GeNN is a framework of semi-automated code generation for simulating neuronal networks on GPU hardware. Using code generation to build a specific simulator engine for each individual network model has important advantages: (i) The simulator system can provide a large choice of different neuron and synapse models for use in simulations without creating any overheads or performance losses in the actual simulation code. It also allows the inclusion of user-defined models without the necessity for a user to understand the GPU code. (ii) The generated simulator software can be optimized for the available GPU hardware and for the structure of the specific model. (iii) The framework is intrinsically extensible: New GPU optimization strategies can be added and strategies of existing simulators can be included in the generated code for situations where they are effective.

At the present, GeNN supports a number of standard neuron models including HH, IF, Izhekevich, Rulkov maps and instantaneous rise/exponential decay synapses. The GeNN system comes as a C/C++ source library with specific build tools for the pre-compilation stages.

GeNN is designed for computational neuroscience researchers with some C/C++ experience who would like to use GPU computing in their work without the need to learn the intricacies of highly parallel GPU computing using the general CUDA API.