In this lesson, the simulation of a virtual epileptic patient is presented as an example of advanced brain simulation as a translational approach to deliver improved clinical results. You will learn about the fundamentals of epilepsy, as well as the concepts underlying epilepsy simulation. By using an iPython notebook, the detailed process of this approach is explained step by step. In the end, you are able to perform simple epilepsy simulations your own.
This lesson provides an introduction to the Symposium on Science Management at the Canadian Association for Neuroscience 2019 Meeting.
This lesson gives a primer to project management in a scientific context, with a particular neuroinformatic case study.
In this lesson, you will hear about the current challenges regarding data management, as well as policies and resources aimed to address them.
This lesson covers "Knowledge Translation", the activities involved in moving research from the laboratory, the research journal, and the academic conference into the hands of people and organizations who can put it to practical use.
In this lesson, you will hear about the various methods developed and employed in managing performance.
This lesson provides an overview of how to manage relationships in a research context, while highlighting the need for effective communication at various levels.
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.
Serving as good refresher, this lesson explains the maths and logic concepts that are important for programmers to understand, including sets, propositional logic, conditional statements, and more.
This compilation is courtesy of freeCodeCamp.
This lesson provides a useful refresher which will facilitate the use of Matlab, Octave, and various matrix-manipulation and machine-learning software.
This lesson was created by RootMath.
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.
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 module covers some basic anatomy such as the brain’s major divisions (brainstem, cerebellum, cerebrum), the cerebral lobes (frontal, temporal, parietal, and occipital), the central and peripheral nervous systems, theories of cognition, and brain orientation terms.
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 lesson provides a brief overview of the Python programming language, with an emphasis on tools relevant to data scientists.
This lecture gives an introduction to the FAIR (findability, accessibility, interoperability, and reusability) science principles and examples of their application in neuroscience research.
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.