This lesson gives an overview of modeling neurons based on firing rate.
This lesson characterizes the pattern generation observed in visual system hallucinations.
This lesson gives an introduction to stability analysis of neural models.
This lesson continues from the previous lectures, providing introduction to stability analysis of neural models.
In this lesson, you will learn about phenomena of neural populations such as synchrony, oscillations, and bursting.
This lesson continues from the previous lecture, giving an overview of various neural phenomena such as oscillations and bursting.
This lesson provides more context around weakly coupled oscillators.
This lesson builds upon previous lectures in this series, providing an overview of coupled oscillators.
In this lesson, you will learn about neuronal models based on their spike rate.
In this lesson, you will learn about neural activity pattern generation in visual system hallucinations.
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.
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 is a tutorial on designing a Bayesian inference model to map belief trajectories, with emphasis on gaining familiarity with Hierarchical Gaussian Filters (HGFs).
This lesson corresponds to slides 65-90 of the PDF below.
This lecture covers a lot of post-war developments in the science of the mind, focusing first on the cognitive revolution, and concluding with living machines.
This lecture provides an overview of depression (epidemiology and course of the disorder), clinical presentation, somatic co-morbidity, and treatment options.
What is the difference between attention and consciousness? This lecture describes the scientific meaning of consciousness, journeys on the search for neural correlates of visual consciousness, and explores the possibility of consciousness in other beings and even non-biological structures.
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 tutorial introduces pipelines and methods to compute brain connectomes from fMRI data. With corresponding code and repositories, participants can follow along and learn how to programmatically preprocess, curate, and analyze functional and structural brain data to produce connectivity matrices.