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 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.