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.

This tutorial demonstrates how to work with neuronal data using MATLAB, including actional potentials and spike counts, orientation tuing curves in visual cortex, and spatial maps of firing rates.

This lesson instructs users on how to import electrophysiological neural data into MATLAB, as well as how to convert spikes to a data matrix.

In this lesson, users will learn how to appropriately sort and bin neural spikes, allowing for the generation of a common and powerful visualization tool in neuroscience, the histogram. 

Followers of this lesson will learn how to compute, visualize and quantify the tuning curves of individual neurons. 

This lesson demonstrates how to programmatically generate a spatial map of neuronal spike counts using MATLAB.

In this lesson, users are shown how to create a spatial map of neuronal orientation tuning. 

This lecture provides an introduction to Plato’s concept of rationality and Aristotle’s concept of empiricism, and the enduring discussion between rationalism and empiricism to this day.

This lecture goes into further detail about the hard problem of developing a scientific discipline for subjective consciousness.

This opening lecture from INCF's Short Course in Neuroinformatics provides an overview of the field of neuroinformatics itself, as well as laying out an argument for the necessity for developing more sophisticated approaches towards FAIR data management principles in neuroscience. 

This lesson provides a thorough description of neuroimaging development over time, both conceptually and technologically. You will learn about the fundamentals of imaging techniques such as MRI and PET, as well as how the resultant data may be used to generate novel data visualization schemas. 

This lesson contains the first part of the lecture Data Science and Reproducibility. You will learn about the development of data science and what the term currently encompasses, as well as how neuroscience and data science intersect. 

This lecture aims to help researchers, students, and health care professionals understand the place for neuroinformatics in the patient journey using the exemplar of an epilepsy patient. 

This video will document how to run a correlation analysis between the gray matter volume of two different structures using the output from brainlife app-freesurfer-stats.

This lesson gives a brief introduction to the course Neuroscience for Machine Learners (Neuro4ML). 

This lesson briefly goes over the outline of the Neuroscience for Machine Learners course. 

This lesson covers the history of neuroscience and machine learning, and the story of how these two seemingly disparate fields are increasingly merging.