POSTER PRESENTATION Open Access SpineML and Brian 2.0 interfaces for using GPU enhanced Neuronal Networks (GeNN) Thomas Nowotny1*, Alexander J Cope2, Esin Yavuz1, Marcel Stimberg3, Dan FM Goodman4, James Marshall2, Kevin Gurney5 From The Twenty Third Annual Computational Neuroscience Meeting: CNS*2014 Québec City, Canada. 26-31 July 2014 Background The GPU enhanced Neuronal Networks (GeNN) frame- work [1,2] was introduced in 2011 to facilitate the efficient use of graphical processing units (GPUs) as accelerators for neuronal network simulations, in particular as part of computational neuroscience investigations. GeNN is based substantially on code generation for the NVIDIA CUDA application programming interface. Code generation pro- vides decisive advantages over stand-alone simulators in that (i) code can be optimized both for every individual model and for the specific GPU hardware detected at compile time, and (ii) code generation allows to provide a practically limitless number of pre-defined models while the generated simulation code remains as small and effi- cient as possible. While GeNN is an important step towards facilitating the use of GPU acceleration for computational neu- roscience applications it has been designed with expert users in mind. Particular emphasis has been put on flex- ibility and extendibility and, in case of conflict, these were prioritized over the easy of use. In the work presented here we are aiming to make GeNN and the corresponding GPU acceleration now also available to non-expert users by providing two new inter- faces from SpineCreator [3]/SpineML [4] and the Brian 2 simulator [5,6] to GeNN. Results We have created prototype interfaces from SpineCreator using the SpineML markup language and from Brian 2 by modifying the code generation facilities within Brian 2 to generate valid GeNN input files. In both cases, a middleware was created that takes the model descrip- tions that were either available in SpineML (as gener- ated by SpineCreator) or the internal representation in Brian 2 and translate them into the three main code parts necessary to run a GeNN simulation: (i) Neuron, synapse and network definitions including variables, parameters, code to integrate dynamical equations and properties of the connectivity, (ii) Code that runs the GeNN generated code to execute the simulation and (iii) code that regulates the transfer of information from SpineCreator/Brian 2 to GeNN and vice versa (e.g. for providing connectivity matrices and returning simula- tion results). Conclusions We believe that when the new SpineCreator and Brian 2 interfaces will have been fully developed, tested and released we can make a decisive difference in the uptake of GPU acceleration for neuronal network simulations. The completed prototypes show sufficient flexibility and ease of use to be appealing to a wide range of future users. Acknowledgements This work was supported by the EPSRC (Green Brain Project, grant numbers EP/J019690/1, EP/J019534/1 and Delivery Plan ‘Kickstart’ funding). Authors’ details 1CCNR, School of Engineering and Informatics, University of Sussex, Falmer, Brighton BN1 9QJ, UK. 2Department of Computer Science, University of Sheffield, Sheffield S1 4DP, UK. 3Institut d’Etudes de la Cognition, Ecole Normale Supérieure, Paris, France. 4Harvard Medical School, Harvard University, Boston, MA 02115, USA. 5Department of Psychology, University of Sheffield, Sheffield S10 2TP, UK. Published: 21 July 2014 References 1. Nowotny T: Flexible neuronal network simulation framework using code generation for NVIDIA CUDA. BMC Neurosci 2011, 12(Suppl 1):P239. * Correspondence: t.nowotny@sussex.ac.uk 1CCNR, School of Engineering and Informatics, University of Sussex, Falmer, Brighton BN1 9QJ, UK Full list of author information is available at the end of the article Nowotny et al. BMC Neuroscience 2014, 15(Suppl 1):P148 http://www.biomedcentral.com/1471-2202/15/S1/P148 © 2014 Nowotny et al; licensee BioMed Central