This talk highlights a set of platform technologies, software, and data collections that close and shorten the feedback cycle in research.
This lecture covers modeling the neuron in silicon, modeling vision and audition and sensory fusion using a deep network.
Presentation 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 covers structured data, databases, federating neuroscience-relevant databases, ontologies.