This lesson consists of a demonstration of the BRIAN Simulator. BRIAN is a free, open-source simulator for spiking neural networks. It is written in the Python programming language and is available on almost all platforms, and is designed to be easy to learn and use, highly flexible, and easily extensible.

This lesson provides a demonstration of NeuroFedora, a volunteer-driven initiative to provide a ready-to-use Fedora-based free and open-source software platform for neuroscience. By making the tools used in the scientific process easier to use, NeuroFedora aims to aid reproducibility, data sharing, and collaboration in the research community.The CompNeuro Fedora Lab was specially to enable computational neuroscience.

This lesson provides an introduction and live demonstration of neurolib, a computational framework for simulating coupled neural mass models written in Python. Neurolib provides a simulation and optimization framework which allows you to easily implement your own neural mass model, simulate fMRI BOLD activity, analyse the results and fit your model to empirical data.

In this lesson, you will learn about the GeNN (GPU-enhanced Neuronal Networks) framework, which aims to facilitate the use of graphics accelerators for computational models of large-scale neuronal networks. GeNN is an open-source library that generates code to accelerate the execution of network simulations on NVIDIA GPUs, through a flexible and extensible interface, which does not require in-depth technical knowledge from the users.

This video gives a short introduction to the EBRAINS data sharing platform, why it was developed, and how it contributes to open data sharing.

This video demonstrates how to find, access, and download data on EBRAINS.

This lesson gives a tour of how popular virtualization tools like Docker and Singularity are playing a crucial role in improving reproducibility and enabling high-performance computing in neuroscience.

This lesson gives a demonstration of how to use SciUnit, a Pythonic framework for data-driven unit testing that separates the interface from the implementation, respecting the diversity of conventions for modeling and data collection.

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 provides an introduction to the study of eye-tracking in humans.