Efficient generation of connectivity in neuronal networks from simulator-independent descriptions
- 1PDC Center for High-Performance Computing, KTH Royal Institute of Technology, Stockholm, Sweden
- 2International Neuroinformatics Coordinating Facility, Stockholm, Sweden
- 3CNRS, Unité de Neurosciences, Information et Complexité, Gif sur Yvette, France
- 4Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6), Jülich Research Centre and JARA, Jülich, Germany
Simulator-independent descriptions of connectivity in neuronal networks promise greater ease of model sharing, improved reproducibility of simulation results, and reduced programming effort for computational neuroscientists. However, until now, enabling the use of such descriptions in a given simulator in a computationally efficient way has entailed considerable work for simulator developers, which must be repeated for each new connectivity-generating library that is developed. We have developed a generic connection generator interface that provides a standard way to connect a connectivity-generating library to a simulator, such that one library can easily be replaced by another, according to the modeler's needs. We have used the connection generator interface to connect C++ and Python implementations of the previously described connection-set algebra to the NEST simulator. We also demonstrate how the simulator-independent modeling framework PyNN can transparently take advantage of this, passing a connection description through to the simulator layer for rapid processing in C++ where a simulator supports the connection generator interface and falling-back to slower iteration in Python otherwise. A set of benchmarks demonstrates the good performance of the interface.
Keywords: model description, connectivity, neural simulation, CSA, NEST, PyNN, Python, large-scale modeling
Citation: Djurfeldt M, Davison AP and Eppler JM (2014) Efficient generation of connectivity in neuronal networks from simulator-independent descriptions. Front. Neuroinform. 8:43. doi: 10.3389/fninf.2014.00043
Received: 01 November 2013; Accepted: 28 March 2014;
Published online: 22 April 2014.
Edited by:Eilif B. Muller, EPFL, Switzerland
Reviewed by:James Kozloski, Thomas J. Watson Research Center, USA
Thomas Wennekers, University of Plymouth, UK
Copyright © 2014 Djurfeldt, Davison and Eppler. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Mikael Djurfeldt, International Neuroinformatics Coordinating Facility, Nobels väg 15 A, Stockholm, SE-17177, Sweden e-mail: email@example.com