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.
Ion channels and the movement of ions across the cell membrane.
Action potentials, and biophysics of voltage-gated ion channels.
Voltage-gating kinetics of sodium and potassium channels.
The ionic basis of the action potential, including the Hodgkin Huxley model.
Action potential initiation and propagation.
Long-range inhibitory connections in the brain, with examples from three different systems.
How does the brain learn? This lecture discusses the roles of development and adult plasticity in shaping functional connectivity.