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

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.

This lesson introduces concepts and practices surrounding reference atlases for the mouse and rat brains. Additionally, this lesson provides discussion around examples of data systems employed to organize neuroscience data collections in the context of reference atlases as well as analytical workflows applied to the data.

This talk covers EBRAINS, an open research infrastructure that gathers data, tools and computing facilities for brain-related research, built with interoperability at the core.

This lesson provides an introduction to the European open research infrastructure EBRAINS and its digital brain atlas resources.