Evaluating the Connection-Set Algebra for the neural simulator NEST
Jochen Martin Eppler1, Håkon Enger2,
Thomas Heiberg2, Birgit Kriener2, Hans Ekkehard
Plesser2, Markus Diesmann1,3, Mikael
- Institute of Neuroscience and Medicine (INM-6), Computational and
Systems Neuroscience, Research Center Jülich, Jülich,
- Department of Mathematical Sciences and Technology, Norwegian
University of Life Sciences, Ås, Norway
- Computational Neurophysics, RIKEN Brain Science Institute \&
Computational Science Research Program, Wako City, Japan
- PDC Center for High Performance Computing, School of Computer
Science and Communication, KTH, Stockholm, Sweden
- INCF Secretariat, Stockholm, Sweden
We present our ongoing work to evaluate the usability and efficiency
of an implementation of the Connection Set Algebra
 for the neural simulation tool NEST
Present day simulation languages work well for simple network
structures like random or feed-forward networks. However, they either
lack the required expressiveness or the required computational
efficiency to specify the connectivity of complex hierarchical network
models now under investigation. This increases the risk of errors in
the model description or prevents the investigation of relevant model
sizes. Therefore, a description language in the problem domain of the
neuroscientist is required which guides the researcher to correct
model specifications and avoids technical clutter.
The Connection Set Algebra (CSA) is a generic framework, which
provides elementary connection sets and operators for combining
them. It allows one to define network connectivity in a compact and
accessible format. A prototype implementation of the CSA exists in
form of a Python module (libcsa). NEST is a simulator for large
heterogeneous networks of point neurons or neurons with a small number
The concrete goals of this project are:
- Reduce the complexity of NEST's native connection routines through
the use of CSA.
- Base the topology module of NEST on CSA in order to obtain better
scaling, especially for very large networks on HPC facilities.
- Create a C++ implementation of CSA (libcsa) that can be used by
neural simulators to describe network connectivity.
The result of this study is a test implementation and a quantitative
assessment of the proposed technology.
Partially funded by "The Next-Generation
Integrated Simulation of Living Matter" project, part of the
Development and Use of the Next-Generation Supercomputer Project of
the Ministry of Education, Culture, Sports, Science and Technology
(MEXT) of Japan, the Helmholtz Alliance on Systems Biology, the
Research Council of Norway and the International Neuroinformatics
 Mikael Djurfeldt, under revision (2011)
 Marc-Oliver Gewaltig and Markus Diesmann (2007) NEST (Neural
Simulation Tool), Scholarpedia 2 (4), p. 1430.
The Poster is available as PDF.
 Djurfeldt (2011) CNS*2011, BMC Neuroscience,
 Gewaltig & Diesmann (2007)
 Plesser & Austvoll (2009) BMC Neuroscience,
 Brunel (2000) J. Comput. Neurosci,
 Raikov & De Schutter (2010) FENS Abstr., vol.5, 059.26.
 Eppler et al (2009) Front. Neuroinform.,
The example in the CSA box:
The balanced random network used for the benchmark:
We used CSA trunk revision 93 and NEST trunk revision 9325 for this