Mind and Brain.
This lecture covers a lot of post-war developments in the science of the mind, focusing first on the cognitive revolution, and concluding with living machines.
In this lesson, you will learn about the current challenges facing the integration of machine learning and neuroscience.
This lecture provides an overview of depression (epidemiology and course of the disorder), clinical presentation, somatic co-morbidity, and treatment options.
This lesson gives an introduction to simple spiking neuron models.
This lesson provides an introduction to simple spiking neuron models.
This presentation accompanies the paper entitled: An automated pipeline for constructing personalized virtual brains from multimodal neuroimaging data (see link below to download publication).
This lesson provides an introduction to the lifecycle of EEG/ERP data, describing the various phases through which these data pass, from collection to publication.
In this lesson you will learn about experimental design for EEG acquisition, as well as the first phases of the EEG/ERP data lifecycle.
This lesson provides an overview of the current regulatory measures in place regarding experimental data security and privacy.
In this lesson, you will learn the appropriate methods for collection of both data and associated metadata during EEG experiments.
This lesson goes over methods for managing EEG/ERP data after it has been collected, from annotation to publication.
In this final lesson of the course, you will learn broadly about EEG signal processing, as well as specific applications which make this kind of brain signal valuable to researchers and clinicians.
This lesson provides a brief overview of the Python programming language, with an emphasis on tools relevant to data scientists.
The lecture provides an overview of the core skills and practical solutions required to practice reproducible research.
This lecture on model types introduces the advantages of modeling, provide examples of different model types, and explain what modeling is all about.
This lecture focuses on how to get from a scientific question to a model using concrete examples. We will present a 10-step practical guide on how to succeed in modeling. This lecture contains links to 2 tutorials, lecture/tutorial slides, suggested reading list, and 3 recorded Q&A sessions.
This lecture formalizes modeling as a decision process that is constrained by a precise problem statement and specific model goals. We provide real-life examples on how model building is usually less linear than presented in Modeling Practice I.
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.