Our paper on brian2genn was published today. It automatically translates Brian models into a format that can be read by the GPU-enhanced neural network simulator (GeNN) to allow it to run on GPU. Some simulations were 400x faster on GPU compared to a single CPU.
We are happy to report that our paper “Brian 2, an intuitive and efficient neural simulator” has been accepted for publication in eLife: https://elifesciences.org/articles/47314
In this paper, we describe a number of examples that showcase various features of Brian 2, ranging from non-standard neuronal/synaptic models to interacting with hardware in real time via C++ code. All the code to run these examples is openly available in a github repository.
The examples are provided as interactive jupyter notebooks, and can be tried out without a local installation of Brian by running them on the binder infrastructure.