GeNN (GPU-enhanced Neuronal Networks) framework, which aims to facilitate the use of graphics accelerators for computational models of large-scale neuronal networks to address this challenge. 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.

Peer Herholz 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.

Today’s (neuro)scientific computing landscape depends more than ever on selecting, combining, and implementing a range of tools and technologies for each specific use case. For decades, neuroscience users have turned to MATLAB as an integration environment for pioneering & innovative small-scale studies. Tune in to learn how today’s MATLAB integrates with today’s powerful tools & technologies for larger-scale and next-generation neuroscience challenges.

This workshop will introduce reproducible workflows and a range of tools along the themes of organisation, documentation, analysis, and dissemination. After a brief introduction to the topic of reproducibility, the workshop will provide specific tips and tools useful in improving daily research workflows. The content will include modules such as data management, electronic lab notebooks, reproducible bioinformatics tools and methods, protocol and reagent sharing, data visualisation, and version control. All modules include interactive learning, real-time participation, and active knowledge sharing. The methods and tools introduced help researchers share work with their future self, their immediate colleagues, and the wider scientific community.

This lecture covers describing and characterizing an input-output relationship.

Part 1 of 2 of a tutorial on statistical models for neural data

Part 2 of 2 of a tutorial on statistical models for neural data.

Introduction to stability analysis of neural models

Introduction to stability analysis of neural models

Oscillations and bursting

Oscillations and bursting

Weakly coupled oscillators

Continuation of coupled oscillators

Firing rate models.

Pattern generation in visual system hallucinations.

Introduction to stability analysis of neural models

Introduction to stability analysis of neural models