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. 

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. 

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

This lecture covers modeling the neuron in silicon, modeling vision and audition, and sensory fusion using a deep network. 

This lesson presents a simulation software for spatial model neurons and their networks designed primarily for GPUs.

This lesson gives an overview of past and present neurocomputing approaches and hybrid analog/digital circuits that directly emulate the properties of neurons and synapses.

Presentation of the Brian neural simulator, where models are defined directly by their mathematical equations and code is automatically generated for each specific target.

The lecture covers a brief introduction to neuromorphic engineering, some of the neuromorphic networks that the speaker has developed, and their potential applications, particularly in machine learning.

This lesson provides an overview of the current status in the field of neuroscientific ontologies, presenting examples of data organization and standards, particularly from neuroimaging and electrophysiology. 

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 presents the Medical Informatic Platform's data federation for Traumatic Brain Injury.

This lecture gives insights into the Medical Informatics Platform's current and future data privacy model.

This lecture explains the concept of federated analysis in the context of medical data, associated challenges. The lecture also presents an example of hospital federations via the Medical Informatics Platform.

This talk discusses what are usually considered successful outcomes of scientific research consortia, and how those outcomes can be translated into lasting impacts. 

In this lesson, you will learn about the BRAIN Initiative Cell Atlas Network (BICAN) and how this project adopts a federated approach to data sharing. 

This talks presents an overview of the potential for data federation in stroke research.

This lecture explains the need for data federation in medicine and how it can be achieved.