Combining simulator independent network descriptions with run-time interoperability based on PyNN and MUSIC

Abstract

Event Abstract Back to Event Combining simulator independent network descriptions with run-time interoperability based on PyNN and MUSIC Jochen Martin Eppler1*, Mikael Djurfeldt2, Eilif Muller3, Markus Diesmann1 and Andrew Davison4 1 Institute of Neuroscience and Medicine (INM-6), Computational and Systems Neuroscience, Research Center Jülich, Germany 2 PDC Center for High Performance Computing, School of Computer Science and Communication, KTH, Sweden 3 Blue Brain Project, Ecole Polytechnique Fédérale de Lausanne (EPFL, Switzerland 4 Unité de Neurosciences Intégratives et Computationnelles (UNIC), Centre National de la Recherche Scientifique, France The Multi-Simulation Coordinator (MUSIC) is an INCF funded standard and library implementation that allows neuronal simulators or programs for stimulus generation, data analysis, or visualization to exchange data at run-time. To set up such a multi-simulation consisting of several simulators, the user has to specify the different parts of the network model in the native description or scripting languages of the respective simulators, and provide a configuration file that tells MUSIC what data has to be transported between which applications. This approach works well for connecting multiple already existing models written for different simulators. An example for using this is given in [1]. However, this approach requires the user to be familiar with the configuration languages of all simulators involved in the simulation. To alleviate this problem, we extended the application programming interface (API) of PyNN [2], to enable the simulator-independent specification of multi-simulator neuronal network models. In this contribution, we provide a detailed description of necessary changes to the existing software packages (i.e. NEST [3], NEURON [4] and PyNN) and the user experience for our prototype implementation of this API. The API allows the user to set up the network model using all of PyNN's high-level language constructs and abstracts away from the technical aspects of using the simulator's native MUSIC interfaces and setting up the multi-simulation. Acknowledgments: Partially supported by the Helmholtz Alliance on Systems Biology, the Next-Generation Supercomputer Project of MEXT, and EU Grant 269921 (BrainScaleS).