This course provides introductory and refresher lessons for a range of concepts and methods useful in the field of neuroscience and neuroinformatics.
Conceptual Background & Refreshers
This lecture covers an Introduction to neuron anatomy and signaling, and different types of models, including the Hodgkin-Huxley model.
This lecture describes non-spiking simple neuron models used in artificial neural networks and machine learning.
This lesson covers the ionic basis of the action potential, including the Hodgkin-Huxley model.
This lecture on model types introduces the advantages of modeling, provide examples of different model types, and explain what modeling is all about.
This lecture summarizes the concepts introduced in Model Types I and further explains how models can be used answer different scientific questions.
This lecture covers acquisition techniques, the physics of magnetic resonance imaging (MRI), diffusion imaging, and prediction using functional magnetic resonance imaging (fMRI).
Serving as good refresher, this lesson explains the maths and logic concepts that are important for programmers to understand, including sets, propositional logic, conditional statements, and more.
This compilation is courtesy of freeCodeCamp.