This lecture provides an introduction to optogenetics, a biological technique to control the activity of neurons or other cell types with light.

This lecture describes how to build research workflows, including a demonstrate using DataJoint Elements to build data pipelines.

This lecture discusses how FAIR practices affect personalized data models, including workflows, challenges, and how to improve these practices.

This lecture covers how to make modeling workflows FAIR by working through a practical example, dissecting the steps within the workflow, and detailing the tools and resources used at each step.

The state of the field regarding the diagnosis and treatment of major depressive disorder (MDD) is discussed. Current challenges and opportunities facing the research and clinical communities are outlined, including appropriate quantitative and qualitative analyses of the heterogeneity of biological, social, and psychiatric factors which may contribute to MDD.

This lesson delves into the opportunities and challenges of telepsychiatry. While novel digital approaches to clinical research and care have the potential to improve and accelerate patient outcomes, researchers and care providers must consider new population factors, such as digital disparity. 

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. 

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 module explains how neurons come together to create the networks that give rise to our thoughts. The totality of our neurons and their connection is called our connectome. Learn how this connectome changes as we learn, and computes information.

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 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 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.