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Related Experiment Video

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Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

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Published on: March 2, 2015

Brian2GeNN: accelerating spiking neural network simulations with graphics hardware.

Marcel Stimberg¹, Dan F M Goodman², Thomas Nowotny³

¹Sorbonne Université, INSERM, CNRS, Institut de la Vision, Paris, France.

Scientific Reports

|January 17, 2020

Summary

This summary is machine-generated.

Brian2GeNN enables computational neuroscience researchers to accelerate spiking neural network simulations on GPUs. This new tool integrates Brian and GeNN, offering significant speedups without requiring users to know C++ or GPU programming.

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Area of Science:

Computational Neuroscience
Scientific Computing
Artificial Intelligence

Background:

Spiking neural networks (SNNs) are crucial for understanding brain function and developing AI.
Brian is a widely-used Python simulator for SNNs, but can be limited by CPU performance.
GeNN is a C++ meta-compiler that accelerates SNN simulations using Graphics Processing Units (GPUs).

Purpose of the Study:

To introduce Brian2GeNN, a software package connecting Brian and GeNN for GPU acceleration.
To enable Brian users to leverage GeNN's GPU capabilities without C++ or GPU expertise.
To significantly enhance the simulation speed of complex SNN models.

Main Methods:

Developed a code generation pipeline to translate Brian scripts into GeNN-compatible C++ code.
Integrated this pipeline into the Brian simulator.
Tested Brian2GeNN with two non-trivial SNN models from existing literature.

Main Results:

Brian2GeNN successfully translates Brian models for execution on NVIDIA GPUs via GeNN.
Simulations using Brian2GeNN achieved speedups of tens to hundreds of times compared to CPU execution.
The integration requires minimal user intervention (adding two lines to Brian scripts).

Conclusions:

Brian2GeNN provides a user-friendly solution for accelerating SNN simulations.
It democratizes GPU acceleration for computational neuroscience research within the Brian ecosystem.
This significantly advances the feasibility of simulating larger and more complex neural models.