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

This tutorial covers the fundamentals of collaborating with Git and GitHub.

This lecture provides an introduction to the Brain Imaging Data Structure (BIDS), a standard for organizing human neuroimaging datasets.

This lecture covers the rationale for developing the DAQCORD, a framework for the design, documentation, and reporting of data curation methods in order to advance the scientific rigour, reproducibility, and analysis of data.

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 goes over some examples of how machine learners and computational neuroscientists go about designing and building neural network models inspired by biological brain systems. 

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

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. 

This lesson gives an introduction to the central concepts of machine learning, and how they can be applied in Python using the scikit-learn package. 

This tutorial provides instruction on how to simulate brain tumors with TVB (reproducing publication: Marinazzo et al. 2020 Neuroimage). This tutorial comprises a didactic video, jupyter notebooks, and full data set for the construction of virtual brains from patients and health controls.

The tutorial on modelling strokes in TVB includes a didactic video and jupyter notebooks (reproducing publication: Falcon et al. 2016 eNeuro).

This lesson introduces population models and the phase plane, and is part of the The Virtual Brain (TVB) Node 10 Series, a 4-day workshop dedicated to learning about the full brain simulation platform TVB, as well as brain imaging, brain simulation, personalised brain models, and TVB use cases.

In this tutorial, you will learn how to run a typical TVB simulation. 

This lesson introduces TVB-multi-scale extensions and other TVB tools which facilitate modeling and analyses of multi-scale data. 

This tutorial introduces The Virtual Mouse Brain (TVMB), walking users through the necessary steps for performing simulation operations on animal brain data. 

In this tutorial, you will learn the necessary steps in modeling the brain of one of the most commonly studied animals among non-human primates, the macaque.