GPU-enhanced neural networks

Fancy running your SNNs 10x faster? Our GPU enhanced Neuronal Networks (GeNN) library is freely available from 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 (, a library for converting ANNs trained in Keras to SNNs.

This year at CapoCaccia, we're 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 so no installation is required!

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Day Time Location
Wed, 04.05.2022 16:30 - 17:00 Lecture room
Mon, 09.05.2022 14:00 - 15:00 Sala Panorama


James Knight
Thomas Nowotny
James Turner


Federico Barban
Felix Bauer
Yeshwanth Bethi
Matteo Cartiglia
Nik Dennler
Norbert Domcsek
Moritz Drangmeister
Jakub Fil
Giacomo Indiveri
Efstathios Kagioulis
James Knight
Laura Kriener
Thomas Nowotny
Michael Schmuker
Simon Thorpe
James Turner
Barbara Webb