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. 

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 lecture gives an overview of how to prepare and preprocess neuroimaging (EEG/MEG) data for use in TVB.  

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.

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

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. 

 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. 

Learn how to create a standard extracellular electrophysiology dataset in NWB using Python.

Learn how to create a standard calcium imaging dataset in NWB using Python.

In this tutorial, you will learn how to create a standard intracellular electrophysiology dataset in NWB using Python.

In this tutorial, you will learn how to use the icephys-metadata extension to enter meta-data detailing your experimental paradigm.

This lesson provides instructions on how to build and share extensions in NWB.

Learn how to build custom APIs for extension.

This lesson provides instruction on advanced writing strategies in HDF5 that are accessible through PyNWB.

In this tutorial, users learn how to create a standard extracellular electrophysiology dataset in NWB using MATLAB.

Learn how to create a standard calcium imaging dataset in NWB using MATLAB.

Learn how to create a standard intracellular electrophysiology dataset in NWB.

This lesson provides a tutorial on how to handle writing very large data in MatNWB. 

This lesson gives an overview of the Brainstorm package for analyzing extracellular electrophysiology, including preprocessing, spike sorting, trial alignment, and spectrotemporal decomposition.