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 lecture covers different perspectives on the study of the mental, focusing on the difference between Mind and Brain.
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 lesson briefly goes over the outline of the Neuroscience for Machine Learners course.
This lesson delves into the the structure of one of the brain's most elemental computational units, the neuron, and how said structure influences computational neural network models.
In this lesson you will learn how machine learners and neuroscientists construct abstract computational models based on various neurophysiological signalling properties.
This lesson describes spike timing-dependent plasticity (STDP), a biological process that adjusts the strength of connections between neurons in the brain, and how one can implement or mimic this process in a computational model. You will also find links for practical exercises at the bottom of this page.
In this lesson, you will learn about some of the many methods to train spiking neural networks (SNNs) with either no attempt to use gradients, or only use gradients in a limited or constrained way.
In this lesson, you will learn how to train spiking neural networks (SNNs) with a surrogate gradient method.
This lesson explores how researchers try to understand neural networks, particularly in the case of observing neural activity.
In this lesson, you will hear about some of the open issues in the field of neuroscience, as well as a discussion about whether neuroscience works, and how can we know?
This lecture will provide an overview of neuroimaging techniques and their clinical applications.
This lesson discusses both state-of-the-art detection and prevention schema in working with neurodegenerative diseases.
This lesson gives an introduction to simple spiking neuron models.
This lesson provides an introduction to simple spiking neuron models.
This presentation accompanies the paper entitled: An automated pipeline for constructing personalized virtual brains from multimodal neuroimaging data (see link below to download publication).
This lesson provides a brief overview of the Python programming language, with an emphasis on tools relevant to data scientists.
This lecture provides an introduction to the Brain Imaging Data Structure (BIDS), a standard for organizing human neuroimaging datasets.
This tutorial covers the fundamentals of collaborating with Git and GitHub.
This lecture presents an overview of functional brain parcellations, as well as a set of tutorials on bootstrap agregation of stable clusters (BASC) for fMRI brain parcellation.