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

This lesson briefly goes over the outline of the Neuroscience for Machine Learners course. 

This lesson provides a brief overview of the Python programming language, with an emphasis on tools relevant to data scientists.

This tutorial covers the fundamentals of collaborating with Git and GitHub.

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.

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 provides a basic introduction to clinical presentation of schizophrenia, its etiology, and current treatment options.

This lesson goes over the basic mechanisms of neural synapses, the space between neurons where signals may be transmitted. 

This lesson describes spike timing-dependent plasticity (STDP), a biological process that adjusts the strength of connections between neurons in the brain, and how one can implement or mimic this process in a computational model. You will also find links for practical exercises at the bottom of this page. 

This lesson discusses a gripping neuroscientific question: why have neurons developed the discrete action potential, or spike, as a principle method of communication? 

This lecture discusses the FAIR principles as they apply to electrophysiology data and metadata, the building blocks for community tools and standards, platforms and grassroots initiatives, and the challenges therein.

This lecture contains an overview of electrophysiology data reuse within the EBRAINS ecosystem.

This lecture contains an overview of the Distributed Archives for Neurophysiology Data Integration (DANDI) archive, its ties to FAIR and open-source, integrations with other programs, and upcoming features.

This lecture contains an overview of the Australian Electrophysiology Data Analytics Platform (AEDAPT), how it works, how to scale it, and how it fits into the FAIR ecosystem.

This lecture discusses how to standardize electrophysiology data organization to move towards being more FAIR.

This lecture will provide an overview of the INCF Training Suite, a collection of tools that embraces the FAIR principles developed by members of the INCF Community. This will include an overview of TrainingSpace, Neurostars, and KnowledgeSpace.

This lecture contains an overview of the China-Cuba-Canada neuroinformatics ecosystem for Quantitative Tomographic EEG Analysis (qEEGt).

This lecture provides an overview of some of the essential concepts in neuropharmacology (e.g. receptor binding, agonism, antagonism), an introduction to pharmacodynamics and pharmacokinetics, and an overview of the drug discovery process relative to diseases of the central nervous system.