Fancy running your SNNs 10x faster? Our GPU enhanced Neuronal Networks (GeNN) library is freely available from https://genn-team.github.io/ and provides an environment for GPU accelerated spiking neural network simulations. GeNN is capable of simulating large spiking neural network (SNN) models at competitive speeds, even on single, commodity GPUs. In GeNN, SNN models are described using a simple model description API through which variables, parameters and C-like code snippets that describe various aspects of the model elements can be specified, e.g. neuron and synapse update equations or learning dynamics. Model elements of neuron and synapse types are combined into neuron and synapse populations to form a full spiking neural network model. GeNN takes the model description and generates optimised code to simulate the model. Current code-generation backends include CUDA for NVIDIA GPUs and OpenCL for other accelerators as well as a C++ CPU-only mode.

In recent years the GeNN ecosystem has expanded rapidly with a Python wrapper, OpenCL backend and, most recently, mlGeNN (https://github.com/genn-team/ml_genn/), a library for converting ANNs trained in Keras to SNNs as well as training SNNs from scratch using e-prop and EventProp.

This year at CapoCaccia, I'm going to run sessions to introduce people to GeNN, help them with installation problems and then walk them through our selection of tutorials that should allow them to get a flavour of the capabilities and user experience of GeNN; and enable them to start using it for their own work. CapoCaccia WiFi permitting, all our tutorials are now available as Google Collab notebooks at https://genn-team.github.io/tutorials.html so no installation is required!