This lesson briefly goes over the outline of the Neuroscience for Machine Learners course.
This tutorial covers the fundamentals of collaborating with Git and GitHub.
This is a continuation of the talk on the cellular mechanisms of neuronal communication, this time at the level of brain microcircuits and associated global signals like those measureable by electroencephalography (EEG). This lecture also discusses EEG biomarkers in mental health disorders, and how those cortical signatures may be simulated digitally.
This lesson describes the principles underlying functional magnetic resonance imaging (fMRI), diffusion-weighted imaging (DWI), tractography, and parcellation. These tools and concepts are explained in a broader context of neural connectivity and mental health.
This lesson breaks down the principles of Bayesian inference and how it relates to cognitive processes and functions like learning and perception. It is then explained how cognitive models can be built using Bayesian statistics in order to investigate how our brains interface with their environment.
This lesson corresponds to slides 1-64 in the PDF below.
This lesson delves into the human nervous system and the immense cellular, connectomic, and functional sophistication therein.
In this lesson, you will learn about one particular aspect of decision making: reaction times. In other words, how long does it take to take a decision based on a stream of information arriving continuously over time?