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INCF/OCNS Working Group on Computational Neuroscience Software

By
Level
Beginner

This working group is a collaboration between OCNS and INCF. The group focuses on evaluating and testing computational neuroscience tools; finding them, testing them, learning how they work, and informing developers of issues to ensure that these tools remain in good shape by having communities looking after them. Since many members of the WG are themselves tool developers, we will also learn from each other and will work towards improving interoperability between related tools.

The working group has hosted a variety of recorded development sessions, each with the aim of demonstrating a different tool. More lessons will be added as the working group hosts more sessions.

The INCF/OCNS Working Group on Computational Neuroscience Software is active and soliciting new members.

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Course Features
Videos
Lectures
Tutorials
Lessons of this Course
1
1
Duration:
1:27:32

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.

2
2
Duration:
1:06:08
Speaker:

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.

3
3
Duration:
1:06:53

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.

4
4
Duration:
59:00

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.

5
5
Duration:
1:23:00

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.

6
6
Duration:
58:00

In this lesson, users will learn about the importance of proper citation of software resources and tools used in neuroscientific research.