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This lesson is a general overview of overarching concepts in neuroinformatics research, with a particular focus on clinical approaches to defining, measuring, studying, diagnosing, and treating various brain disorders. Also described are the complex, multi-level nature of brain disorders and the data associated with them, from genes and individual cells up to cortical microcircuits and whole-brain network dynamics. Given the heterogeneity of brain disorders and their underlying mechanisms, this lesson lays out a case for multiscale neuroscience data integration.

Difficulty level: Intermediate
Duration: 1:09:33
Speaker: : Sean Hill

This is the first of two workshops on reproducibility in science, during which participants are introduced to concepts of FAIR and open science. After discussing the definition of and need for FAIR science, participants are walked through tutorials on installing and using Github and Docker, the powerful, open-source tools for versioning and publishing code and software, respectively.

Difficulty level: Intermediate
Duration: 1:20:58

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. 

Difficulty level: Beginner
Duration: 1:22:06
Speaker: : Daniel Buchman

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. 

Difficulty level: Beginner
Duration: 1:06:35
Speaker: : Laura Sikstrom

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. 

Difficulty level: Beginner
Duration: 52:26

This is a continuation of the talk on the cellular mechanisms of neuronal communication, this time at the level of brain microcircuits and associated global signals like those measureable by electroencephalography (EEG). This lecture also discusses EEG biomarkers in mental health disorders, and how those cortical signatures may be simulated digitally.

Difficulty level: Intermediate
Duration: 1:11:04
Speaker: : Etay Hay

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.

Difficulty level: Beginner
Duration: 2:24:35

Introduction of the Foundations of Machine Learning in Python course - Day 01.

High-Performance Computing and Analytics Lab, University of Bonn

Difficulty level: Beginner
Duration: 35:24
Speaker: : Elena Trunz

This lesson delves into the the structure of one of the brain's most elemental computational units, the neuron, and how said structure influences computational neural network models. 

Difficulty level: Intermediate
Duration: 6:33
Speaker: : Marcus Ghosh

In this lesson you will learn how machine learners and neuroscientists construct abstract computational models based on various neurophysiological signalling properties. 

Difficulty level: Intermediate
Duration: 10:52
Speaker: : Dan Goodman

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

Difficulty level: Intermediate
Duration: 7:03
Speaker: : Marcus Ghosh

This lesson delves into the human nervous system and the immense cellular, connectomic, and functional sophistication therein. 

Difficulty level: Intermediate
Duration: 8:41
Speaker: : Marcus Ghosh

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. 

Difficulty level: Intermediate
Duration: 12:50
Speaker: : Dan Goodman

 In this lesson, you will learn about some of the many methods to train spiking neural networks (SNNs) with either no attempt to use gradients, or only use gradients in a limited or constrained way. 

Difficulty level: Intermediate
Duration: 5:14
Speaker: : Dan Goodman

In this lesson, you will learn how to train spiking neural networks (SNNs) with a surrogate gradient method. 

Difficulty level: Intermediate
Duration: 11:23
Speaker: : Dan Goodman

This video gives a brief introduction to Neuro4ML's lessons on neuromorphic computing - the use of specialized hardware which either directly mimics brain function or is inspired by some aspect of the way the brain computes. 

Difficulty level: Intermediate
Duration: 3:56
Speaker: : Dan Goodman

In this lesson, you will learn in more detail about neuromorphic computing, that is, non-standard computational architectures that mimic some aspect of the way the brain works. 

Difficulty level: Intermediate
Duration: 10:08
Speaker: : Dan Goodman

This video provides a very quick introduction to some of the neuromorphic sensing devices, and how they offer unique, low-power applications.

Difficulty level: Intermediate
Duration: 2:37
Speaker: : Dan Goodman

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?

Difficulty level: Intermediate
Duration: 6:54
Speaker: : Marcus Ghosh

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

Difficulty level: Intermediate
Duration: 9:34
Speaker: : Dan Goodman