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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.

Difficulty level: Intermediate
Duration: 5:17
Speaker: : Mike X. Cohen

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

Difficulty level: Intermediate
Duration: 11:37
Speaker: : Mike X. Cohen

In this lesson, users will learn about human brain signals as measured by electroencephalography (EEG), as well as associated neural signatures such as steady state visually evoked potentials (SSVEPs) and alpha oscillations. 

Difficulty level: Intermediate
Duration: 8:51
Speaker: : Mike X. Cohen

This is a tutorial introducing participants to the basics of RNA-sequencing data and how to analyze its features using Seurat. 

Difficulty level: Intermediate
Duration: 1:19:17
Speaker: : Sonny Chen

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. 

Difficulty level: Intermediate
Duration: 1:47:22

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. 

Difficulty level: Intermediate
Duration: 1:28:14

In this lecture, you will learn about current methods, approaches, and challenges to studying human neuroanatomy, particularly through the lense of neuroimaging data such as fMRI and diffusion tensor imaging (DTI). 

Difficulty level: Intermediate
Duration: 1:35:14
Speaker: : Matt Glasser

This lesson continues from part one of the lecture Ontologies, Databases, and Standards, diving deeper into a description of ontologies and knowledg graphs. 

Difficulty level: Intermediate
Duration: 50:18
Speaker: : Jeff Grethe

In this final lecture of the INCF Short Course: Introduction to Neuroinformatics, you will hear about new advances in the application of machine learning methods to clinical neuroscience data. In particular, this talk discusses the performance of SynthSeg, an image segmentation tool for automated analysis of highly heterogeneous brain MRI clinical scans.

Difficulty level: Intermediate
Duration: 1:32:01

This lesson contains practical exercises which accompanies the first few lessons of the Neuroscience for Machine Learners (Neuro4ML) course. 

Difficulty level: Intermediate
Duration: 5:58
Speaker: : Dan Goodman

This lesson introduces some practical exercises which accompany the Synapses and Networks portion of this Neuroscience for Machine Learners course. 

Difficulty level: Intermediate
Duration: 3:51
Speaker: : Dan Goodman

This lesson introduces the practical exercises which accompany the previous lessons on animal and human connectomes in the brain and nervous system. 

Difficulty level: Intermediate
Duration: 4:10
Speaker: : Dan Goodman

This lesson characterizes different types of learning in a neuroscientific and cellular context, and various models employed by researchers to investigate the mechanisms involved. 

Difficulty level: Intermediate
Duration: 3:54
Speaker: : Dan Goodman

In this lesson, you will learn about different approaches to modeling learning in neural networks, particularly focusing on system parameters such as firing rates and synaptic weights impact a network. 

Difficulty level: Intermediate
Duration: 9:40
Speaker: : Dan Goodman

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. 

Difficulty level: Intermediate
Duration: 12:50
Speaker: : Dan Goodman

 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. 

Difficulty level: Intermediate
Duration: 5:14
Speaker: : Dan Goodman

In this lesson, you will learn how to train spiking neural networks (SNNs) with a surrogate gradient method. 

Difficulty level: Intermediate
Duration: 11:23
Speaker: : Dan Goodman

This lesson explores how researchers try to understand neural networks, particularly in the case of observing neural activity. 

Difficulty level: Intermediate
Duration: 8:20
Speaker: : Marcus Ghosh

This video briefly goes over the exercises accompanying Week 6 of the Neuroscience for Machine Learners (Neuro4ML) course, Understanding Neural Networks.

Difficulty level: Intermediate
Duration: 2:43
Speaker: : Marcus Ghosh

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?

Difficulty level: Intermediate
Duration: 6:54
Speaker: : Marcus Ghosh