Paper Info
Title
Efficient generation of connectivity in neuronal networks from simulator-independent descriptions.
Abstract
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.
Year
DOI
Venue
2014
10.3389/fninf.2014.00043
FRONTIERS IN NEUROINFORMATICS
Keywords
Field
DocType
model description,connectivity,neural simulation,CSA,NEST,PyNN,Python,large-scale modeling
Computer architecture simulator,Simulation,Computer science,Implementation,Artificial intelligence,Model sharing,Python (programming language),Machine learning,Information and Computer Science
Journal
Volume
ISSN
Citations 
8
1662-5196
3
PageRank 
References 
Authors
0.38
6
3
Name
Order
Citations
PageRank
Mikael Djurfeldt129822.07
Andrew P. Davison267454.28
Jochen M Eppler325013.52