This lecture covers the needs and challenges involved in creating a FAIR ecosystem for neuroimaging research.

This lecture covers multiple aspects of FAIR neuroscience data: what makes it unique, the challenges to making it FAIR, the importance of overcoming these challenges, and how data governance comes into play.

Open Brain Consent is an international initiative aiming to address the challenge of creating participant consent language that will promote the open sharing of data, protect participant privacy, and conform to legal norms and institutional review boards.

Open Brain Consent addresses the aforementioned difficulties in neuroscience research with human participants by collecting:

• widely acceptable consent forms (with various translations) allowing deposition of anonymized data to public data archives
• collection of tools/pipelines to help anonymization of neuroimaging data making it ready for sharing

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. 

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.

This lecture provides an overview of depression (epidemiology and course of the disorder), clinical presentation, somatic co-morbidity, and treatment options.

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. 

This lesson delves into the human nervous system and the immense cellular, connectomic, and functional sophistication therein. 

In this lesson, you will hear about some of the open issues in the field of neuroscience, as well as a discussion about whether neuroscience works, and how can we know?

This lecture covers the history of behaviorism and the ultimate challenge to behaviorism. 

This lecture covers various learning theories.

This lesson characterizes different types of learning in a neuroscientific and cellular context, and various models employed by researchers to investigate the mechanisms involved. 

In this lesson, you will learn about different approaches to modeling learning in neural networks, particularly focusing on system parameters such as firing rates and synaptic weights impact a network. 

This lecture provides an introduction to the course "Cognitive Science & Psychology: Mind, Brain, and Behavior".

This lecture covers the emergence of cognitive science after the Second World War as an interdisciplinary field for studying the mind, with influences from anthropology, cybernetics, and artificial intelligence.

This lecture covers different perspectives on the study of the mental, focusing on the difference between Mind and Brain. 

This lecture outlines various approaches to studying Mind, Brain, and Behavior.