This lecture covers different perspectives on the study of the mental, focusing on the difference between Mind and Brain.
This lesson provides a brief overview of the Python programming language, with an emphasis on tools relevant to data scientists.
This hands-on tutorial walks you through DataJoint platform, highlighting features and schema which can be used to build robost neuroscientific pipelines.
This lesson gives an introductory presentation on how data science can help with scientific reproducibility.
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.
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 lecture provides an introduction to Plato’s concept of rationality and Aristotle’s concept of empiricism, and the enduring discussion between rationalism and empiricism to this day.
This lecture goes into further detail about the hard problem of developing a scientific discipline for subjective consciousness.
This lesson gives a brief introduction to the course Neuroscience for Machine Learners (Neuro4ML).
This lesson covers the history of neuroscience and machine learning, and the story of how these two seemingly disparate fields are increasingly merging.
In this lesson, you will learn about the current challenges facing the integration of machine learning and neuroscience.
In this lesson, you will learn about how genetics can contribute to our understanding of psychiatric phenotypes.
The lecture provides an overview of the core skills and practical solutions required to practice reproducible research.
This lecture covers the description and brief history of data science and its use in neuroinformatics.
This lesson provides an overview of self-supervision as it relates to neural data tasks and the Mine Your Own vieW (MYOW) approach.
An introduction to data management, manipulation, visualization, and analysis for neuroscience. Students will learn scientific programming in Python, and use this to work with example data from areas such as cognitive-behavioral research, single-cell recording, EEG, and structural and functional MRI. Basic signal processing techniques including filtering are covered. The course includes a Jupyter Notebook and video tutorials.
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 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.