This lecture provides an introduction to the study of eye-tracking in humans. 

This lecture provides an introduction to the application of genetic testing in neurodevelopmental disorders.

This lesson gives an in-depth introduction of ethics in the field of artificial intelligence, particularly in the context of its impact on humans and public interest. As the healthcare sector becomes increasingly affected by the implementation of ever stronger AI algorithms, this lecture covers key interests which must be protected going forward, including privacy, consent, human autonomy, inclusiveness, and equity. 

This lesson describes a definitional framework for fairness and health equity in the age of the algorithm. While acknowledging the impressive capability of machine learning to positively affect health equity, this talk outlines potential (and actual) pitfalls which come with such powerful tools, ultimately making the case for collaborative, interdisciplinary, and transparent science as a way to operationalize fairness in health equity. 

This lesson contains both a lecture and a tutorial component. The lecture (0:00-20:03 of YouTube video) discusses both the need for intersectional approaches in healthcare as well as the impact of neglecting intersectionality in patient populations. The lecture is followed by a practical tutorial in both Python and R on how to assess intersectional bias in datasets. Links to relevant code and data are found below. 

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

This lecture covers various learning theories.

This lesson contains the first part of the lecture Data Science and Reproducibility. You will learn about the development of data science and what the term currently encompasses, as well as how neuroscience and data science intersect. 

This lecture covers a wide range of aspects regarding neuroinformatics and data governance, describing both their historical developments and current trajectories. Particular tools, platforms, and standards to make your research more FAIR are also discussed.

In this lesson, you will learn about how genetics can contribute to our understanding of psychiatric phenotypes.

In this lesson, you will hear about the current challenges regarding data management, as well as policies and resources aimed to address them. 

This lecture presents selected theories of ethics as applied to questions raised by the Human Brain Project.

The HBP as an ICT flagship project crucially relies on ICT and will contribute important input into the development of new computing principles and artefacts. Individuals working on the HBP should therefore be aware of the long history of ethical issues discussed in computing. This lessson provides an overview of the most widely discussed ethical issues in computing and demonstrate that privacy and data protection are by no means the only issue worth worrying about. 

This lecture explores two questions regarding the ethics of robot development and use. Firstly, the increasingly urgent question of the ethical use of robots: are there particular applications of robots that should be proscribed, in eldercare, or surveillance, or combat? Secondly, the talk deals with the longer-term question of whether intelligent robots themselves could or should be ethical.

In this lesson, attendees will learn about the challenges involved in working with life scientists to enhance their capacity for understanding, and taking responsibility for, the social implications of their research.

This lecture considers some of the key social and ethical issues raised by the ‘big brain projects’ currently under way in Europe, the USA, China, Japan, and many other regions. 

This lecture aims to give an introduction and overview of the dual-use challenge as it applies to neuroscience today and will apply in coming decades.

What is ethics in biomedical research? In this case, ethics refers to how we think we can use animals in biomedical research and what we gain from the experimental setup of those investigations. We will talk about “a common set of values” and how 3R engagement can make a difference in experimental procedures, results, and scientific publications of the future.

This lecture discusses how artificial intelligence (AI) is presented in policy documents as a disruptive and transformative technology. The talk will further elaborate on how policy-makers frame social risks and opportunities associated with AI in areas such as employment, business, healthcare, education, and military.

This lesson provides a high-level overview of the ethical issues related to data use in such a large, complex, and multi-national research initiative as the HBP.