This lecture gives an introduction to the European Academy of Neurology, its recent achievements and ambitions.

This lecture gives an overview on the European Health Dataspace. 

The International Brain Initiative (IBI) is a consortium of the world’s major large-scale brain initiatives and other organizations with a vested interest in catalyzing and advancing neuroscience research through international collaboration and knowledge sharing. This workshop introduces the IBI, the efforts of the Data Standards and Sharing Working Group,  and keynote lectures on the impact of data standards and sharing on large-scale brain projects, as well as a discussion on prospects and needs for neural data sharing.

Panel of experts discuss the virtues and risks of our digital health data being captured and used by others in the age of Facebook, metadata retention laws, Cambridge Analytica and a rapidly evolving neuroscience. The discussion was moderated by Jon Faine, ABC Radio presenter. The panelists were:

• Mr Sven Bluemmel, Victorian Information Commissioner
• Prof Judy Illes, Neuroethics Canada, University of British Columbia, Order of Canada
• Prof Mark Andrejevic, Professor of Media Studies, Monash University
• Ms Vrinda Edan, Chief Operating Officer, Victorian Mental Illness Awareness Council

This lecture covers how you can make your data public through EBRAINS. This talk focuses on the ethical considerations for sharing data, the requirements that are imposed by various regulations, particularly for sharing human data. The lecture also includes a discussion of how EBRAINS designs its services to deal with the ethical and regulatory aspects of sharing these kinds of data.

This lecture discusses the challenges of protecting hospital data.

This lecture discusses differential privacy and synthetic data in the context of medical data sharing in clinical neurosciences.

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

In this lesson, you will learn about some typical neuronal models employed by machine learners and computational neuroscientists, meant to imitate the biophysical properties of real neurons. 

Whereas the previous two lessons described the biophysical and signalling properties of individual neurons, this lesson describes properties of those units when part of larger networks. 

This lesson goes over some examples of how machine learners and computational neuroscientists go about designing and building neural network models inspired by biological brain systems. 

In this lesson, you will learn about different approaches to modeling learning in neural networks, particularly focusing on system parameters such as firing rates and synaptic weights impact a network. 

In this lesson, you will learn more about some of the issues inherent in modeling neural spikes, approaches to ameliorate these problems, and the pros and cons of these approaches. 

 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. 

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

This lesson explores how researchers try to understand neural networks, particularly in the case of observing neural activity. 

In this lesson you will learn about the motivation behind manipulating neural activity, and what forms that may take in various experimental designs. 

This video briefly goes over the exercises accompanying Week 6 of the Neuroscience for Machine Learners (Neuro4ML) course, Understanding Neural Networks.