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

Presentation of a simulation software for spatial model neurons and their networks designed primarily for GPUs.

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.

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.

Introduction to the Mathematics chapter of Datalabcc's "Foundations in Data Science" series.

Primer on elementary algebra

Primer on linear algebra

Primer on systems of linear equations

Primer on calculus

How calculus relates to optimization

Big O notation

Basics of probability.

This lecture covers describing and characterizing an input-output relationship.

Part 1 of 2 of a tutorial on statistical models for neural data

Part 2 of 2 of a tutorial on statistical models for neural data.

Introduction to stability analysis of neural models

Introduction to stability analysis of neural models

Oscillations and bursting

Oscillations and bursting