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 covers the description and characterization of an input-output relationship in a information-theoretic context. 

This lesson is part 1 of 2 of a tutorial on statistical models for neural data.

This lesson is part 2 of 2 of a tutorial on statistical models for neural data.

From the retina to the superior colliculus, the lateral geniculate nucleus into primary visual cortex and beyond, this lecture gives a tour of the mammalian visual system highlighting the Nobel-prize winning discoveries of Hubel & Wiesel.

From Universal Turing Machines to McCulloch-Pitts and Hopfield associative memory networks, this lecture explains what is meant by computation.

In this lesson you will learn about ion channels and the movement of ions across the cell membrane, one of the key mechanisms underlying neuronal communication.